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2025-12-16 10:45:02
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timestamp[s]date 2020-04-29 09:23:05
2025-12-16 19:34:46
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2025-12-16 14:20:48
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[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18
|
## Describe the bug
I am trying to load the [`GEM/RiSAWOZ` dataset](https://huggingface.co/datasets/GEM/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.
As far as I can tell, the dataset loading script is correct and the problematic features [here](https://huggingface.co/datasets/GEM/RiSAWOZ/blob/main/RiSAWOZ.py#L237) also look fine to me.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dset = load_dataset("GEM/RiSAWOZ")
```
## Expected results
I can load the dataset without error.
## Actual results
<details><summary>Traceback</summary>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1083 example = self.info.features.encode_example(record)
-> 1084 writer.write(example, key)
1085 finally:
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size)
445
--> 446 self.write_examples_on_file()
447
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self)
403 batch_examples[col] = [row[0][col] for row in self.current_examples]
--> 404 self.write_batch(batch_examples=batch_examples)
405 self.current_examples = []
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 497 arrays.append(pa.array(typed_sequence))
498 inferred_features[col] = typed_sequence.get_inferred_type()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
204 # We only do it if trying_type is False - since this is what the user asks for.
--> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
206 return out
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1064 if isinstance(feature, list):
-> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))
1066 elif isinstance(feature, Sequence):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
919 else:
--> 920 return func(array, *args, **kwargs)
921
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
-> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
1088
TypeError: Couldn't cast array of type
struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string>
to
{'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)}
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_44306/2896005239.py in <module>
----> 1 dset = load_dataset("GEM/RiSAWOZ")
2 dset
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1692
1693 # Download and prepare data
-> 1694 builder_instance.download_and_prepare(
1695 download_config=download_config,
1696 download_mode=download_mode,
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
593 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
594 if not downloaded_from_gcs:
--> 595 self._download_and_prepare(
596 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
597 )
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
682 try:
683 # Prepare split will record examples associated to the split
--> 684 self._prepare_split(split_generator, **prepare_split_kwargs)
685 except OSError as e:
686 raise OSError(
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)
1084 writer.write(example, key)
1085 finally:
-> 1086 num_examples, num_bytes = writer.finalize()
1087
1088 split_generator.split_info.num_examples = num_examples
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in finalize(self, close_stream)
525 # Re-intializing to empty list for next batch
526 self.hkey_record = []
--> 527 self.write_examples_on_file()
528 if self.pa_writer is None:
529 if self.schema:
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_examples_on_file(self)
402 # Since current_examples contains (example, key) tuples
403 batch_examples[col] = [row[0][col] for row in self.current_examples]
--> 404 self.write_batch(batch_examples=batch_examples)
405 self.current_examples = []
406
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
495 col_try_type = try_features[col] if try_features is not None and col in try_features else None
496 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 497 arrays.append(pa.array(typed_sequence))
498 inferred_features[col] = typed_sequence.get_inferred_type()
499 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
203 # Also, when trying type "string", we don't want to convert integers or floats to "string".
204 # We only do it if trying_type is False - since this is what the user asks for.
--> 205 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
206 return out
207 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1063 # feature must be either [subfeature] or Sequence(subfeature)
1064 if isinstance(feature, list):
-> 1065 return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))
1066 elif isinstance(feature, Sequence):
1067 if feature.length > -1:
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
1058 }
1059 if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):
-> 1060 arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
1061 return pa.StructArray.from_arrays(arrays, names=list(feature))
1062 elif pa.types.is_list(array.type):
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
942 if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse("4.0.0"):
943 array = _sanitize(array)
--> 944 return func(array, *args, **kwargs)
945
946 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in wrapper(array, *args, **kwargs)
918 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
919 else:
--> 920 return func(array, *args, **kwargs)
921
922 return wrapper
~/miniconda3/envs/huggingface/lib/python3.8/site-packages/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1085 elif not isinstance(feature, (Sequence, dict, list, tuple)):
1086 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
-> 1087 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
1088
1089
TypeError: Couldn't cast array of type
struct<医院-3.0T MRI: string, 医院-CT: string, 医院-DSA: string, 医院-公交线路: string, 医院-区域: string, 医院-名称: string, 医院-地址: string, 医院-地铁可达: string, 医院-地铁线路: string, 医院-性质: string, 医院-挂号时间: string, 医院-电话: string, 医院-等级: string, 医院-类别: string, 医院-重点科室: string, 医院-门诊时间: string, 天气-城市: string, 天气-天气: string, 天气-日期: string, 天气-温度: string, 天气-紫外线强度: string, 天气-风力风向: string, 旅游景点-区域: string, 旅游景点-名称: string, 旅游景点-地址: string, 旅游景点-开放时间: string, 旅游景点-是否地铁直达: string, 旅游景点-景点类型: string, 旅游景点-最适合人群: string, 旅游景点-消费: string, 旅游景点-特点: string, 旅游景点-电话号码: string, 旅游景点-评分: string, 旅游景点-门票价格: string, 汽车-价格(万元): string, 汽车-倒车影像: string, 汽车-动力水平: string, 汽车-厂商: string, 汽车-发动机排量(L): string, 汽车-发动机马力(Ps): string, 汽车-名称: string, 汽车-定速巡航: string, 汽车-巡航系统: string, 汽车-座位数: string, 汽车-座椅加热: string, 汽车-座椅通风: string, 汽车-所属价格区间: string, 汽车-油耗水平: string, 汽车-环保标准: string, 汽车-级别: string, 汽车-综合油耗(L/100km): string, 汽车-能源类型: string, 汽车-车型: string, 汽车-车系: string, 汽车-车身尺寸(mm): string, 汽车-驱动方式: string, 汽车-驾驶辅助影像: string, 火车-出发地: string, 火车-出发时间: string, 火车-到达时间: string, 火车-坐席: string, 火车-日期: string, 火车-时长: string, 火车-目的地: string, 火车-票价: string, 火车-舱位档次: string, 火车-车型: string, 火车-车次信息: string, 电影-主演: string, 电影-主演名单: string, 电影-具体上映时间: string, 电影-制片国家/地区: string, 电影-导演: string, 电影-年代: string, 电影-片名: string, 电影-片长: string, 电影-类型: string, 电影-豆瓣评分: string, 电脑-CPU: string, 电脑-CPU型号: string, 电脑-产品类别: string, 电脑-价格: string, 电脑-价格区间: string, 电脑-内存容量: string, 电脑-分类: string, 电脑-品牌: string, 电脑-商品名称: string, 电脑-屏幕尺寸: string, 电脑-待机时长: string, 电脑-显卡型号: string, 电脑-显卡类别: string, 电脑-游戏性能: string, 电脑-特性: string, 电脑-硬盘容量: string, 电脑-系列: string, 电脑-系统: string, 电脑-色系: string, 电脑-裸机重量: string, 电视剧-主演: string, 电视剧-主演名单: string, 电视剧-制片国家/地区: string, 电视剧-单集片长: string, 电视剧-导演: string, 电视剧-年代: string, 电视剧-片名: string, 电视剧-类型: string, 电视剧-豆瓣评分: string, 电视剧-集数: string, 电视剧-首播时间: string, 辅导班-上课方式: string, 辅导班-上课时间: string, 辅导班-下课时间: string, 辅导班-价格: string, 辅导班-区域: string, 辅导班-年级: string, 辅导班-开始日期: string, 辅导班-教室地点: string, 辅导班-教师: string, 辅导班-教师网址: string, 辅导班-时段: string, 辅导班-校区: string, 辅导班-每周: string, 辅导班-班号: string, 辅导班-科目: string, 辅导班-结束日期: string, 辅导班-课时: string, 辅导班-课次: string, 辅导班-课程网址: string, 辅导班-难度: string, 通用-产品类别: string, 通用-价格区间: string, 通用-品牌: string, 通用-系列: string, 酒店-价位: string, 酒店-停车场: string, 酒店-区域: string, 酒店-名称: string, 酒店-地址: string, 酒店-房型: string, 酒店-房费: string, 酒店-星级: string, 酒店-电话号码: string, 酒店-评分: string, 酒店-酒店类型: string, 飞机-准点率: string, 飞机-出发地: string, 飞机-到达时间: string, 飞机-日期: string, 飞机-目的地: string, 飞机-票价: string, 飞机-航班信息: string, 飞机-舱位档次: string, 飞机-起飞时间: string, 餐厅-人均消费: string, 餐厅-价位: string, 餐厅-区域: string, 餐厅-名称: string, 餐厅-地址: string, 餐厅-推荐菜: string, 餐厅-是否地铁直达: string, 餐厅-电话号码: string, 餐厅-菜系: string, 餐厅-营业时间: string, 餐厅-评分: string>
to
{'旅游景点-名称': Value(dtype='string', id=None), '旅游景点-区域': Value(dtype='string', id=None), '旅游景点-景点类型': Value(dtype='string', id=None), '旅游景点-最适合人群': Value(dtype='string', id=None), '旅游景点-消费': Value(dtype='string', id=None), '旅游景点-是否地铁直达': Value(dtype='string', id=None), '旅游景点-门票价格': Value(dtype='string', id=None), '旅游景点-电话号码': Value(dtype='string', id=None), '旅游景点-地址': Value(dtype='string', id=None), '旅游景点-评分': Value(dtype='string', id=None), '旅游景点-开放时间': Value(dtype='string', id=None), '旅游景点-特点': Value(dtype='string', id=None), '餐厅-名称': Value(dtype='string', id=None), '餐厅-区域': Value(dtype='string', id=None), '餐厅-菜系': Value(dtype='string', id=None), '餐厅-价位': Value(dtype='string', id=None), '餐厅-是否地铁直达': Value(dtype='string', id=None), '餐厅-人均消费': Value(dtype='string', id=None), '餐厅-地址': Value(dtype='string', id=None), '餐厅-电话号码': Value(dtype='string', id=None), '餐厅-评分': Value(dtype='string', id=None), '餐厅-营业时间': Value(dtype='string', id=None), '餐厅-推荐菜': Value(dtype='string', id=None), '酒店-名称': Value(dtype='string', id=None), '酒店-区域': Value(dtype='string', id=None), '酒店-星级': Value(dtype='string', id=None), '酒店-价位': Value(dtype='string', id=None), '酒店-酒店类型': Value(dtype='string', id=None), '酒店-房型': Value(dtype='string', id=None), '酒店-停车场': Value(dtype='string', id=None), '酒店-房费': Value(dtype='string', id=None), '酒店-地址': Value(dtype='string', id=None), '酒店-电话号码': Value(dtype='string', id=None), '酒店-评分': Value(dtype='string', id=None), '电脑-品牌': Value(dtype='string', id=None), '电脑-产品类别': Value(dtype='string', id=None), '电脑-分类': Value(dtype='string', id=None), '电脑-内存容量': Value(dtype='string', id=None), '电脑-屏幕尺寸': Value(dtype='string', id=None), '电脑-CPU': Value(dtype='string', id=None), '电脑-价格区间': Value(dtype='string', id=None), '电脑-系列': Value(dtype='string', id=None), '电脑-商品名称': Value(dtype='string', id=None), '电脑-系统': Value(dtype='string', id=None), '电脑-游戏性能': Value(dtype='string', id=None), '电脑-CPU型号': Value(dtype='string', id=None), '电脑-裸机重量': Value(dtype='string', id=None), '电脑-显卡类别': Value(dtype='string', id=None), '电脑-显卡型号': Value(dtype='string', id=None), '电脑-特性': Value(dtype='string', id=None), '电脑-色系': Value(dtype='string', id=None), '电脑-待机时长': Value(dtype='string', id=None), '电脑-硬盘容量': Value(dtype='string', id=None), '电脑-价格': Value(dtype='string', id=None), '火车-出发地': Value(dtype='string', id=None), '火车-目的地': Value(dtype='string', id=None), '火车-日期': Value(dtype='string', id=None), '火车-车型': Value(dtype='string', id=None), '火车-坐席': Value(dtype='string', id=None), '火车-车次信息': Value(dtype='string', id=None), '火车-时长': Value(dtype='string', id=None), '火车-出发时间': Value(dtype='string', id=None), '火车-到达时间': Value(dtype='string', id=None), '火车-票价': Value(dtype='string', id=None), '飞机-出发地': Value(dtype='string', id=None), '飞机-目的地': Value(dtype='string', id=None), '飞机-日期': Value(dtype='string', id=None), '飞机-舱位档次': Value(dtype='string', id=None), '飞机-航班信息': Value(dtype='string', id=None), '飞机-起飞时间': Value(dtype='string', id=None), '飞机-到达时间': Value(dtype='string', id=None), '飞机-票价': Value(dtype='string', id=None), '飞机-准点率': Value(dtype='string', id=None), '天气-城市': Value(dtype='string', id=None), '天气-日期': Value(dtype='string', id=None), '天气-天气': Value(dtype='string', id=None), '天气-温度': Value(dtype='string', id=None), '天气-风力风向': Value(dtype='string', id=None), '天气-紫外线强度': Value(dtype='string', id=None), '电影-制片国家/地区': Value(dtype='string', id=None), '电影-类型': Value(dtype='string', id=None), '电影-年代': Value(dtype='string', id=None), '电影-主演': Value(dtype='string', id=None), '电影-导演': Value(dtype='string', id=None), '电影-片名': Value(dtype='string', id=None), '电影-主演名单': Value(dtype='string', id=None), '电影-具体上映时间': Value(dtype='string', id=None), '电影-片长': Value(dtype='string', id=None), '电影-豆瓣评分': Value(dtype='string', id=None), '电视剧-制片国家/地区': Value(dtype='string', id=None), '电视剧-类型': Value(dtype='string', id=None), '电视剧-年代': Value(dtype='string', id=None), '电视剧-主演': Value(dtype='string', id=None), '电视剧-导演': Value(dtype='string', id=None), '电视剧-片名': Value(dtype='string', id=None), '电视剧-主演名单': Value(dtype='string', id=None), '电视剧-首播时间': Value(dtype='string', id=None), '电视剧-集数': Value(dtype='string', id=None), '电视剧-单集片长': Value(dtype='string', id=None), '电视剧-豆瓣评分': Value(dtype='string', id=None), '辅导班-班号': Value(dtype='string', id=None), '辅导班-难度': Value(dtype='string', id=None), '辅导班-科目': Value(dtype='string', id=None), '辅导班-年级': Value(dtype='string', id=None), '辅导班-区域': Value(dtype='string', id=None), '辅导班-校区': Value(dtype='string', id=None), '辅导班-上课方式': Value(dtype='string', id=None), '辅导班-开始日期': Value(dtype='string', id=None), '辅导班-结束日期': Value(dtype='string', id=None), '辅导班-每周': Value(dtype='string', id=None), '辅导班-上课时间': Value(dtype='string', id=None), '辅导班-下课时间': Value(dtype='string', id=None), '辅导班-时段': Value(dtype='string', id=None), '辅导班-课次': Value(dtype='string', id=None), '辅导班-课时': Value(dtype='string', id=None), '辅导班-教室地点': Value(dtype='string', id=None), '辅导班-教师': Value(dtype='string', id=None), '辅导班-价格': Value(dtype='string', id=None), '辅导班-课程网址': Value(dtype='string', id=None), '辅导班-教师网址': Value(dtype='string', id=None), '汽车-名称': Value(dtype='string', id=None), '汽车-车型': Value(dtype='string', id=None), '汽车-级别': Value(dtype='string', id=None), '汽车-座位数': Value(dtype='string', id=None), '汽车-车身尺寸(mm)': Value(dtype='string', id=None), '汽车-厂商': Value(dtype='string', id=None), '汽车-能源类型': Value(dtype='string', id=None), '汽车-发动机排量(L)': Value(dtype='string', id=None), '汽车-发动机马力(Ps)': Value(dtype='string', id=None), '汽车-驱动方式': Value(dtype='string', id=None), '汽车-综合油耗(L/100km)': Value(dtype='string', id=None), '汽车-环保标准': Value(dtype='string', id=None), '汽车-驾驶辅助影像': Value(dtype='string', id=None), '汽车-巡航系统': Value(dtype='string', id=None), '汽车-价格(万元)': Value(dtype='string', id=None), '汽车-车系': Value(dtype='string', id=None), '汽车-动力水平': Value(dtype='string', id=None), '汽车-油耗水平': Value(dtype='string', id=None), '汽车-倒车影像': Value(dtype='string', id=None), '汽车-定速巡航': Value(dtype='string', id=None), '汽车-座椅加热': Value(dtype='string', id=None), '汽车-座椅通风': Value(dtype='string', id=None), '汽车-所属价格区间': Value(dtype='string', id=None), '医院-名称': Value(dtype='string', id=None), '医院-等级': Value(dtype='string', id=None), '医院-类别': Value(dtype='string', id=None), '医院-性质': Value(dtype='string', id=None), '医院-区域': Value(dtype='string', id=None), '医院-地址': Value(dtype='string', id=None), '医院-电话': Value(dtype='string', id=None), '医院-挂号时间': Value(dtype='string', id=None), '医院-门诊时间': Value(dtype='string', id=None), '医院-公交线路': Value(dtype='string', id=None), '医院-地铁可达': Value(dtype='string', id=None), '医院-地铁线路': Value(dtype='string', id=None), '医院-重点科室': Value(dtype='string', id=None), '医院-CT': Value(dtype='string', id=None), '医院-3.0T MRI': Value(dtype='string', id=None), '医院-DSA': Value(dtype='string', id=None)}
```
</details>
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyArrow version: 3.0.0
|
CLOSED
| 2022-01-26T21:38:02
| 2022-02-09T16:15:53
| 2022-02-09T16:15:53
|
https://github.com/huggingface/datasets/issues/3637
|
lewtun
| 3
|
[
"bug"
] |
3,634
|
Dataset.shuffle(seed=None) gives fixed row permutation
|
## Describe the bug
Repeated attempts to `shuffle` a dataset without specifying a seed give the same results.
## Steps to reproduce the bug
```python
import datasets
# Some toy example
data = datasets.Dataset.from_dict(
{"feature": [1, 2, 3, 4, 5], "label": ["a", "b", "c", "d", "e"]}
)
# Doesn't work as expected
print("Shuffle dataset")
for _ in range(3):
print(data.shuffle(seed=None)[:])
# This seems to work with pandas
print("\nShuffle via pandas")
for _ in range(3):
df = data.to_pandas().sample(frac=1.0)
print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])
```
## Expected results
I assumed that the default setting would initialize a new/random state of a `np.random.BitGenerator` (see [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)).
Wouldn't that reshuffle the rows each time I call `data.shuffle()`?
## Actual results
```bash
Shuffle dataset
{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}
{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}
{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}
Shuffle via pandas
{'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']}
{'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']}
{'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.0
- Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-26T15:13:08
| 2022-01-27T18:16:07
| 2022-01-27T18:16:07
|
https://github.com/huggingface/datasets/issues/3634
|
elisno
| 2
|
[
"bug"
] |
3,632
|
Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)
|
## Describe the bug
The dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable.
Check out the dataset [homepage](http://data.statmt.org/cc-100/) which isn't accessible.
Also the URLs for dataset file per language isn't accessible: http://data.statmt.org/cc-100/<language code here>.txt.xz (language codes: am, sr, ka, etc.)
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("cc100", "ka")
```
It throws 503 error.
## Expected results
It should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.
## Environment info
Run from google colab. Just installed the library using pip:
```!pip install -U datasets```
|
CLOSED
| 2022-01-26T13:35:37
| 2022-02-10T06:58:11
| 2022-02-10T06:58:11
|
https://github.com/huggingface/datasets/issues/3632
|
AnzorGozalishvili
| 2
|
[
"bug"
] |
3,631
|
Labels conflict when loading a local CSV file.
|
## Describe the bug
I am trying to load a local CSV file with a separate file containing label names. It is successfully loaded for the first time, but when I try to load it again, there is a conflict between provided labels and the cached dataset info. Disabling caching globally and/or using `download_mode="force_redownload"` did not help.
## Steps to reproduce the bug
```python
load_dataset('csv', data_files='data/my_data.csv',
features=Features(text=Value(dtype='string'),
label=ClassLabel(names_file='data/my_data_labels.txt')))
```
`my_data.csv` file has the following structure:
```
text,label
"example1",0
"example2",1
...
```
and the `my_data_labels.txt` looks like this:
```
label1
label2
...
```
## Expected results
Successfully loaded dataset.
## Actual results
```python
File "/usr/local/lib/python3.8/site-packages/datasets/load.py", line 1706, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 766, in as_dataset
datasets = utils.map_nested(
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 261, in map_nested
mapped = [
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 262, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/usr/local/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 797, in _build_single_dataset
ds = self._as_dataset(
File "/usr/local/lib/python3.8/site-packages/datasets/builder.py", line 872, in _as_dataset
return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File "/usr/local/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 638, in __init__
inferred_features = Features.from_arrow_schema(arrow_table.schema)
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1242, in from_arrow_schema
return Features.from_dict(metadata["info"]["features"])
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1271, in from_dict
obj = generate_from_dict(dic)
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1076, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 1083, in generate_from_dict
return class_type(**{k: v for k, v in obj.items() if k in field_names})
File "<string>", line 7, in __init__
File "/usr/local/lib/python3.8/site-packages/datasets/features/features.py", line 776, in __post_init__
raise ValueError("Please provide either names or names_file but not both.")
ValueError: Please provide either names or names_file but not both.
```
## Environment info
- `datasets` version: 1.18.0
- Python version: 3.8.2
|
CLOSED
| 2022-01-26T10:00:33
| 2022-02-11T23:02:31
| 2022-02-11T23:02:31
|
https://github.com/huggingface/datasets/issues/3631
|
pichljan
| 1
|
[
"bug"
] |
3,630
|
DuplicatedKeysError of NewsQA dataset
|
After processing the dataset following official [NewsQA](https://github.com/Maluuba/newsqa), I used datasets to load it:
```
a = load_dataset('newsqa', data_dir='news')
```
and the following error occurred:
```
Using custom data configuration default-data_dir=news
Downloading and preparing dataset newsqa/default to /root/.cache/huggingface/datasets/newsqa/default-data_dir=news/1.0.0/b0b23e22d94a3d352ad9d75aff2b71375264a122fae301463079ee8595e05ab9...
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1084, in _prepare_split
writer.write(example, key)
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 442, in write
self.check_duplicate_keys()
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 1694, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 595, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 684, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.8/dist-packages/datasets/builder.py", line 1086, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 524, in finalize
self.check_duplicate_keys()
File "/usr/local/lib/python3.8/dist-packages/datasets/arrow_writer.py", line 453, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: ./cnn/stories/6a0f9c8a5d0c6e8949b37924163c92923fe5770d.story
Keys should be unique and deterministic in nature
```
|
CLOSED
| 2022-01-26T03:05:49
| 2022-02-14T08:37:19
| 2022-02-14T08:37:19
|
https://github.com/huggingface/datasets/issues/3630
|
StevenTang1998
| 1
|
[
"dataset bug"
] |
3,628
|
Dataset Card Creator drops information for "Additional Information" Section
|
First of all, the card creator is a great addition and really helpful for streamlining dataset cards!
## Describe the bug
I encountered an inconvenient bug when entering "Additional Information" in the react app, which drops already entered text when switching to a previous section, and then back again to "Additional Information". I was able to reproduce the issue in both Firefox and Chrome, so I suspect a problem with the React logic that doesn't expect users to switch back in the final section.
Edit: I'm also not sure whether this is the right place to open the bug report on, since it's not clear to me which particular project it belongs to, or where I could find associated source code.
## Steps to reproduce the bug
1. Navigate to the Section "Additional Information" in the [dataset card creator](https://huggingface.co/datasets/card-creator/)
2. Enter text in an arbitrary field, e.g., "Dataset Curators".
3. Switch back to a previous section, like "Dataset Creation".
4. When switching back again to "Additional Information", the text has been deleted.
Notably, this behavior can be reproduced again and again, it's not just problematic for the first "switch-back" from Additional Information.
## Expected results
For step 4, the previously entered information should still be present in the boxes, similar to the behavior to all other sections (switching back there works as expected)
## Actual results
The text boxes are empty again, and previously entered text got deleted.
## Environment info
- `datasets` version: N/A
- Platform: Firefox 96.0 / Chrome 97.0
- Python version: N/A
- PyArrow version: N/A
|
OPEN
| 2022-01-25T14:06:17
| 2022-01-25T14:09:01
| null |
https://github.com/huggingface/datasets/issues/3628
|
dennlinger
| 0
|
[
"bug"
] |
3,626
|
The Pile cannot connect to host
|
## Describe the bug
The Pile had issues with their previous host server and have mirrored its content to another server.
The new URL server should be updated.
|
CLOSED
| 2022-01-25T07:43:33
| 2022-02-14T08:40:58
| 2022-02-14T08:40:58
|
https://github.com/huggingface/datasets/issues/3626
|
albertvillanova
| 0
|
[
"bug"
] |
3,625
|
Add a metadata field for when source data was produced
|
**Is your feature request related to a problem? Please describe.**
The current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly.
**Describe the solution you'd like**
There are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`.
**Describe alternatives you've considered**
This information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets.
**Additional context**
I believe this feature is relevant for a number of reasons:
- Increasingly, there is an interest in using historical data for training language models (for example, https://huggingface.co/dbmdz/bert-base-historic-dutch-cased), and datasets to support this task (for example, https://huggingface.co/datasets/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant.
- More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important.
- time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here.
**open questions**
- I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss.
- what level of granularity would make sense for this? e.g. assigning a decade, century or year?
- how to encode this information? What formatting makes sense
- what specific time to encode; a data range? (mean, modal, min, max value?)
This is a slightly amorphous feature request - I would be happy to discuss further/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the 🤗 hubs ecosystem.
|
OPEN
| 2022-01-24T18:52:39
| 2022-06-28T13:54:49
| null |
https://github.com/huggingface/datasets/issues/3625
|
davanstrien
| 5
|
[
"enhancement"
] |
3,622
|
Extend support for streaming datasets that use os.path.relpath
|
Extend support for streaming datasets that use `os.path.relpath`.
This feature will also be useful to yield the relative path of audio or image files.
|
CLOSED
| 2022-01-24T15:58:23
| 2022-02-04T14:03:54
| 2022-02-04T14:03:54
|
https://github.com/huggingface/datasets/issues/3622
|
albertvillanova
| 0
|
[
"enhancement"
] |
3,621
|
Consider adding `ipywidgets` as a dependency.
|
When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error.
```
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```
It's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?
|
CLOSED
| 2022-01-24T14:27:11
| 2022-02-24T09:04:36
| 2022-02-24T09:04:36
|
https://github.com/huggingface/datasets/issues/3621
|
koaning
| 4
|
[
"bug"
] |
3,618
|
TIMIT Dataset not working with GPU
|
## Describe the bug
I am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the "audio" column from the dataset when working with a GPU.
I am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU).
I don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance.
This issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error.
## Steps to reproduce the bug
```python
from datasets import load_dataset
timit_train = load_dataset('timit_asr', split='train')
print(timit_train['audio'])
```
## Expected results
Expected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.
## Actual results
Traceback
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-ceeac555e921> in <module>
----> 1 timit_train['audio']
/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key)
1917 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
1918 return self._getitem(
-> 1919 key,
1920 )
1921
/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)
1902 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
1903 formatted_output = format_table(
-> 1904 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
1905 )
1906 return formatted_output
/opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)
529 python_formatter = PythonFormatter(features=None)
530 if format_columns is None:
--> 531 return formatter(pa_table, query_type=query_type)
532 elif query_type == "column":
533 if key in format_columns:
/opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type)
280 return self.format_row(pa_table)
281 elif query_type == "column":
--> 282 return self.format_column(pa_table)
283 elif query_type == "batch":
284 return self.format_batch(pa_table)
/opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_column(self, pa_table)
315 column = self.python_arrow_extractor().extract_column(pa_table)
316 if self.decoded:
--> 317 column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
318 return column
319
/opt/conda/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_column(self, column, column_name)
221
222 def decode_column(self, column: list, column_name: str) -> list:
--> 223 return self.features.decode_column(column, column_name) if self.features else column
224
225 def decode_batch(self, batch: dict) -> dict:
/opt/conda/lib/python3.6/site-packages/datasets/features/features.py in decode_column(self, column, column_name)
1337 return (
1338 [self[column_name].decode_example(value) if value is not None else None for value in column]
-> 1339 if self._column_requires_decoding[column_name]
1340 else column
1341 )
/opt/conda/lib/python3.6/site-packages/datasets/features/features.py in <listcomp>(.0)
1336 """
1337 return (
-> 1338 [self[column_name].decode_example(value) if value is not None else None for value in column]
1339 if self._column_requires_decoding[column_name]
1340 else column
/opt/conda/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value)
85 dict
86 """
---> 87 path, file = (value["path"], BytesIO(value["bytes"])) if value["bytes"] is not None else (value["path"], None)
88 if path is None and file is None:
89 raise ValueError(f"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.")
TypeError: string indices must be integers
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.0
- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-24T03:26:03
| 2023-07-25T15:20:20
| 2023-07-25T15:20:20
|
https://github.com/huggingface/datasets/issues/3618
|
TheSeamau5
| 3
|
[
"bug"
] |
3,615
|
Dataset BnL Historical Newspapers does not work in streaming mode
|
## Describe the bug
When trying to load in streaming mode, it "hangs"...
## Steps to reproduce the bug
```python
ds = load_dataset("bnl_newspapers", split="train", streaming=True)
```
## Expected results
The code should be optimized, so that it works fast in streaming mode.
CC: @davanstrien
|
CLOSED
| 2022-01-22T14:12:59
| 2022-02-04T14:05:21
| 2022-02-04T14:05:21
|
https://github.com/huggingface/datasets/issues/3615
|
albertvillanova
| 3
|
[
"bug"
] |
3,613
|
Files not updating in dataset viewer
|
## Dataset viewer issue for '*name of the dataset*'
**Link:**
Some examples:
* https://huggingface.co/datasets/abidlabs/crowdsourced-speech4
* https://huggingface.co/datasets/abidlabs/test-audio-13
*short description of the issue*
It seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error:
Am I the one who added this dataset? Yes
|
CLOSED
| 2022-01-21T16:47:20
| 2022-01-22T08:13:13
| 2022-01-22T08:13:13
|
https://github.com/huggingface/datasets/issues/3613
|
abidlabs
| 2
|
[
"dataset-viewer"
] |
3,611
|
Indexing bug after dataset.select()
|
## Describe the bug
A clear and concise description of what the bug is.
Dataset indexing is not working as expected after `dataset.select(range(100))`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import datasets
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
task_name = "sst2"
raw_datasets = datasets.load_dataset("glue", task_name)
train_dataset = raw_datasets["train"]
print("before select: ",train_dataset[-2:])
# before select: {'sentence': ['a patient viewer ', 'this new jangle of noise , mayhem and stupidity must be a serious contender for the title . '], 'label': [1, 0], 'idx': [67347, 67348]}
train_dataset = train_dataset.select(range(100))
print("after select: ",train_dataset[-2:])
# after select: {'sentence': [], 'label': [], 'idx': []}
```
link to colab: https://colab.research.google.com/drive/1LngeRC9f0jE7eSQ4Kh1cIeb411lRXQD-?usp=sharing
## Expected results
A clear and concise description of the expected results.
showing 98, 99 index data
## Actual results
Specify the actual results or traceback.
empty
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
|
CLOSED
| 2022-01-21T12:09:30
| 2022-01-27T18:16:22
| 2022-01-27T18:16:22
|
https://github.com/huggingface/datasets/issues/3611
|
kamalkraj
| 1
|
[
"bug"
] |
3,610
|
Checksum error when trying to load amazon_review dataset
|
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
I am getting the issue when trying to load dataset using
```
dataset = load_dataset("amazon_polarity")
```
## Expected results
dataset loaded
## Actual results
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-3-b4758ba980ae> in <module>()
----> 1 dataset = load_dataset("amazon_polarity")
2 dataset.set_format(type='pandas')
3 content_series = dataset['train']['content']
4 label_series = dataset['train']['label']
5 df = pd.concat([content_series, label_series], axis=1)
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
38 if len(bad_urls) > 0:
39 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))
41 logger.info("All the checksums matched successfully" + for_verification_name)
42
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Google colab
- Python version: 3.7.12
|
CLOSED
| 2022-01-20T21:20:32
| 2022-01-21T13:22:31
| 2022-01-21T13:22:31
|
https://github.com/huggingface/datasets/issues/3610
| null | 1
|
[
"bug"
] |
3,608
|
Add support for continuous metrics (RMSE, MAE)
|
**Is your feature request related to a problem? Please describe.**
I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https://huggingface.co/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.
**Describe the solution you'd like**
I would like to be able to tag our models on the Hub with the following metrics:
- RMSE
- MAE
**Describe alternatives you've considered**
I don't know if there are any alternatives.
**Additional context**
Our preprint is available here: https://arxiv.org/abs/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https://www.kaggle.com/c/jigsaw-toxic-severity-rating/overview . I have our first model uploaded to the Hub at https://huggingface.co/ucberkeley-dlab/hate-measure-roberta-large
Thanks,
Chris
|
CLOSED
| 2022-01-20T13:35:36
| 2022-03-09T17:18:20
| 2022-03-09T17:18:20
|
https://github.com/huggingface/datasets/issues/3608
|
ck37
| 3
|
[
"enhancement",
"good first issue"
] |
3,606
|
audio column not saved correctly after resampling
|
## Describe the bug
After resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type.
## Steps to reproduce the bug
- load a subset of common voice dataset (48Khz)
- resample audio column to 16Khz
- save with save_to_disk()
- load with load_from_disk()
## Expected results
I expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)
{'accent': Value(dtype='string', id=None),
'age': Value(dtype='string', id=None),
'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),
'client_id': Value(dtype='string', id=None),
'down_votes': Value(dtype='int64', id=None),
'gender': Value(dtype='string', id=None),
'locale': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'segment': Value(dtype='string', id=None),
'sentence': Value(dtype='string', id=None),
'up_votes': Value(dtype='int64', id=None)}
## Actual results
Audio column does not have the right type
{'accent': Value(dtype='string', id=None),
'age': Value(dtype='string', id=None),
'audio': {'bytes': Value(dtype='binary', id=None),
'path': Value(dtype='string', id=None)},
'client_id': Value(dtype='string', id=None),
'down_votes': Value(dtype='int64', id=None),
'gender': Value(dtype='string', id=None),
'locale': Value(dtype='string', id=None),
'path': Value(dtype='string', id=None),
'segment': Value(dtype='string', id=None),
'sentence': Value(dtype='string', id=None),
'up_votes': Value(dtype='int64', id=None)}
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: linux
- Python version:
- PyArrow version:
|
CLOSED
| 2022-01-20T06:37:10
| 2022-01-23T01:41:01
| 2022-01-23T01:24:14
|
https://github.com/huggingface/datasets/issues/3606
|
laphang
| 3
|
[
"bug"
] |
3,604
|
Dataset Viewer not showing Previews for Private Datasets
|
## Dataset viewer issue for 'abidlabs/test-audio-13'
It seems that the dataset viewer does not show previews for `private` datasets, even for the user who's private dataset it is. See [1] for example. If I change the visibility to public, then it does show, but it would be useful to have the viewer even for private datasets.
**Link:**
[1] https://huggingface.co/datasets/abidlabs/test-audio-13
**Am I the one who added this dataset?**
Yes
|
CLOSED
| 2022-01-19T19:29:26
| 2022-09-26T08:04:43
| 2022-09-26T08:04:43
|
https://github.com/huggingface/datasets/issues/3604
|
abidlabs
| 2
|
[
"enhancement",
"dataset-viewer"
] |
3,599
|
The `add_column()` method does not work if used on dataset sliced with `select()`
|
Hello, I posted this as a question on the forums ([here](https://discuss.huggingface.co/t/add-column-does-not-work-if-used-on-dataset-sliced-with-select/13893)):
I have a dataset with 2000 entries
> dataset = Dataset.from_dict({'colA': list(range(2000))})
and from which I want to extract the first one thousand rows, create a new dataset with these and also add a new column to it:
> dataset2 = dataset.select(list(range(1000)))
> final_dataset = dataset2.add_column('colB', list(range(1000)))
This gives an error
>ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000
So it looks like even though it is a dataset with 1000 rows, it "remembers" the shape of the one it was sliced from.
## Actual results
```
ArrowInvalid Traceback (most recent call last)
<ipython-input-138-e806860f3ce3> in <module>
----> 1 final_dataset = dataset2.add_column('colB', list(range(1000)))
~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
468 }
469 # apply actual function
--> 470 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
471 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
472 # re-apply format to the output
~/.local/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
404 # Call actual function
405
--> 406 out = func(self, *args, **kwargs)
407
408 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint)
3343 column_table = InMemoryTable.from_pydict({name: column})
3344 # Concatenate tables horizontally
-> 3345 table = ConcatenationTable.from_tables([self._data, column_table], axis=1)
3346 # Update features
3347 info = self.info.copy()
~/.local/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis)
729 table_blocks = to_blocks(table)
730 blocks = _extend_blocks(blocks, table_blocks, axis=axis)
--> 731 return cls.from_blocks(blocks)
732
733 @property
~/.local/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks)
668 @classmethod
669 def from_blocks(cls, blocks: TableBlockContainer) -> "ConcatenationTable":
--> 670 blocks = cls._consolidate_blocks(blocks)
671 if isinstance(blocks, TableBlock):
672 table = blocks
~/.local/lib/python3.8/site-packages/datasets/table.py in _consolidate_blocks(cls, blocks)
664 return cls._merge_blocks(blocks, axis=0)
665 else:
--> 666 return cls._merge_blocks(blocks)
667
668 @classmethod
~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis)
650 merged_blocks += list(block_group)
651 else: # both
--> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks]
653 if all(len(row_block) == 1 for row_block in merged_blocks):
654 merged_blocks = cls._merge_blocks(
~/.local/lib/python3.8/site-packages/datasets/table.py in <listcomp>(.0)
650 merged_blocks += list(block_group)
651 else: # both
--> 652 merged_blocks = [cls._merge_blocks(row_block, axis=1) for row_block in blocks]
653 if all(len(row_block) == 1 for row_block in merged_blocks):
654 merged_blocks = cls._merge_blocks(
~/.local/lib/python3.8/site-packages/datasets/table.py in _merge_blocks(cls, blocks, axis)
647 for is_in_memory, block_group in groupby(blocks, key=lambda x: isinstance(x, InMemoryTable)):
648 if is_in_memory:
--> 649 block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]
650 merged_blocks += list(block_group)
651 else: # both
~/.local/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis)
626 else:
627 for name, col in zip(table.column_names, table.columns):
--> 628 pa_table = pa_table.append_column(name, col)
629 return pa_table
630 else:
~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column()
~/.local/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column()
~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/.local/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Added column's length must match table's length. Expected length 2000 but got length 1000
```
A solution provided by @mariosasko is to use `dataset2.flatten_indices()` after the `select()` and before attempting to add the new column:
> dataset = Dataset.from_dict({'colA': list(range(2000))})
> dataset2 = dataset.select(list(range(1000)))
> dataset2 = dataset2.flatten_indices()
> final_dataset = dataset2.add_column('colB', list(range(1000)))
which works.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.13.2 (note: also checked with version 1.17.0, still the same error)
- Platform: Ubuntu 20.04.3
- Python version: 3.8.10
- PyArrow version: 6.0.0
|
CLOSED
| 2022-01-19T13:36:50
| 2022-01-28T15:35:57
| 2022-01-28T15:35:57
|
https://github.com/huggingface/datasets/issues/3599
|
ThGouzias
| 1
|
[
"bug"
] |
3,598
|
Readme info not being parsed to show on Dataset card page
|
## Describe the bug
The info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.
## Steps to reproduce the bug
# Sample code to reproduce the bug
The README file is this one: https://huggingface.co/datasets/softcatala/Tilde-MODEL-Catalan/blob/main/README.md
## Expected results
README info should appear in the Dataset card page.
## Actual results
Nothing is shown. However, labels are parsed and shown successfully.
|
CLOSED
| 2022-01-19T13:32:29
| 2022-01-21T10:20:01
| 2022-01-21T10:20:01
|
https://github.com/huggingface/datasets/issues/3598
|
davidcanovas
| 4
|
[
"bug"
] |
3,597
|
ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
|
## Bug
The install of streaming dataset is giving following error.
## Steps to reproduce the bug
```python
! git clone https://github.com/huggingface/datasets.git
! cd datasets
! pip install -e ".[streaming]"
```
## Actual results
Cloning into 'datasets'...
remote: Enumerating objects: 50816, done.
remote: Counting objects: 100% (2356/2356), done.
remote: Compressing objects: 100% (1606/1606), done.
remote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460
Receiving objects: 100% (50816/50816), 72.47 MiB | 27.68 MiB/s, done.
Resolving deltas: 100% (22541/22541), done.
Checking out files: 100% (6722/6722), done.
ERROR: File "setup.py" or "setup.cfg" not found. Directory cannot be installed in editable mode: /content
|
CLOSED
| 2022-01-19T13:19:28
| 2022-08-05T12:35:51
| 2022-02-14T08:46:34
|
https://github.com/huggingface/datasets/issues/3597
|
amitkml
| 2
|
[
"bug"
] |
3,596
|
Loss of cast `Image` feature on certain dataset method
|
## Describe the bug
When an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.
I suspect this might be related to https://github.com/huggingface/datasets/pull/3556 but I don't believe that pull request fixes this issue.
## Steps to reproduce the bug
An example of casting a url to an image followed by using the `select` method:
```python
from datasets import Dataset
from datasets import features
url = "https://cf.ltkcdn.net/cats/images/std-lg/246866-1200x816-grey-white-kitten.webp"
data_dict = {"url": [url]*2}
dataset = Dataset.from_dict(data_dict)
dataset = dataset.cast_column('url',features.Image())
sample = dataset.select([1])
```
[example notebook](https://gist.github.com/davanstrien/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)
## Expected results
The cast value is maintained when further methods are applied to the dataset.
## Actual results
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-12-47f393bc2d0d> in <module>()
----> 1 sample = dataset.select([1])
4 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
487 }
488 # apply actual function
--> 489 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
490 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
491 # re-apply format to the output
/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
409 # Call actual function
410
--> 411 out = func(self, *args, **kwargs)
412
413 # Update fingerprint of in-place transforms + update in-place history of transforms
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
2772 )
2773 else:
-> 2774 return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
2775
2776 @transmit_format
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)
2688 split=self.split,
2689 indices_table=indices_table,
-> 2690 fingerprint=fingerprint,
2691 )
2692
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
664 if self.info.features.type != inferred_features.type:
665 raise ValueError(
--> 666 f"External features info don't match the dataset:\nGot\n{self.info.features}\nwith type\n{self.info.features.type}\n\nbut expected something like\n{inferred_features}\nwith type\n{inferred_features.type}"
667 )
668
ValueError: External features info don't match the dataset:
Got
{'url': Image(id=None)}
with type
struct<url: extension<arrow.py_extension_type<ImageExtensionType>>>
but expected something like
{'url': Value(dtype='string', id=None)}
with type
struct<url: string>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
|
CLOSED
| 2022-01-18T20:44:01
| 2022-01-21T18:07:28
| 2022-01-21T18:07:28
|
https://github.com/huggingface/datasets/issues/3596
|
davanstrien
| 7
|
[
"bug"
] |
3,587
|
No module named 'fsspec.archive'
|
## Describe the bug
Cannot import datasets after installation.
## Steps to reproduce the bug
```shell
$ python
Python 3.9.7 (default, Sep 16 2021, 13:09:58)
[GCC 7.5.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/__init__.py", line 34, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 61, in <module>
from .arrow_writer import ArrowWriter, OptimizedTypedSequence
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/arrow_writer.py", line 28, in <module>
from .features import (
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/__init__.py", line 2, in <module>
from .audio import Audio
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/features/audio.py", line 7, in <module>
from ..utils.streaming_download_manager import xopen
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py", line 18, in <module>
from ..filesystems import COMPRESSION_FILESYSTEMS
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/__init__.py", line 6, in <module>
from . import compression
File "/home/shuchen/miniconda3/envs/hf/lib/python3.9/site-packages/datasets/filesystems/compression.py", line 5, in <module>
from fsspec.archive import AbstractArchiveFileSystem
ModuleNotFoundError: No module named 'fsspec.archive'
```
|
CLOSED
| 2022-01-18T10:17:01
| 2022-08-11T09:57:54
| 2022-01-18T10:33:10
|
https://github.com/huggingface/datasets/issues/3587
|
shuuchen
| 0
|
[
"bug"
] |
3,586
|
Revisit `enable/disable_` toggle function prefix
|
As discussed in https://github.com/huggingface/transformers/pull/15167, we should revisit the `enable/disable_` toggle function prefix, potentially in favor of `set_enabled_`. Concretely, this translates to
- De-deprecating `disable_progress_bar()`
- Adding `enable_progress_bar()`
- On the caching side, adding `enable_caching` and `disable_caching`
Additional decisions have to be made with regards to the existing `set_enabled_X` functions; that is, whether to keep them as is or deprecate them in favor of the aforementioned functions.
cc @mariosasko @lhoestq
|
CLOSED
| 2022-01-18T04:09:55
| 2022-03-14T15:01:08
| 2022-03-14T15:01:08
|
https://github.com/huggingface/datasets/issues/3586
|
jaketae
| 0
|
[
"enhancement"
] |
3,585
|
Datasets streaming + map doesn't work for `Audio`
|
## Describe the bug
When using audio datasets in streaming mode, applying a `map(...)` before iterating leads to an error as the key `array` does not exist anymore.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("common_voice", "en", streaming=True, split="train")
def map_fn(batch):
print("audio keys", batch["audio"].keys())
batch["audio"] = batch["audio"]["array"][:100]
return batch
ds = ds.map(map_fn)
sample = next(iter(ds))
```
I think the audio is somehow decoded before `.map(...)` is actually called.
## Expected results
IMO, the above code snippet should work.
## Actual results
```bash
audio keys dict_keys(['path', 'bytes'])
Traceback (most recent call last):
File "./run_audio.py", line 15, in <module>
sample = next(iter(ds))
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/home/patrick/python_bin/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "./run_audio.py", line 9, in map_fn
batch["input"] = batch["audio"]["array"][:100]
KeyError: 'array'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.1.dev0
- Platform: Linux-5.3.0-64-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-17T12:55:42
| 2022-01-20T13:28:00
| 2022-01-20T13:28:00
|
https://github.com/huggingface/datasets/issues/3585
|
patrickvonplaten
| 1
|
[
"bug",
"duplicate"
] |
3,584
|
https://huggingface.co/datasets/huggingface/transformers-metadata
|
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
CLOSED
| 2022-01-17T00:18:14
| 2022-02-14T08:51:27
| 2022-02-14T08:51:27
|
https://github.com/huggingface/datasets/issues/3584
|
ecankirkic
| 0
|
[
"wontfix",
"dataset-viewer"
] |
3,583
|
Add The Medical Segmentation Decathlon Dataset
|
## Adding a Dataset
- **Name:** *The Medical Segmentation Decathlon Dataset*
- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects.
- **Paper:** [link to the dataset paper if available](https://arxiv.org/abs/2106.05735)
- **Data:** http://medicaldecathlon.com/
- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.
(cc @osanseviero @abidlabs )
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
OPEN
| 2022-01-16T21:42:25
| 2022-03-18T10:44:42
| null |
https://github.com/huggingface/datasets/issues/3583
|
omarespejel
| 5
|
[
"dataset request",
"vision"
] |
3,582
|
conll 2003 dataset source url is no longer valid
|
## Describe the bug
Loading `conll2003` dataset fails because it was removed (just yesterday 1/14/2022) from the location it is looking for.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("conll2003")
```
## Expected results
The dataset should load.
## Actual results
It is looking for the dataset at `https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt` but it was removed from there yesterday (see [commit](https://github.com/davidsbatista/NER-datasets/commit/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https://github.com/davidsbatista/NER-datasets/issues/8)).
- We should replace this with an alternate valid location.
- this is being referenced in the huggingface course chapter 7 [colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter7/section2_pt.ipynb), which is also broken.
```python
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-27c956bec93c> in <module>()
1 from datasets import load_dataset
2
----> 3 raw_datasets = load_dataset("conll2003")
11 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)
610 )
611 elif response is not None and response.status_code == 404:
--> 612 raise FileNotFoundError(f"Couldn't find file at {url}")
613 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
614 if head_error is not None:
FileNotFoundError: Couldn't find file at https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
|
CLOSED
| 2022-01-15T23:04:17
| 2022-07-20T13:06:40
| 2022-01-21T16:57:32
|
https://github.com/huggingface/datasets/issues/3582
|
rcanand
| 9
|
[
"bug",
"dataset bug"
] |
3,581
|
Unable to create a dataset from a parquet file in S3
|
## Describe the bug
Trying to create a dataset from a parquet file in S3.
## Steps to reproduce the bug
```python
import s3fs
from datasets import Dataset
s3 = s3fs.S3FileSystem(anon=False)
with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file:
dataset = Dataset.from_parquet(s3file)
```
## Expected results
A new Dataset object
## Actual results
```AttributeError: 'S3File' object has no attribute 'decode'```
```
AttributeError Traceback (most recent call last)
<command-2452877612515691> in <module>
5
6 with s3.open(PATH_LTR_TOY_CLEAN_DATASET, 'rb') as s3file:
----> 7 dataset = Dataset.from_parquet(s3file)
/databricks/python/lib/python3.8/site-packages/datasets/arrow_dataset.py in from_parquet(path_or_paths, split, features, cache_dir, keep_in_memory, columns, **kwargs)
907 from .io.parquet import ParquetDatasetReader
908
--> 909 return ParquetDatasetReader(
910 path_or_paths,
911 split=split,
/databricks/python/lib/python3.8/site-packages/datasets/io/parquet.py in __init__(self, path_or_paths, split, features, cache_dir, keep_in_memory, **kwargs)
28 path_or_paths = path_or_paths if isinstance(path_or_paths, dict) else {self.split: path_or_paths}
29 hash = _PACKAGED_DATASETS_MODULES["parquet"][1]
---> 30 self.builder = Parquet(
31 cache_dir=cache_dir,
32 data_files=path_or_paths,
/databricks/python/lib/python3.8/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, base_path, info, features, use_auth_token, namespace, data_files, data_dir, **config_kwargs)
246
247 if data_files is not None and not isinstance(data_files, DataFilesDict):
--> 248 data_files = DataFilesDict.from_local_or_remote(
249 sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token
250 )
/databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
576 for key, patterns_for_key in patterns.items():
577 out[key] = (
--> 578 DataFilesList.from_local_or_remote(
579 patterns_for_key,
580 base_path=base_path,
/databricks/python/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
544 ) -> "DataFilesList":
545 base_path = base_path if base_path is not None else str(Path().resolve())
--> 546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
548 return cls(data_files, origin_metadata)
/databricks/python/lib/python3.8/site-packages/datasets/data_files.py in resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
191 data_files = []
192 for pattern in patterns:
--> 193 if is_remote_url(pattern):
194 data_files.append(Url(pattern))
195 else:
/databricks/python/lib/python3.8/site-packages/datasets/utils/file_utils.py in is_remote_url(url_or_filename)
115
116 def is_remote_url(url_or_filename: str) -> bool:
--> 117 parsed = urlparse(url_or_filename)
118 return parsed.scheme in ("http", "https", "s3", "gs", "hdfs", "ftp")
119
/usr/lib/python3.8/urllib/parse.py in urlparse(url, scheme, allow_fragments)
370 Note that we don't break the components up in smaller bits
371 (e.g. netloc is a single string) and we don't expand % escapes."""
--> 372 url, scheme, _coerce_result = _coerce_args(url, scheme)
373 splitresult = urlsplit(url, scheme, allow_fragments)
374 scheme, netloc, url, query, fragment = splitresult
/usr/lib/python3.8/urllib/parse.py in _coerce_args(*args)
122 if str_input:
123 return args + (_noop,)
--> 124 return _decode_args(args) + (_encode_result,)
125
126 # Result objects are more helpful than simple tuples
/usr/lib/python3.8/urllib/parse.py in _decode_args(args, encoding, errors)
106 def _decode_args(args, encoding=_implicit_encoding,
107 errors=_implicit_errors):
--> 108 return tuple(x.decode(encoding, errors) if x else '' for x in args)
109
110 def _coerce_args(*args):
/usr/lib/python3.8/urllib/parse.py in <genexpr>(.0)
106 def _decode_args(args, encoding=_implicit_encoding,
107 errors=_implicit_errors):
--> 108 return tuple(x.decode(encoding, errors) if x else '' for x in args)
109
110 def _coerce_args(*args):
AttributeError: 'S3File' object has no attribute 'decode'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.17.0
- Platform: Ubuntu 20.04.3 LTS
- Python version: 3.8.10
- PyArrow version: 6.0.1
|
OPEN
| 2022-01-15T21:34:16
| 2022-02-14T08:52:57
| null |
https://github.com/huggingface/datasets/issues/3581
|
regCode
| 1
|
[
"bug",
"enhancement"
] |
3,580
|
Bug in wiki bio load
|
wiki_bio is failing to load because of a failing drive link . Can someone fix this ?
a
|
CLOSED
| 2022-01-15T10:04:33
| 2022-01-31T08:38:09
| 2022-01-31T08:38:09
|
https://github.com/huggingface/datasets/issues/3580
|
tuhinjubcse
| 4
|
[
"dataset bug"
] |
3,578
|
label information get lost after parquet serialization
|
## Describe the bug
In *dataset_info.json* file, information about the label get lost after the dataset serialization.
## Steps to reproduce the bug
```python
from datasets import load_dataset
# normal save
dataset = load_dataset('glue', 'sst2', split='train')
dataset.save_to_disk("normal_save")
# save after parquet serialization
dataset.to_parquet("glue-sst2-train.parquet")
dataset = load_dataset("parquet", data_files='glue-sst2-train.parquet')
dataset.save_to_disk("save_after_parquet")
```
## Expected results
I expected to keep label information in *dataset_info.json* file even after parquet serialization
## Actual results
In the normal serialization i got
```json
"label": {
"num_classes": 2,
"names": [
"negative",
"positive"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
```
And after parquet serialization i got
```json
"label": {
"dtype": "int64",
"id": null,
"_type": "Value"
},
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.0
- Platform: ubuntu 20.04
- Python version: 3.8.10
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-14T10:10:38
| 2023-07-25T15:44:53
| 2023-07-25T15:44:53
|
https://github.com/huggingface/datasets/issues/3578
|
Tudyx
| 2
|
[
"bug"
] |
3,577
|
Add The Mexican Emotional Speech Database (MESD)
|
## Adding a Dataset
- **Name:** *The Mexican Emotional Speech Database (MESD)*
- **Description:** *Contains 864 voice recordings with six different prosodies: anger, disgust, fear, happiness, neutral, and sadness. Furthermore, three voice categories are included: female adult, male adult, and child. *
- **Paper:** *[Paper](https://ieeexplore.ieee.org/abstract/document/9629934/authors#authors)*
- **Data:** *[link to the Github repository or current dataset location](https://data.mendeley.com/datasets/cy34mh68j9/3)*
- **Motivation:** *Would add Spanish speech data to the HF datasets :) *
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
OPEN
| 2022-01-13T23:49:36
| 2022-01-27T14:14:38
| null |
https://github.com/huggingface/datasets/issues/3577
|
omarespejel
| 0
|
[
"dataset request",
"speech"
] |
3,572
|
ConnectionError in IndicGLUE dataset
|
While I am trying to load IndicGLUE dataset (https://huggingface.co/datasets/indic_glue) it is giving me with the error:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/evaluations/wikiann-ner.tar.gz (error 403)
|
CLOSED
| 2022-01-12T17:59:36
| 2022-09-15T21:57:34
| 2022-09-15T21:57:34
|
https://github.com/huggingface/datasets/issues/3572
|
sahoodib
| 3
|
[
"dataset bug"
] |
3,568
|
Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError
|
I wanted to download the Nedical Dialog Dataset from huggingface, using this github link:
https://github.com/huggingface/datasets/tree/master/datasets/medical_dialog
After downloading the raw datasets from google drive, i unpacked everything and put it in the same folder as the medical_dialog.py which is:
```
import copy
import os
import re
import datasets
_CITATION = """\
@article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint arXiv:2004.03329},
year={2020}
}
"""
_DESCRIPTION = """\
The MedDialog dataset (English) contains conversations (in English) between doctors and patients.\
It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \
The raw dialogues are from healthcaremagic.com and icliniq.com.\
All copyrights of the data belong to healthcaremagic.com and icliniq.com.
"""
_HOMEPAGE = "https://github.com/UCSD-AI4H/Medical-Dialogue-System"
_LICENSE = ""
class MedicalDialog(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="en", description="The dataset of medical dialogs in English.", version=VERSION),
datasets.BuilderConfig(name="zh", description="The dataset of medical dialogs in Chinese.", version=VERSION),
]
@property
def manual_download_instructions(self):
return """\
\n For English:\nYou need to go to https://drive.google.com/drive/folders/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\
and manually download the dataset from Google Drive. Once it is completed,
a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder(
or whichever folder your browser chooses to save files to). Unzip the folder to obtain
a folder named "Medical-Dialogue-Dataset-English" several text files.
Now, you can specify the path to this folder for the data_dir argument in the
datasets.load_dataset(...) option.
The <path/to/folder> can e.g. be "/Downloads/Medical-Dialogue-Dataset-English".
The data can then be loaded using the below command:\
datasets.load_dataset("medical_dialog", name="en", data_dir="/Downloads/Medical-Dialogue-Dataset-English")`.
\n For Chinese:\nFollow the above process. Change the 'name' to 'zh'.The download link is https://drive.google.com/drive/folders/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2
**NOTE**
- A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times.
- After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path.
"""
datasets.load_dataset("medical_dialog", name="en", data_dir="Medical-Dialogue-Dataset-English")
def _info(self):
if self.config.name == "zh":
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["病人", "医生"]),
"utterance": datasets.Value("string"),
}
),
}
)
if self.config.name == "en":
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"dialogue_id": datasets.Value("int32"),
"dialogue_url": datasets.Value("string"),
"dialogue_turns": datasets.Sequence(
{
"speaker": datasets.ClassLabel(names=["Patient", "Doctor"]),
"utterance": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=features,
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(path_to_manual_file):
raise FileNotFoundError(
f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})"
)
filepaths = [
os.path.join(path_to_manual_file, txt_file_name)
for txt_file_name in sorted(os.listdir(path_to_manual_file))
if txt_file_name.endswith("txt")
]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": filepaths})]
def _generate_examples(self, filepaths):
"""Yields examples. Iterates over each file and give the creates the corresponding features.
NOTE:
- The code makes some assumption on the structure of the raw .txt file.
- There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added.
"""
data_lang = self.config.name
id_ = -1
for filepath in filepaths:
with open(filepath, encoding="utf-8") as f_in:
# Parameters to just "sectionize" the raw data
last_part = ""
last_dialog = {}
last_list = []
last_user = ""
check_list = []
# These flags are present to have a single function address both chinese and english data
# English data is a little hahazard (i.e. the sentences spans multiple different lines),
# Chinese is compact with one line for doctor and patient.
conv_flag = False
des_flag = False
while True:
line = f_in.readline()
if not line:
break
# Extracting the dialog id
if line[:2] == "id": # Hardcode alert!
# Handling ID references that may come in the description
# These were observed in the Chinese dataset and were not
# followed by numbers
try:
dialogue_id = int(re.findall(r"\d+", line)[0])
except IndexError:
continue
# Extracting the url
if line[:4] == "http": # Hardcode alert!
dialogue_url = line.rstrip()
# Extracting the patient info from description.
if line[:11] == "Description": # Hardcode alert!
last_part = "description"
last_dialog = {}
last_list = []
last_user = ""
last_conv = {"speaker": "", "utterance": ""}
while True:
line = f_in.readline()
if (not line) or (line in ["\n", "\n\r"]):
break
else:
if data_lang == "zh": # Condition in chinese
if line[:5] == "病情描述：": # Hardcode alert!
last_user = "病人"
sen = f_in.readline().rstrip()
des_flag = True
if data_lang == "en":
last_user = "Patient"
sen = line.rstrip()
des_flag = True
if des_flag:
if sen == "":
continue
if sen in check_list:
last_conv["speaker"] = ""
last_conv["utterance"] = ""
else:
last_conv["speaker"] = last_user
last_conv["utterance"] = sen
check_list.append(sen)
des_flag = False
break
# Extracting the conversation info from dialogue.
elif line[:8] == "Dialogue": # Hardcode alert!
if last_part == "description" and len(last_conv["utterance"]) > 0:
last_part = "dialogue"
if data_lang == "zh":
last_user = "病人"
if data_lang == "en":
last_user = "Patient"
while True:
line = f_in.readline()
if (not line) or (line in ["\n", "\n\r"]):
conv_flag = False
last_user = ""
last_list.append(copy.deepcopy(last_conv))
# To ensure close of conversation, only even number of sentences
# are extracted
last_turn = len(last_list)
if int(last_turn / 2) > 0:
temp = int(last_turn / 2)
id_ += 1
last_dialog["file_name"] = filepath
last_dialog["dialogue_id"] = dialogue_id
last_dialog["dialogue_url"] = dialogue_url
last_dialog["dialogue_turns"] = last_list[: temp * 2]
yield id_, last_dialog
break
if data_lang == "zh":
if line[:3] == "病人：" or line[:3] == "医生：": # Hardcode alert!
user = line[:2] # Hardcode alert!
line = f_in.readline()
conv_flag = True
# The elif block is to ensure that multi-line sentences are captured.
# This has been observed only in english.
if data_lang == "en":
if line.strip() == "Patient:" or line.strip() == "Doctor:": # Hardcode alert!
user = line.replace(":", "").rstrip()
line = f_in.readline()
conv_flag = True
elif line[:2] != "id": # Hardcode alert!
conv_flag = True
# Continues till the next ID is parsed
if conv_flag:
sen = line.rstrip()
if sen == "":
continue
if user == last_user:
last_conv["utterance"] = last_conv["utterance"] + sen
else:
last_user = user
last_list.append(copy.deepcopy(last_conv))
last_conv["utterance"] = sen
last_conv["speaker"] = user
```
running this code gives me the error:
```
File "C:\Users\Fabia\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\utils\info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=292801173, num_examples=229674, dataset_name='medical_dialog')}]
```
|
CLOSED
| 2022-01-12T14:03:44
| 2022-02-14T09:32:34
| 2022-02-14T09:32:34
|
https://github.com/huggingface/datasets/issues/3568
|
fabianslife
| 1
|
[
"dataset bug"
] |
3,563
|
Dataset.from_pandas preserves useless index
|
## Describe the bug
Let's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this:
```
import pandas as pd
from datasets import Dataset
df = pd.read_csv('some_dataset.csv')
# Some DataFrame preprocessing code...
dataset = Dataset.from_pandas(df)
```
If your preprocessing code contain indexing operations like this:
```
df = df[df.col1 == some_value]
```
then your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8,
9,
...
83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987,
83988],
dtype='int64', length=16590)```
In this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'.
You can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```.
If you approve that this isn't desirable behavior, I can make a PR fixing that.
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-11T12:07:07
| 2022-01-12T16:11:27
| 2022-01-12T16:11:27
|
https://github.com/huggingface/datasets/issues/3563
|
Sorrow321
| 1
|
[
"bug"
] |
3,561
|
Cannot load ‘bookcorpusopen’
|
## Describe the bug
Cannot load 'bookcorpusopen'
## Steps to reproduce the bug
```python
dataset = load_dataset('bookcorpusopen')
```
or
```python
dataset = load_dataset('bookcorpusopen',script_version='master')
```
## Actual results
ConnectionError: Couldn't reach https://the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64
- Python version: 3.6.13
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-10T20:17:18
| 2022-02-14T09:19:27
| 2022-02-14T09:18:47
|
https://github.com/huggingface/datasets/issues/3561
|
HUIYINXUE
| 3
|
[
"bug",
"dataset bug"
] |
3,558
|
Integrate Milvus (pymilvus) library
|
Milvus is a popular open-source vector database. We should add a new vector index to support this project.
|
OPEN
| 2022-01-10T15:20:29
| 2022-03-05T12:28:36
| null |
https://github.com/huggingface/datasets/issues/3558
|
mariosasko
| 5
|
[
"enhancement"
] |
3,555
|
DuplicatedKeysError when loading tweet_qa dataset
|
When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs:
`DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 2a167f9e016ba338e1813fed275a6a1e
Keys should be unique and deterministic in nature
`
Might be related to issues #2433 and #2333
- `datasets` version: 1.17.0
- Python version: 3.8.5
|
CLOSED
| 2022-01-10T10:53:11
| 2022-01-12T15:17:33
| 2022-01-12T15:13:56
|
https://github.com/huggingface/datasets/issues/3555
|
LeonieWeissweiler
| 1
|
[
"bug"
] |
3,554
|
ImportError: cannot import name 'is_valid_waiter_error'
|
Based on [SO post](https://stackoverflow.com/q/70606147/17840900).
I'm following along to this [Notebook][1], cell "**Loading the dataset**".
Kernel: `conda_pytorch_p36`.
I run:
```
! pip install datasets transformers optimum[intel]
```
Output:
```
Requirement already satisfied: datasets in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (1.17.0)
Requirement already satisfied: transformers in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (4.15.0)
Requirement already satisfied: optimum[intel] in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (0.1.3)
Requirement already satisfied: numpy>=1.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.19.5)
Requirement already satisfied: dill in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.3.4)
Requirement already satisfied: tqdm>=4.62.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.62.3)
Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.2.1)
Requirement already satisfied: packaging in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (21.3)
Requirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (6.0.1)
Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (1.1.5)
Requirement already satisfied: xxhash in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.0.2)
Requirement already satisfied: aiohttp in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (3.8.1)
Requirement already satisfied: fsspec[http]>=2021.05.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2021.11.1)
Requirement already satisfied: dataclasses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.8)
Requirement already satisfied: multiprocess in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (0.70.12.2)
Requirement already satisfied: importlib-metadata in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (4.5.0)
Requirement already satisfied: requests>=2.19.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from datasets) (2.25.1)
Requirement already satisfied: pyyaml>=5.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (5.4.1)
Requirement already satisfied: regex!=2019.12.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (2021.4.4)
Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.10.3)
Requirement already satisfied: filelock in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (3.0.12)
Requirement already satisfied: sacremoses in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from transformers) (0.0.46)
Requirement already satisfied: torch>=1.9 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.10.1)
Requirement already satisfied: sympy in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.8)
Requirement already satisfied: coloredlogs in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (15.0.1)
Requirement already satisfied: pycocotools in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (2.0.3)
Requirement already satisfied: neural-compressor>=1.7 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from optimum[intel]) (1.9)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)
Requirement already satisfied: sigopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)
Requirement already satisfied: opencv-python in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48)
Requirement already satisfied: cryptography in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7)
Requirement already satisfied: py-cpuinfo in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0)
Requirement already satisfied: gevent in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2)
Requirement already satisfied: schema in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5)
Requirement already satisfied: psutil in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0)
Requirement already satisfied: gevent-websocket in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1)
Requirement already satisfied: hyperopt in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7)
Requirement already satisfied: Flask in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1)
Requirement already satisfied: prettytable in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0)
Requirement already satisfied: Flask-SocketIO in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1)
Requirement already satisfied: scikit-learn in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2)
Requirement already satisfied: Pillow in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0)
Requirement already satisfied: Flask-Cors in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from packaging->datasets) (2.4.7)
Requirement already satisfied: chardet<5,>=3.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (4.0.0)
Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2021.5.30)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (1.26.5)
Requirement already satisfied: idna<3,>=2.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from requests>=2.19.0->datasets) (2.10)
Requirement already satisfied: yarl<2.0,>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.6.3)
Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (2.0.9)
Requirement already satisfied: attrs>=17.3.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (21.2.0)
Requirement already satisfied: asynctest==0.13.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (0.13.0)
Requirement already satisfied: idna-ssl>=1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.1.0)
Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (4.0.1)
Requirement already satisfied: aiosignal>=1.1.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0)
Requirement already satisfied: frozenlist>=1.1.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (1.2.0)
Requirement already satisfied: multidict<7.0,>=4.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from aiohttp->datasets) (5.1.0)
Requirement already satisfied: humanfriendly>=9.1 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from coloredlogs->optimum[intel]) (10.0)
Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from importlib-metadata->datasets) (3.4.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pandas->datasets) (2021.1)
Requirement already satisfied: matplotlib>=2.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (3.3.4)
Requirement already satisfied: cython>=0.27.3 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (0.29.23)
Requirement already satisfied: setuptools>=18.0 in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125)
Requirement already satisfied: joblib in /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages (from sacremoses->transformers) (1.0.1)
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```
---
**Cell:**
```python
from datasets import load_dataset, load_metric
```
OR
```python
import datasets
```
**Traceback:**
```
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-7-34fb7ba3338d> in <module>
----> 1 from datasets import load_dataset, load_metric
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/__init__.py in <module>
32 )
33
---> 34 from .arrow_dataset import Dataset, concatenate_datasets
35 from .arrow_reader import ArrowReader, ReadInstruction
36 from .arrow_writer import ArrowWriter
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_dataset.py in <module>
59 from . import config, utils
60 from .arrow_reader import ArrowReader
---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper
63 from .filesystems import extract_path_from_uri, is_remote_filesystem
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/arrow_writer.py in <module>
26
27 from . import config, utils
---> 28 from .features import (
29 Features,
30 ImageExtensionType,
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/__init__.py in <module>
1 # flake8: noqa
----> 2 from .audio import Audio
3 from .features import *
4 from .features import (
5 _ArrayXD,
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/features/audio.py in <module>
5 import pyarrow as pa
6
----> 7 from ..utils.streaming_download_manager import xopen
8
9
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/utils/streaming_download_manager.py in <module>
16
17 from .. import config
---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS
19 from .download_manager import DownloadConfig, map_nested
20 from .file_utils import (
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/__init__.py in <module>
11
12 if _has_s3fs:
---> 13 from .s3filesystem import S3FileSystem # noqa: F401
14
15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/datasets/filesystems/s3filesystem.py in <module>
----> 1 import s3fs
2
3
4 class S3FileSystem(s3fs.S3FileSystem):
5 """
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/__init__.py in <module>
----> 1 from .core import S3FileSystem, S3File
2 from .mapping import S3Map
3
4 from ._version import get_versions
5
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/s3fs/core.py in <module>
12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper
13
---> 14 import aiobotocore
15 import botocore
16 import aiobotocore.session
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/__init__.py in <module>
----> 1 from .session import get_session, AioSession
2
3 __all__ = ['get_session', 'AioSession']
4 __version__ = '1.3.0'
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/session.py in <module>
4 from botocore import retryhandler, translate
5 from botocore.exceptions import PartialCredentialsError
----> 6 from .client import AioClientCreator, AioBaseClient
7 from .hooks import AioHierarchicalEmitter
8 from .parsers import AioResponseParserFactory
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/client.py in <module>
11 from .args import AioClientArgsCreator
12 from .utils import AioS3RegionRedirector
---> 13 from . import waiter
14
15 history_recorder = get_global_history_recorder()
~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/aiobotocore/waiter.py in <module>
4 from botocore.exceptions import ClientError
5 from botocore.waiter import WaiterModel # noqa: F401, lgtm[py/unused-import]
----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \
7 NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error
8 from botocore.docs.docstring import WaiterDocstring
ImportError: cannot import name 'is_valid_waiter_error'
```
Please let me know if there's anything else I can add to post.
[1]: https://github.com/huggingface/notebooks/blob/master/examples/text_classification_quantization_inc.ipynb
|
CLOSED
| 2022-01-10T10:32:04
| 2022-02-14T09:35:57
| 2022-02-14T09:35:57
|
https://github.com/huggingface/datasets/issues/3554
|
danielbellhv
| 3
|
[
"bug"
] |
3,553
|
set_format("np") no longer works for Image data
|
## Describe the bug
`dataset.set_format("np")` no longer works for image data, previously you could load the MNIST like this:
```python
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = dataset["train"]["image"][..., None] # <== No longer a numpy array
```
but now it doesn't work, `set_format("np")` seems to have no effect and the dataset just returns a list/array of PIL images instead of numpy arrays as requested.
|
CLOSED
| 2022-01-09T17:18:13
| 2022-10-14T12:03:55
| 2022-10-14T12:03:54
|
https://github.com/huggingface/datasets/issues/3553
|
cgarciae
| 5
|
[
"bug"
] |
3,550
|
Bug in `openbookqa` dataset
|
## Describe the bug
Dataset entries contains a typo.
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> obqa = load_dataset('openbookqa', 'main')
>>> obqa['train'][0]
```
## Expected results
```python
{'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['A', 'B', 'C', 'D']}, 'answerKey': 'D'}
```
## Actual results
```python
{'id': '7-980', 'question_stem': 'The sun is responsible for', 'choices': {'text': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting'], 'label': ['puppies learning new tricks', 'children growing up and getting old', 'flowers wilting in a vase', 'plants sprouting, blooming and wilting']}, 'answerKey': 'D'}
```
The bug is present in all configs and all splits.
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.4.0-1057-aws-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 4.0.1
|
CLOSED
| 2022-01-07T17:32:57
| 2022-05-04T06:33:00
| 2022-05-04T06:32:19
|
https://github.com/huggingface/datasets/issues/3550
|
lucadiliello
| 1
|
[
"bug",
"dataset bug"
] |
3,548
|
Specify the feature types of a dataset on the Hub without needing a dataset script
|
**Is your feature request related to a problem? Please describe.**
Currently if I upload a CSV with paths to audio files, the column type is string instead of Audio.
**Describe the solution you'd like**
I'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.
The feature types could read from the `dataset_infos.json` for example.
**Describe alternatives you've considered**
Create a dataset script to specify the features, but that seems complicated for a simple thing.
cc @abidlabs
|
CLOSED
| 2022-01-07T15:17:06
| 2022-01-20T14:48:38
| 2022-01-20T14:48:38
|
https://github.com/huggingface/datasets/issues/3548
|
lhoestq
| 1
|
[
"enhancement"
] |
3,547
|
Datasets created with `push_to_hub` can't be accessed in offline mode
|
## Describe the bug
In offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`.
## Steps to reproduce the bug
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
in bash:
```
export HF_DATASETS_OFFLINE=1
```
in Python:
```
import datasets
mpwiki = datasets.load_dataset("teven/matched_passages_wikidata")
```
## Expected results
`datasets` should find the previously-cached dataset.
## Actual results
ConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven/matched_passages_wikidata': Offline mode is enabled
## Environment info
- `datasets` version: 1.16.2.dev0
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 3.0.0
|
CLOSED
| 2022-01-07T15:12:25
| 2024-02-15T17:41:24
| 2023-12-21T15:13:12
|
https://github.com/huggingface/datasets/issues/3547
|
TevenLeScao
| 18
|
[
"bug"
] |
3,544
|
Ability to split a dataset in multiple files.
|
Hello,
**Is your feature request related to a problem? Please describe.**
My use case is that I have one writer that adds columns and multiple workers reading the same `Dataset`. Each worker should have access to columns added by the writer when they reload the dataset.
I understand that we shouldn't overwrite an arrow file as this could cause Segfault and so on. Before 1.16, I was able to overwrite the dataset and that would work most of the time with some retries.
**Describe the solution you'd like**
I was thinking that if we could append `Dataset._data_files`, when the workers reload the Dataset, they would get the new columns.
**Describe alternatives you've considered**
I currently need to
1. Save multiple "versions" of the dataset and load the latest.
2. Try working with cache files to get the latest columns.
**Additional context**
I think this would be a great addition to HFDataset as Parquet supports multi-files input out of the box!
I can make a PR myself with some pointers as needed :)
|
OPEN
| 2022-01-06T23:02:25
| 2022-01-06T23:02:25
| null |
https://github.com/huggingface/datasets/issues/3544
|
Dref360
| 0
|
[
"enhancement"
] |
3,543
|
Allow loading community metrics from the hub, just like datasets
|
**Is your feature request related to a problem? Please describe.**
Currently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.
However, there is no option to do it with the metric uploaded to the hub.
This means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.
**Describe the solution you'd like**
Load metrics from the hub just like datasets are loaded.
In order to not break stuff, the convention can be to put the metric file in a "metrics" folder in the hub.
|
CLOSED
| 2022-01-06T11:26:26
| 2022-05-31T20:59:14
| 2022-05-31T20:53:37
|
https://github.com/huggingface/datasets/issues/3543
|
eladsegal
| 5
|
[
"enhancement",
"generic discussion"
] |
3,541
|
Support 7-zip compressed data files
|
**Is your feature request related to a problem? Please describe.**
We should support 7-zip compressed data files:
- [x] in `extract`:
- #4672
- [ ] in `iter_archive`: for streaming mode
both in streaming and non-streaming modes.
|
OPEN
| 2022-01-06T07:11:03
| 2022-07-19T10:18:30
| null |
https://github.com/huggingface/datasets/issues/3541
|
albertvillanova
| 1
|
[
"enhancement"
] |
3,540
|
How to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
|
Hi,
I use torch.utils.data.Dataset to define my own data, but I need to use the 'map' function of datasets.arrow_dataset.Dataset later, so I hope to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset.
Here is an example.
```
from torch.utils.data import Dataset
from datasets.arrow_dataset import Dataset as HFDataset
class ADataset(Dataset):
def __init__(self, data):
super().__init__()
self.data = data
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class MDataset():
def __init__(self, tokenizer: AutoTokenizer, data_args, training_args):
self.train_dataset = ADataset(data_args)
self.tokenizer = tokenizer
self.data_args = data_args
self.train_dataset = self.train_dataset.map(
self.process_function,
batched=True,
remove_columns=column_names,
load_from_cache_file=True,
desc="Running tokenizer on train dataset",
)
def process_function(self, examples):
sentences = [" ".join(sample[0][3]) for sample in examples]
tokenized = self.tokenizer(
sentences,
max_length=self.max_seq_len,
padding=self.padding,
truncation=True)
```
But it would raise an ERROR, AttributeError: 'ADataset' object has no attribute 'map'.
so how to convert torch.utils.data.Dataset to datasets.arrow_dataset.Dataset?
Thanks in advance!
|
OPEN
| 2022-01-06T02:13:42
| 2022-01-06T02:17:39
| null |
https://github.com/huggingface/datasets/issues/3540
|
CindyTing
| 0
|
[
"enhancement"
] |
3,533
|
Task search function on hub not working correctly
|
When I want to look at all datasets of the category: `speech-processing` *i.e.* https://huggingface.co/datasets?task_categories=task_categories:speech-processing&sort=downloads , then the following dataset doesn't show up for some reason:
- https://huggingface.co/datasets/speech_commands
even thought it's task tags seem correct:
https://raw.githubusercontent.com/huggingface/datasets/master/datasets/speech_commands/README.md
|
OPEN
| 2022-01-05T09:36:30
| 2022-05-12T14:45:57
| null |
https://github.com/huggingface/datasets/issues/3533
|
patrickvonplaten
| 3
|
[
"bug"
] |
3,531
|
Give clearer instructions to add the YAML tags
|
## Describe the bug
As reported by @julien-c, many community datasets contain the line `YAML tags:` at the top of the YAML section in the header of the README file. See e.g.: https://huggingface.co/datasets/bigscience/P3/commit/a03bea08cf4d58f268b469593069af6aeb15de32
Maybe we should give clearer instruction/hints in the README template.
|
CLOSED
| 2022-01-05T06:44:20
| 2022-01-17T15:54:36
| 2022-01-17T15:54:36
|
https://github.com/huggingface/datasets/issues/3531
|
albertvillanova
| 0
|
[
"bug"
] |
3,522
|
wmt19 is broken (zh-en)
|
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("wmt19", 'zh-en')
```
## Expected results
The dataset should download.
## Actual results
`ConnectionError: Couldn't reach ftp://cwmt-wmt:cwmt-wmt@datasets.nju.edu.cn/parallel/casia2015.zip`
## Environment info
- `datasets` version: 1.15.1
- Platform: Linux
- Python version: 3.8
|
CLOSED
| 2022-01-04T22:33:45
| 2022-05-06T16:27:37
| 2022-05-06T16:27:37
|
https://github.com/huggingface/datasets/issues/3522
|
AjayP13
| 1
|
[
"bug",
"dataset bug"
] |
3,518
|
Add PubMed Central Open Access dataset
|
## Adding a Dataset
- **Name:** PubMed Central Open Access
- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
CLOSED
| 2022-01-04T06:54:35
| 2022-01-17T15:25:57
| 2022-01-17T15:25:57
|
https://github.com/huggingface/datasets/issues/3518
|
albertvillanova
| 3
|
[
"dataset request"
] |
3,515
|
`ExpectedMoreDownloadedFiles` for `evidence_infer_treatment`
|
## Describe the bug
I am trying to load a dataset called `evidence_infer_treatment`. The first subset (`1.1`) works fine but the second returns an error (`2.0`). It downloads a file but crashes during the checksums.
## Steps to reproduce the bug
```python
>>> from datasets import load_dataset
>>> load_dataset("evidence_infer_treatment", "2.0")
Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset
use_auth_token=use_auth_token,
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 664, in _download_and_prepare
self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), "dataset source files"
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 33, in verify_checksums
raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'http://evidence-inference.ebm-nlp.com/v2.0.tar.gz'}
```
I did try to pass the argument `ignore_verifications=True` but run into an error when trying to build the dataset:
```python
>>> load_dataset("evidence_infer_treatment", "2.0", ignore_verifications=True, download_mode="force_redownload")
Downloading and preparing dataset evidence_infer_treatment/2.0 (download: 34.84 MiB, generated: 91.46 MiB, post-processed: Unknown size, total: 126.30 MiB) to /home/victor_huggingface_co/.cache/huggingface/datasets/evidence_infer_treatment/2.0/2.0.0/6812655bfd26cbaa58c84eab098bf6403694b06c6ae2ded603c55681868a1e24...
Downloading: 164MB [00:23, 6.98MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/load.py", line 1669, in load_dataset
use_auth_token=use_auth_token,
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 594, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 681, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/builder.py", line 1080, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 1032, in encode_example
return encode_nested_example(self, example)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in encode_nested_example
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 807, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in encode_nested_example
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 829, in <listcomp>
list_dict[k] = [encode_nested_example(dict_tuples[0], o) for o in dict_tuples[1:]]
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/features/features.py", line 828, in encode_nested_example
for k, dict_tuples in utils.zip_dict(schema.feature, *obj):
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in zip_dict
yield key, tuple(d[key] for d in dicts)
File "/home/victor_huggingface_co/miniconda3/envs/promptsource/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 136, in <genexpr>
yield key, tuple(d[key] for d in dicts)
KeyError: ''
```
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid
- Python version: 3.7.11
- PyArrow version: 6.0.1
|
CLOSED
| 2022-01-03T15:58:38
| 2022-02-14T13:21:43
| 2022-02-14T13:21:43
|
https://github.com/huggingface/datasets/issues/3515
|
VictorSanh
| 1
|
[
"bug",
"dataset bug"
] |
3,512
|
No Data format found
|
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
CLOSED
| 2022-01-03T09:41:11
| 2022-01-17T13:26:05
| 2022-01-17T13:26:05
|
https://github.com/huggingface/datasets/issues/3512
|
shazzad47
| 1
|
[
"dataset-viewer"
] |
3,511
|
Dataset
|
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
CLOSED
| 2022-01-03T02:03:23
| 2022-01-03T08:41:26
| 2022-01-03T08:23:07
|
https://github.com/huggingface/datasets/issues/3511
|
MIKURI0114
| 2
|
[
"dataset-viewer"
] |
3,510
|
`wiki_dpr` details for Open Domain Question Answering tasks
|
Hey guys!
Thanks for creating the `wiki_dpr` dataset!
I am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton!
P.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!)
|
CLOSED
| 2022-01-02T11:04:01
| 2022-02-17T13:46:20
| 2022-02-17T13:46:20
|
https://github.com/huggingface/datasets/issues/3510
|
pk1130
| 2
|
[] |
3,507
|
Discuss whether support canonical datasets w/o dataset_infos.json and/or dummy data
|
I open this PR to have a public discussion about this topic and make a decision.
As previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?
On the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:
- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)
- we are migrating canonical datasets to the Hub
Do we really need to continue testing them in out CI?
Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).
Feel free to ping other people for the discussion.
CC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw
|
CLOSED
| 2021-12-30T17:04:25
| 2022-11-04T15:31:38
| 2022-11-04T15:31:37
|
https://github.com/huggingface/datasets/issues/3507
|
albertvillanova
| 17
|
[
"enhancement",
"generic discussion"
] |
3,505
|
cast_column function not working with map function in streaming mode for Audio features
|
## Describe the bug
I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.
I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset, Audio
from transformers import Wav2Vec2Processor
def encode(batch, processor):
print("Audio: ",batch['audio'])
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
return batch
def print_ds(ds):
iterator = iter(ds)
for d in iterator:
print("Data: ",d)
break
processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)
dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'},
data_dir="data", streaming=True, split="train")
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.map(lambda x: encode(x,processor))
print("Features: ",dataset.features)
print_ds(dataset)
```
## Expected results
map function not printing Audio type features be used with processor function and getting error in processor call due to this.
## Actual results
# after load_dataset call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'}
# after cast_column call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ...,
1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}
# after map call
Features: None
Audio: data/0116_003.wav
Traceback (most recent call last):
File "demo2.py", line 36, in <module>
print_ds(dataset)
File "demo2.py", line 11, in print_ds
for d in iterator:
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "demo2.py", line 32, in <lambda>
dataset = dataset.map(lambda x: batch_encode(x,processor))
File "demo2.py", line 6, in batch_encode
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
TypeError: string indices must be integers
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
|
CLOSED
| 2021-12-30T14:52:01
| 2022-01-18T19:54:07
| 2022-01-18T19:54:07
|
https://github.com/huggingface/datasets/issues/3505
|
ashu5644
| 1
|
[
"bug"
] |
3,504
|
Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst
|
## Describe the bug
I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt).
https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
I also tried with `wget` as follows.
```
wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
```
## Expected results
I expect to be able to download this file.
## Actual results
Traceback
```
---------------------------------------------------------------------------
timeout Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
158 try:
--> 159 conn = connection.create_connection(
160 (self._dns_host, self.port), self.timeout, **extra_kw
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
timeout: timed out
During handling of the above exception, another exception occurred:
ConnectTimeoutError Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
664 # Make the request on the httplib connection object.
--> 665 httplib_response = self._make_request(
666 conn,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
375 try:
--> 376 self._validate_conn(conn)
377 except (SocketTimeout, BaseSSLError) as e:
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 996 conn.connect()
997
/usr/lib/python3/dist-packages/urllib3/connection.py in connect(self)
313 # Add certificate verification
--> 314 conn = self._new_conn()
315 hostname = self.host
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
163 except SocketTimeout:
--> 164 raise ConnectTimeoutError(
165 self,
ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
438 if not chunked:
--> 439 resp = conn.urlopen(
440 method=request.method,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
718
--> 719 retries = retries.increment(
720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
/usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
435 if new_retry.is_exhausted():
--> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause))
437
MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
During handling of the above exception, another exception occurred:
ConnectTimeout Traceback (most recent call last)
/tmp/ipykernel_15104/606583593.py in <module>
3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)
4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
6 pubmed_dataset
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1655
1656 # Create a dataset builder
-> 1657 builder_instance = load_dataset_builder(
1658 path=path,
1659 name=name,
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)
1492 download_config = download_config.copy() if download_config else DownloadConfig()
1493 download_config.use_auth_token = use_auth_token
-> 1494 dataset_module = dataset_module_factory(
1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
1496 )
~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)
1116 # Try packaged
1117 if path in _PACKAGED_DATASETS_MODULES:
-> 1118 return PackagedDatasetModuleFactory(
1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode
1120 ).get_module()
~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self)
773 else get_patterns_locally(str(Path().resolve()))
774 )
--> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]
777 builder_kwargs = {"hash": hash, "data_files": data_files}
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
576 for key, patterns_for_key in patterns.items():
577 out[key] = (
--> 578 DataFilesList.from_local_or_remote(
579 patterns_for_key,
580 base_path=base_path,
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
545 base_path = base_path if base_path is not None else str(Path().resolve())
546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
--> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
548 return cls(data_files, origin_metadata)
549
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)
492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None
493 ) -> Tuple[str]:
--> 494 return thread_map(
495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),
496 data_files,
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)
92 """
93 from concurrent.futures import ThreadPoolExecutor
---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
95
96
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)
74 map_args.update(chunksize=chunksize)
75 with PoolExecutor(**pool_kwargs) as ex:
---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
77
78
~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self)
252 def __iter__(self):
253 try:
--> 254 for obj in super(tqdm_notebook, self).__iter__():
255 # return super(tqdm...) will not catch exception
256 yield obj
~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self)
1171 # (note: keep this check outside the loop for performance)
1172 if self.disable:
-> 1173 for obj in iterable:
1174 yield obj
1175 return
/usr/lib/python3.8/concurrent/futures/_base.py in result_iterator()
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield fs.pop().result()
620 else:
621 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
442 raise CancelledError()
443 elif self._state == FINISHED:
--> 444 return self.__get_result()
445 else:
446 raise TimeoutError()
/usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
387 if self._exception:
388 try:
--> 389 raise self._exception
390 finally:
391 # Break a reference cycle with the exception in self._exception
/usr/lib/python3.8/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)
483 if isinstance(data_file, Url):
484 data_file = str(data_file)
--> 485 return (request_etag(data_file, use_auth_token=use_auth_token),)
486 else:
487 data_file = str(data_file.resolve())
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token)
489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:
490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
--> 491 response = http_head(url, headers=headers, max_retries=3)
492 response.raise_for_status()
493 etag = response.headers.get("ETag") if response.ok else None
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
474 headers = copy.deepcopy(headers) or {}
475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent"))
--> 476 response = _request_with_retry(
477 method="HEAD",
478 url=url,
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
408 if tries > max_retries:
--> 409 raise err
410 else:
411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]")
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
403 tries += 1
404 try:
--> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
406 success = True
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs)
58 # cases, and look like a memory leak in others.
59 with sessions.Session() as session:
---> 60 return session.request(method=method, url=url, **kwargs)
61
62
/usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
531 }
532 send_kwargs.update(settings)
--> 533 resp = self.send(prep, **send_kwargs)
534
535 return resp
/usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs)
644
645 # Send the request
--> 646 r = adapter.send(request, **kwargs)
647
648 # Total elapsed time of the request (approximately)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
502 # TODO: Remove this in 3.0.0: see #2811
503 if not isinstance(e.reason, NewConnectionError):
--> 504 raise ConnectTimeout(e, request=request)
505
506 if isinstance(e.reason, ResponseError):
ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
```
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.1
|
CLOSED
| 2021-12-29T18:23:20
| 2024-05-20T09:44:59
| 2022-02-17T15:04:25
|
https://github.com/huggingface/datasets/issues/3504
|
ToddMorrill
| 10
|
[
"bug",
"dataset bug"
] |
3,503
|
Batched in filter throws error
|
I hope this is really a bug, I could not find it among the open issues
## Describe the bug
using `batched=False` in DataSet.filter throws error
```python
TypeError: filter() got an unexpected keyword argument 'batched'
```
but in the docs it is lister as an argument.
## Steps to reproduce the bug
```python
task = "mnli"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/")
dataset = load_dataset("glue", task)
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mnli-mm": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
##### tokenization_parameters
sentence1_key, sentence2_key = task_to_keys[task]
def preprocess_function(examples, max_length):
if sentence2_key is None:
return tokenizer(
examples[sentence1_key], truncation=True, max_length=max_length
)
return tokenizer(
examples[sentence1_key],
examples[sentence2_key],
truncation=False,
padding="max_length",
max_length=max_length,
)
encoded_dataset = dataset.map(
lambda x: preprocess_function(x, max_length=max_length), batched=False
)
encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1, 1.17.0
- Platform: ubuntu
- Python version: 3.8.12
|
CLOSED
| 2021-12-29T12:01:04
| 2022-01-04T10:24:27
| 2022-01-04T10:24:27
|
https://github.com/huggingface/datasets/issues/3503
|
gpucce
| 0
|
[
"bug"
] |
3,499
|
Adjusting chunk size for streaming datasets
|
**Is your feature request related to a problem? Please describe.**
I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing.
**Describe the solution you'd like**
I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.
|
CLOSED
| 2021-12-28T21:17:53
| 2022-05-06T16:29:05
| 2022-05-06T16:29:05
|
https://github.com/huggingface/datasets/issues/3499
|
JoelNiklaus
| 2
|
[
"enhancement"
] |
3,497
|
Changing sampling rate in audio dataset and subsequently mapping with `num_proc > 1` leads to weird bug
|
Running:
```python
from datasets import load_dataset, DatasetDict
import datasets
from transformers import AutoFeatureExtractor
raw_datasets = DatasetDict()
raw_datasets["train"] = load_dataset("common_voice", "ab", split="train")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
raw_datasets = raw_datasets.cast_column(
"audio", datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
)
num_workers = 16
def prepare_dataset(batch):
sample = batch["audio"]
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
batch["input_values"] = inputs.input_values[0]
batch["input_length"] = len(batch["input_values"])
return batch
raw_datasets.map(
prepare_dataset,
remove_columns=next(iter(raw_datasets.values())).column_names,
num_proc=16,
desc="preprocess datasets",
)
```
gives
```bash
File "/home/patrick/experiments/run_bug.py", line 25, in <module>
raw_datasets.map(
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 492, in map
{
File "/home/patrick/python_bin/datasets/dataset_dict.py", line 493, in <dictcomp>
k: dataset.map(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2139, in map
shards = [
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2140, in <listcomp>
self.shard(num_shards=num_proc, index=rank, contiguous=True, keep_in_memory=keep_in_memory)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 3164, in shard
return self.select(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 485, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/fingerprint.py", line 411, in wrapper
out = func(self, *args, **kwargs)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2756, in select
return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 2667, in _new_dataset_with_indices
return Dataset(
File "/home/patrick/python_bin/datasets/arrow_dataset.py", line 659, in __init__
raise ValueError(
ValueError: External features info don't match the dataset:
Got
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: string, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
but expected something like
{'client_id': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None), 'down_votes': Value(dtype='int64', id=None), 'age': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'accent': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None)}
with type
struct<client_id: string, path: string, audio: struct<path: string, bytes: binary>, sentence: string, up_votes: int64, down_votes: int64, age: string, gender: string, accent: string, locale: string, segment: string>
```
Versions:
```python
- `datasets` version: 1.16.2.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
- PyArrow version: 6.0.1
```
and `transformers`:
```
- `transformers` version: 4.16.0.dev0
- Platform: Linux-5.15.8-76051508-generic-x86_64-with-glibc2.33
- Python version: 3.9.7
```
|
CLOSED
| 2021-12-28T18:03:49
| 2022-01-21T13:22:27
| 2022-01-21T13:22:27
|
https://github.com/huggingface/datasets/issues/3497
|
patrickvonplaten
| 2
|
[
"bug"
] |
3,495
|
Add VoxLingua107
|
## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** 107 languages, totaling 6628 hours for the train split.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
OPEN
| 2021-12-28T15:51:43
| 2021-12-28T15:51:43
| null |
https://github.com/huggingface/datasets/issues/3495
|
jaketae
| 0
|
[
"dataset request"
] |
3,491
|
Update version of pib dataset
|
On the Hub we have v0, while there exists v1.3.
Related to bigscience-workshop/data_tooling#130
|
CLOSED
| 2021-12-28T14:03:58
| 2021-12-29T08:42:57
| 2021-12-29T08:42:57
|
https://github.com/huggingface/datasets/issues/3491
|
albertvillanova
| 0
|
[
"dataset request"
] |
3,490
|
Does datasets support load text from HDFS?
|
The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine,
so I wander does datasets support read data from hdfs?
|
OPEN
| 2021-12-28T08:56:02
| 2022-02-14T14:00:51
| null |
https://github.com/huggingface/datasets/issues/3490
|
dancingpipi
| 1
|
[
"enhancement"
] |
3,488
|
URL query parameters are set as path in the compression hop for fsspec
|
## Describe the bug
There is an ssue with `StreamingDownloadManager._extract`.
I don't know how the test `test_streaming_gg_drive_gzipped` passes:
For
```python
TEST_GG_DRIVE_GZIPPED_URL = "https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz"
urlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL)
```
gives `urlpath`:
```python
'gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz'
```
The gzip path makes no sense: `gzip://uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz`
## Steps to reproduce the bug
```python
from datasets.utils.streaming_download_manager import StreamingDownloadManager
dl_manager = StreamingDownloadManager()
urlpath = dl_manager.extract("https://drive.google.com/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz")
print(urlpath)
```
## Expected results
The query parameters should not be set as part of the path.
|
OPEN
| 2021-12-27T16:29:00
| 2022-01-05T15:15:25
| null |
https://github.com/huggingface/datasets/issues/3488
|
albertvillanova
| 1
|
[
"bug"
] |
3,485
|
skip columns which cannot set to specific format when set_format
|
**Is your feature request related to a problem? Please describe.**
When using `dataset.set_format("torch")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.
**Describe the solution you'd like**
skip columns which cannot set to specific format when set_format instead of raise an error.
|
CLOSED
| 2021-12-27T07:19:55
| 2021-12-27T09:07:07
| 2021-12-27T09:07:07
|
https://github.com/huggingface/datasets/issues/3485
|
tshu-w
| 2
|
[
"enhancement"
] |
3,484
|
make shape verification to use ArrayXD instead of nested lists for map
|
As describe in https://github.com/huggingface/datasets/issues/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https://colab.research.google.com/drive/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added.
|
OPEN
| 2021-12-27T02:16:02
| 2022-01-05T13:54:03
| null |
https://github.com/huggingface/datasets/issues/3484
|
tshu-w
| 1
|
[
"enhancement"
] |
3,480
|
the compression format requested when saving a dataset in json format is not respected
|
## Describe the bug
In the documentation of the `to_json` method, it is stated in the parameters that
> **to_json_kwargs – Parameters to pass to pandas’s pandas.DataFrame.to_json.
however when we pass for example `compression="gzip"`, the saved file is not compressed.
Would you also have expected compression to be applied? :relaxed:
## Steps to reproduce the bug
```python
my_dict = {"a": [1, 2, 3], "b": [1, 2, 3]}
```
### Result with datasets
```python
from datasets import Dataset
dataset = Dataset.from_dict(my_dict)
dataset.to_json("dic_with_datasets.jsonl.gz", compression="gzip")
!cat dic_with_datasets.jsonl.gz
```
output
```
{"a":1,"b":1}
{"a":2,"b":2}
{"a":3,"b":3}
```
Note: I would expected to see binary data here
### Result with pandas
```python
import pandas as pd
df = pd.DataFrame(my_dict)
df.to_json("dic_with_pandas.jsonl.gz", lines=True, orient="records", compression="gzip")
!cat dic_with_pandas.jsonl.gz
```
output
```
4��a�dic_with_pandas.jsonl��VJT�2�QJ��\� ��g��yƵ���������)���
```
Note: It looks like binary data
## Expected results
I would have expected that the saved result with datasets would also be a binary file
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.11
- PyArrow version: 5.0.0
|
CLOSED
| 2021-12-24T09:23:51
| 2022-01-05T13:03:35
| 2022-01-05T13:03:35
|
https://github.com/huggingface/datasets/issues/3480
|
SaulLu
| 3
|
[
"bug"
] |
3,479
|
Dataset preview is not available (I think for all Hugging Face datasets)
|
## Dataset viewer issue for '*french_book_reviews*'
**Link:** https://huggingface.co/datasets/Abirate/french_book_reviews
**short description of the issue**
For my dataset, the dataset preview is no longer functional (it used to work: The dataset had been added the day before and it was fine...)
And, after looking over the datasets, I discovered that this issue affects all Hugging Face datasets (as of yesterday, December 23, 2021, around 10 p.m. (CET)).
**Am I the one who added this dataset** : Yes
**Note**: here a screenshot showing the issue
**And here for glue dataset :**
|
CLOSED
| 2021-12-24T08:18:48
| 2021-12-24T14:27:46
| 2021-12-24T14:27:46
|
https://github.com/huggingface/datasets/issues/3479
|
Abirate
| 4
|
[
"bug",
"dataset-viewer"
] |
3,475
|
The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish
|
## Describe the bug
See title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user.
## Steps to reproduce the bug
Go to the [dataset viewer](https://huggingface.co/datasets/viewer/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that.
## Expected results
English movie reviews only.
## Actual results
Example of a Spanish movie review (4173):
> "É uma pena que , mais tarde , o próprio filme abandone o tom de paródia e passe a utilizar os mesmos clichês que havia satirizado "
|
OPEN
| 2021-12-23T03:56:43
| 2021-12-24T00:23:03
| null |
https://github.com/huggingface/datasets/issues/3475
|
puzzler10
| 2
|
[
"bug"
] |
3,473
|
Iterating over a vision dataset doesn't decode the images
|
## Describe the bug
If I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.
## Steps to reproduce the bug
```python
from datasets import load_dataset
import PIL
mnist = load_dataset("mnist", split="train")
first_image = mnist[0]["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # passes
first_image = next(iter(mnist))["image"]
assert isinstance(first_image, PIL.PngImagePlugin.PngImageFile) # fails
```
## Expected results
The image should be decoded, as a PIL Image
## Actual results
We get a dictionary
```
{'bytes': b'\x89PNG\r\n\x1a\n\x00..., 'path': None}
```
## Environment info
- `datasets` version: 1.17.1.dev0
- Platform: Darwin-20.6.0-x86_64-i386-64bit
- Python version: 3.7.2
- PyArrow version: 6.0.0
The bug also exists in 1.17.0
## Investigation
I think the issue is that decoding is disabled in `__iter__`:
https://github.com/huggingface/datasets/blob/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6/src/datasets/arrow_dataset.py#L1651-L1661
Do you remember why it was disabled in the first place @albertvillanova ?
Also cc @mariosasko @NielsRogge
|
CLOSED
| 2021-12-22T15:26:32
| 2021-12-27T14:13:21
| 2021-12-23T15:21:57
|
https://github.com/huggingface/datasets/issues/3473
|
lhoestq
| 9
|
[
"bug",
"vision"
] |
3,465
|
Unable to load 'cnn_dailymail' dataset
|
## Describe the bug
I wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)
```
## Expected results
Expecting to load 'cnn_dailymail' dataset.
## Actual results
`NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
|
CLOSED
| 2021-12-21T03:32:21
| 2024-06-12T14:41:17
| 2022-02-17T14:13:57
|
https://github.com/huggingface/datasets/issues/3465
|
talha1503
| 4
|
[
"bug",
"duplicate",
"dataset bug"
] |
3,464
|
struct.error: 'i' format requires -2147483648 <= number <= 2147483647
|
## Describe the bug
A clear and concise description of what the bug is.
using latest datasets=datasets-1.16.1-py3-none-any.whl
process my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:
then I get this error:
I have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux docker
- Python version: 3.6
|
OPEN
| 2021-12-21T03:29:01
| 2022-11-21T19:55:11
| null |
https://github.com/huggingface/datasets/issues/3464
|
koukoulala
| 2
|
[
"bug"
] |
3,462
|
Update swahili_news dataset
|
Please note also: the HuggingFace version at https://huggingface.co/datasets/swahili_news is outdated. An updated version, with deduplicated text and official splits, can be found at https://zenodo.org/record/5514203.
## Adding a Dataset
- **Name:** swahili_news
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Related to:
- bigscience-workshop/data_tooling#107
|
CLOSED
| 2021-12-20T17:44:01
| 2021-12-21T06:24:02
| 2021-12-21T06:24:01
|
https://github.com/huggingface/datasets/issues/3462
|
albertvillanova
| 0
|
[
"dataset request"
] |
3,459
|
dataset.filter overwriting previously set dataset._indices values, resulting in the wrong elements being selected.
|
## Describe the bug
When using dataset.select to select a subset of a dataset, dataset._indices are set to indicate which elements are now considered in the dataset.
The same thing happens when you shuffle the dataset; dataset._indices are set to indicate what the new order of the data is.
However, if you then use a dataset.filter, that filter interacts with those dataset._indices values in a non-intuitive manner.
https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset.filter
Effectively, it looks like the original set of _indices were discared and overwritten by the set created during the filter operation.
I think this is actually an issue with how the map function handles dataset._indices. Ideally it should use the _indices it gets passed, and then return an updated _indices which reflect the map transformation applied to the starting _indices.
## Steps to reproduce the bug
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print("initial 10 elements")
print(dataset['label']) # -> [1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
print("filtered 10 elements looking for label 0")
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1]
```
## Actual results
```
$ python indices_bug.py
initial 10 elements
[1, 1, 0, 1, 0, 0, 0, 1, 0, 0]
filtered 10 elements looking for label 0
[1, 1, 1, 1, 1, 1]
```
This code block first shuffles the dataset (to get a mix of label 0 and label 1).
Then it selects just the first 10 elements (the number of elements does not matter, 10 is just easy to visualize). The important part is that you select some subset of the dataset.
Finally, a filter is applied to pull out just the elements with `label == 0`.
The bug is that you cannot combine any dataset operation which sets the dataset._indices with filter.
In this case I have 2, shuffle and subset.
If you just use a single dataset._indices operation (in this case shuffle) the bug still shows up.
The shuffle sets the dataset._indices and then filter uses those indices in the map, then overwrites dataset._indices with the filter results.
```python
dataset = load_dataset('imdb', split='train', keep_in_memory=True)
dataset = dataset.shuffle(keep_in_memory=True)
dataset = dataset.filter(lambda x: x['label'] == 0, keep_in_memory=True)
dataset = dataset.select(range(0, 10), keep_in_memory=True)
print(dataset['label']) # -> [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
```
## Expected results
In an ideal world, the dataset filter would respect any dataset._indices values which had previously been set.
If you use dataset.filter with the base dataset (where dataset._indices has not been set) then the filter command works as expected.
## Environment info
Here are the commands required to rebuild the conda environment from scratch.
```
# create a virtual environment
conda create -n dataset_indices python=3.8 -y
# activate the virtual environment
conda activate dataset_indices
# install huggingface datasets
conda install datasets
```
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 3.0.0
### Full Conda Environment
```
$ conda env export
name: dasaset_indices
channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- abseil-cpp=20210324.2=h2531618_0
- aiohttp=3.8.1=py38h7f8727e_0
- aiosignal=1.2.0=pyhd3eb1b0_0
- arrow-cpp=3.0.0=py38h6b21186_4
- attrs=21.2.0=pyhd3eb1b0_0
- aws-c-common=0.4.57=he6710b0_1
- aws-c-event-stream=0.1.6=h2531618_5
- aws-checksums=0.1.9=he6710b0_0
- aws-sdk-cpp=1.8.185=hce553d0_0
- bcj-cffi=0.5.1=py38h295c915_0
- blas=1.0=mkl
- boost-cpp=1.73.0=h27cfd23_11
- bottleneck=1.3.2=py38heb32a55_1
- brotli=1.0.9=he6710b0_2
- brotli-python=1.0.9=py38heb0550a_2
- brotlicffi=1.0.9.2=py38h295c915_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.10.26=h06a4308_2
- certifi=2021.10.8=py38h06a4308_0
- cffi=1.14.6=py38h400218f_0
- conllu=4.4.1=pyhd3eb1b0_0
- cryptography=36.0.0=py38h9ce1e76_0
- dataclasses=0.8=pyh6d0b6a4_7
- dill=0.3.4=pyhd3eb1b0_0
- double-conversion=3.1.5=he6710b0_1
- et_xmlfile=1.1.0=py38h06a4308_0
- filelock=3.4.0=pyhd3eb1b0_0
- frozenlist=1.2.0=py38h7f8727e_0
- gflags=2.2.2=he6710b0_0
- glog=0.5.0=h2531618_0
- gmp=6.2.1=h2531618_2
- grpc-cpp=1.39.0=hae934f6_5
- huggingface_hub=0.0.17=pyhd3eb1b0_0
- icu=58.2=he6710b0_3
- idna=3.3=pyhd3eb1b0_0
- importlib-metadata=4.8.2=py38h06a4308_0
- importlib_metadata=4.8.2=hd3eb1b0_0
- intel-openmp=2021.4.0=h06a4308_3561
- krb5=1.19.2=hac12032_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libboost=1.73.0=h3ff78a5_11
- libcurl=7.80.0=h0b77cf5_0
- libedit=3.1.20210910=h7f8727e_0
- libev=4.33=h7f8727e_1
- libevent=2.1.8=h1ba5d50_1
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libnghttp2=1.46.0=hce63b2e_0
- libprotobuf=3.17.2=h4ff587b_1
- libssh2=1.9.0=h1ba5d50_1
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libthrift=0.14.2=hcc01f38_0
- libxml2=2.9.12=h03d6c58_0
- libxslt=1.1.34=hc22bd24_0
- lxml=4.6.3=py38h9120a33_0
- lz4-c=1.9.3=h295c915_1
- mkl=2021.4.0=h06a4308_640
- mkl-service=2.4.0=py38h7f8727e_0
- mkl_fft=1.3.1=py38hd3c417c_0
- mkl_random=1.2.2=py38h51133e4_0
- multiprocess=0.70.12.2=py38h7f8727e_0
- multivolumefile=0.2.3=pyhd3eb1b0_0
- ncurses=6.3=h7f8727e_2
- numexpr=2.7.3=py38h22e1b3c_1
- numpy=1.21.2=py38h20f2e39_0
- numpy-base=1.21.2=py38h79a1101_0
- openpyxl=3.0.9=pyhd3eb1b0_0
- openssl=1.1.1l=h7f8727e_0
- orc=1.6.9=ha97a36c_3
- packaging=21.3=pyhd3eb1b0_0
- pip=21.2.4=py38h06a4308_0
- py7zr=0.16.1=pyhd3eb1b0_1
- pycparser=2.21=pyhd3eb1b0_0
- pycryptodomex=3.10.1=py38h27cfd23_1
- pyopenssl=21.0.0=pyhd3eb1b0_1
- pyparsing=3.0.4=pyhd3eb1b0_0
- pyppmd=0.16.1=py38h295c915_0
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.12=h12debd9_0
- python-dateutil=2.8.2=pyhd3eb1b0_0
- python-xxhash=2.0.2=py38h7f8727e_0
- pyzstd=0.14.4=py38h7f8727e_3
- re2=2020.11.01=h2531618_1
- readline=8.1=h27cfd23_0
- requests=2.26.0=pyhd3eb1b0_0
- setuptools=58.0.4=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- snappy=1.1.8=he6710b0_0
- sqlite=3.36.0=hc218d9a_0
- texttable=1.6.4=pyhd3eb1b0_0
- tk=8.6.11=h1ccaba5_0
- typing_extensions=3.10.0.2=pyh06a4308_0
- uriparser=0.9.3=he6710b0_1
- utf8proc=2.6.1=h27cfd23_0
- wheel=0.37.0=pyhd3eb1b0_1
- xxhash=0.8.0=h7f8727e_3
- xz=5.2.5=h7b6447c_0
- zipp=3.6.0=pyhd3eb1b0_0
- zlib=1.2.11=h7f8727e_4
- zstd=1.4.9=haebb681_0
- pip:
- async-timeout==4.0.2
- charset-normalizer==2.0.9
- datasets==1.16.1
- fsspec==2021.11.1
- huggingface-hub==0.2.1
- multidict==5.2.0
- pandas==1.3.5
- pyarrow==6.0.1
- pytz==2021.3
- pyyaml==6.0
- tqdm==4.62.3
- typing-extensions==4.0.1
- urllib3==1.26.7
- yarl==1.7.2
```
|
CLOSED
| 2021-12-20T16:16:49
| 2021-12-20T16:34:57
| 2021-12-20T16:34:57
|
https://github.com/huggingface/datasets/issues/3459
|
mmajurski
| 2
|
[
"bug"
] |
3,457
|
Add CMU Graphics Lab Motion Capture dataset
|
## Adding a Dataset
- **Name:** CMU Graphics Lab Motion Capture database
- **Description:** The database contains free motions which you can download and use.
- **Data:** http://mocap.cs.cmu.edu/
- **Motivation:** Nice motion capture dataset
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
OPEN
| 2021-12-20T14:34:39
| 2022-03-16T16:53:09
| null |
https://github.com/huggingface/datasets/issues/3457
|
osanseviero
| 3
|
[
"dataset request",
"vision"
] |
3,455
|
Easier information editing
|
**Is your feature request related to a problem? Please describe.**
It requires a lot of effort to improve a datasheet.
**Describe the solution you'd like**
UI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)
**Describe alternatives you've considered**
The current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.
|
CLOSED
| 2021-12-20T10:10:43
| 2023-07-25T15:36:14
| 2023-07-25T15:36:14
|
https://github.com/huggingface/datasets/issues/3455
|
borgr
| 2
|
[
"enhancement",
"generic discussion"
] |
3,453
|
ValueError while iter_archive
|
## Describe the bug
After the merge of:
- #3443
the method `iter_archive` throws a ValueError:
```
ValueError: read of closed file
```
## Steps to reproduce the bug
```python
for path, file in dl_manager.iter_archive(archive_path):
pass
```
|
CLOSED
| 2021-12-20T08:46:18
| 2021-12-20T10:04:59
| 2021-12-20T10:04:59
|
https://github.com/huggingface/datasets/issues/3453
|
albertvillanova
| 0
|
[
"bug"
] |
3,452
|
why the stratify option is omitted from test_train_split function?
|
why the stratify option is omitted from test_train_split function?
is there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.
|
CLOSED
| 2021-12-18T10:37:47
| 2022-05-25T20:43:51
| 2022-05-25T20:43:51
|
https://github.com/huggingface/datasets/issues/3452
|
j-sieger
| 4
|
[
"enhancement",
"good second issue"
] |
3,450
|
Unexpected behavior doing Split + Filter
|
## Describe the bug
I observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter')
## Steps to reproduce the bug
```
from datasets import Dataset
import pandas as pd
dic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}
df = pd.DataFrame.from_dict(dic)
dataset = Dataset.from_pandas(df)
split_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
eval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)
print( eval_dataset['x'])
print(eval_dataset_2['x'])
```
One observes that elements in eval_dataset2 are actually coming from the training dataset...
## Expected results
The expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: Windows 10
- Python version: 3.7
- PyArrow version: 5.0.0
|
CLOSED
| 2021-12-17T17:00:39
| 2023-07-25T15:38:47
| 2023-07-25T15:38:47
|
https://github.com/huggingface/datasets/issues/3450
|
jbrachat
| 1
|
[
"bug"
] |
3,449
|
Add `__add__()`, `__iadd__()` and similar to `Dataset` class
|
**Is your feature request related to a problem? Please describe.**
No.
**Describe the solution you'd like**
I would like to be able to concatenate datasets as follows:
```python
>>> dataset["train"] += dataset["validation"]
```
... instead of using `concatenate_datasets()`:
```python
>>> raw_datasets["train"] = concatenate_datasets([raw_datasets["train"], raw_datasets["validation"]])
>>> del raw_datasets["validation"]
```
**Describe alternatives you've considered**
Well, I have considered `concatenate_datasets()` 😀
**Additional context**
N.a.
|
CLOSED
| 2021-12-17T15:29:11
| 2024-02-29T16:47:56
| 2023-07-25T15:33:56
|
https://github.com/huggingface/datasets/issues/3449
|
sgraaf
| 2
|
[
"enhancement",
"generic discussion"
] |
3,448
|
JSONDecodeError with HuggingFace dataset viewer
|
## Dataset viewer issue for 'pubmed_neg'
**Link:** https://huggingface.co/datasets/IGESML/pubmed_neg
I am getting the error:
Status code: 400
Exception: JSONDecodeError
Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)
I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.
Am I the one who added this dataset ? Yes
|
CLOSED
| 2021-12-17T12:52:41
| 2022-02-24T09:10:26
| 2022-02-24T09:10:26
|
https://github.com/huggingface/datasets/issues/3448
|
kathrynchapman
| 3
|
[
"dataset-viewer"
] |
3,447
|
HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading
|
## Describe the bug
According to https://huggingface.co/docs/datasets/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to "run in full offline mode". It didn't work for me. At the very beginning, datasets still tried to download "custom data configuration" for JSON, despite I have run the program once and cached all data into the same --cache_dir.
"Downloading" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers "FileNotFound" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.
## Steps to reproduce the bug
```
export HF_DATASETS_OFFLINE=1
python run_clm.py --model_name_or_path=models/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache/trainpy.v2
```
## Expected results
datasets should stop all "downloading" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, "default-471372bed4b51b53", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.
## Actual results
The logging shows datasets are still downloading into "datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426".
```
12/16/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53
12/16/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)
Downloading and preparing dataset json/default to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 17623.13it/s]
12/16/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min
12/16/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min
100%|███████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1206.99it/s]
12/16/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.
12/16/2021 10:26:00 - INFO - datasets.builder - Generating split train
12/16/2021 10:26:01 - INFO - datasets.builder - Generating split validation
12/16/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.
Dataset json downloaded and prepared to datacache/trainpy.v2/json/default-471372bed4b51b53/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.
100%|█████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 53.54it/s]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1
- Platform: Linux
- Python version: 3.8.10
- PyArrow version: 6.0.1
|
CLOSED
| 2021-12-16T18:51:13
| 2022-02-17T14:16:27
| 2022-02-17T14:16:27
|
https://github.com/huggingface/datasets/issues/3447
|
dunalduck0
| 3
|
[
"bug"
] |
3,445
|
question
|
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
CLOSED
| 2021-12-16T15:57:00
| 2022-01-03T10:09:00
| 2022-01-03T10:09:00
|
https://github.com/huggingface/datasets/issues/3445
|
BAKAYOKO0232
| 1
|
[
"dataset-viewer"
] |
3,444
|
Align the Dataset and IterableDataset processing API
|
## Intro
items marked like <s>this</s> are done already :)
Currently the two classes have two distinct API for processing:
### The `.map()` method
Both have those parameters in common: function, batched, batch_size
- IterableDataset is missing those parameters:
<s>with_indices</s>, with_rank, <s>input_columns</s>, <s>drop_last_batch</s>, <s>remove_columns</s>, features, disable_nullable, fn_kwargs, num_proc
- Dataset also has additional parameters that are exclusive, due to caching:
keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint
- <s>There is also an important difference in terms of behavior:
**Dataset.map adds new columns** (with dict.update)
BUT
**IterableDataset discards previous columns** (it overwrites the dict)
IMO the two methods should have the same behavior. This would be an important breaking change though.</s>
- Dataset.map is eager while IterableDataset.map is lazy
### The `.shuffle()` method
- <s>Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.</s>
- <s>IterableDataset is missing the parameter generator</s>
- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint
### The `.with_format()` method
- <s>IterableDataset only supports "torch" (it misses tf, jax, pandas, arrow)</s> and is missing the parameters: columns, output_all_columns and format_kwargs
- other methods like `set_format`, `reset_format` or `formatted_as` are also missing
### Other methods
- Both have the same `remove_columns` method
- IterableDataset is missing: <s>cast</s>, <s>cast_column</s>, <s>filter</s>, <s>rename_column</s>, <s>rename_columns</s>, class_encode_column, flatten, train_test_split, <s>shard</s>
- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform
- And others don't really make sense for an iterable dataset: select, sort, <s>add_column</s>, add_item
- Dataset is missing skip and take, that IterableDataset implements.
## Questions
I think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.
1. What should be aligned and what shouldn't between those two APIs ?
IMO the minimum is to align the main processing methods.
It would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters. DONE ✅
It would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard. WIP 🟠
2. What are the breaking changes for IterableDataset ?
The main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them. DONE ✅
3. Shall we also do some changes for regular datasets ?
I agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:
- keep the eager Dataset.map with caching
- keep the with_transform method for lazy processing
- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)
We could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.
For information, TFDS does lazy map by default, and has an additional `.cache()` method.
## Opinions ?
I'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.
cc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger
|
OPEN
| 2021-12-16T11:26:11
| 2025-01-31T11:07:07
| null |
https://github.com/huggingface/datasets/issues/3444
|
lhoestq
| 11
|
[
"enhancement",
"generic discussion"
] |
3,441
|
Add QuALITY dataset
|
## Adding a Dataset
- **Name:** QuALITY
- **Description:** A challenging question answering with very long contexts (Twitter [thread](https://twitter.com/sleepinyourhat/status/1471225421794529281?s=20))
- **Paper:** No ArXiv link yet, but draft is [here](https://github.com/nyu-mll/quality/blob/main/quality_preprint.pdf)
- **Data:** GitHub repo [here](https://github.com/nyu-mll/quality)
- **Motivation:** This dataset would serve as a nice way to benchmark long-range Transformer models like BigBird, Longformer and their descendants. In particular, it would be very interesting to see how the S4 model fares on this given it's impressive performance on the Long Range Arena
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
OPEN
| 2021-12-15T22:26:19
| 2021-12-28T15:17:05
| null |
https://github.com/huggingface/datasets/issues/3441
|
lewtun
| 1
|
[
"dataset request"
] |
3,440
|
datasets keeps reading from cached files, although I disabled it
|
## Describe the bug
Hi,
I am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings:
```
from datasets import set_caching_enabled
set_caching_enabled(False)
```
also force redownlaod:
```
download_mode='force_redownload'
```
but none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq
many thanks
```
File "run_clm.py", line 496, in <module>
main()
File "run_clm.py", line 419, in main
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 943, in train
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/transformers/trainer.py", line 1445, in _maybe_log_save_evaluate
metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 172, in evaluate
output = self.eval_loop(
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 241, in eval_loop
metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 268, in compute_pet_metrics
centroids = self._compute_per_token_train_centroids(model, task=task)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 353, in _compute_per_token_train_centroids
data = get_label_samples(self.get_per_task_train_dataset(task), label)
File "/users/dara/codes/fewshot/debug/fewshot/third_party/trainers/trainer.py", line 350, in get_label_samples
return dataset.filter(lambda example: int(example['labels']) == label)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
out = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2519, in filter
indices = self.map(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2036, in map
return self._map_single(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 503, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 470, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/fingerprint.py", line 406, in wrapper
out = func(self, *args, **kwargs)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2248, in _map_single
return Dataset.from_file(cache_file_name, info=info, split=self.split)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 654, in from_file
return cls(
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 593, in __init__
self.info.features = self.info.features.reorder_fields_as(inferred_features)
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1092, in reorder_fields_as
return Features(recursive_reorder(self, other))
File "/users/dara/conda/envs/multisuccess/lib/python3.8/site-packages/datasets/features/features.py", line 1081, in recursive_reorder
raise ValueError(f"Keys mismatch: between {source} and {target}" + stack_position)
ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: linux
- Python version: 3.8.12
- PyArrow version: 6.0.1
|
CLOSED
| 2021-12-15T21:26:22
| 2022-02-24T09:12:22
| 2022-02-24T09:12:22
|
https://github.com/huggingface/datasets/issues/3440
|
dorost1234
| 1
|
[
"bug"
] |
3,434
|
Add The People's Speech
|
## Adding a Dataset
- **Name:** The People's Speech
- **Description:** a massive English-language dataset of audio transcriptions of full sentences.
- **Paper:** https://openreview.net/pdf?id=R8CwidgJ0yT
- **Data:** https://mlcommons.org/en/peoples-speech/
- **Motivation:** With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today.
[The article](https://thegradient.pub/new-datasets-to-democratize-speech-recognition-technology-2/) which may be useful when working on the dataset.
cc: @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
CLOSED
| 2021-12-15T11:21:21
| 2023-02-28T16:22:29
| 2023-02-28T16:22:28
|
https://github.com/huggingface/datasets/issues/3434
|
mariosasko
| 1
|
[
"dataset request",
"speech"
] |
3,433
|
Add Multilingual Spoken Words dataset
|
## Adding a Dataset
- **Name:** Multilingual Spoken Words
- **Description:** Multilingual Spoken Words Corpus is a large and growing audio dataset of spoken words in 50 languages for academic research and commercial applications in keyword spotting and spoken term search, licensed under CC-BY 4.0. The dataset contains more than 340,000 keywords, totaling 23.4 million 1-second spoken examples (over 6,000 hours).
Read more: https://mlcommons.org/en/news/spoken-words-blog/
- **Paper:** https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/fe131d7f5a6b38b23cc967316c13dae2-Paper-round2.pdf
- **Data:** https://mlcommons.org/en/multilingual-spoken-words/
- **Motivation:**
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
CLOSED
| 2021-12-15T11:14:44
| 2022-02-22T10:03:53
| 2022-02-22T10:03:53
|
https://github.com/huggingface/datasets/issues/3433
|
albertvillanova
| 0
|
[
"dataset request",
"speech"
] |
3,431
|
Unable to resolve any data file after loading once
|
when I rerun my program, it occurs this error
" Unable to resolve any data file that matches '['**train*']' at /data2/whr/lzy/open_domain_data/retrieval/wiki_dpr with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'zip']", so how could i deal with this problem?
thx.
And below is my code .
|
CLOSED
| 2021-12-14T15:02:15
| 2022-12-11T10:53:04
| 2022-02-24T09:13:52
|
https://github.com/huggingface/datasets/issues/3431
|
LzyFischer
| 2
|
[] |
3,425
|
Getting configs names takes too long
|
## Steps to reproduce the bug
```python
from datasets import get_dataset_config_names
get_dataset_config_names("allenai/c4")
```
## Expected results
I would expect to get the answer quickly, at least in less than 10s
## Actual results
It takes about 45s on my environment
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
|
OPEN
| 2021-12-13T14:27:57
| 2021-12-13T14:53:33
| null |
https://github.com/huggingface/datasets/issues/3425
|
severo
| 3
|
[
"bug"
] |
3,423
|
data duplicate when setting num_works > 1 with streaming data
|
## Describe the bug
The data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import pandas as pd
import numpy as np
import os
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import shutil
NUM_OF_USER = 1000000
NUM_OF_ACTION = 50000
NUM_OF_SEQUENCE = 10000
NUM_OF_FILES = 32
NUM_OF_WORKERS = 16
if __name__ == "__main__":
shutil.rmtree("./dataset")
for i in range(NUM_OF_FILES):
sequence_data = pd.DataFrame(
{
"imei": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),
"sequence": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)
}
)
if not os.path.exists("./dataset"):
os.makedirs("./dataset")
sequence_data.to_csv(f"./dataset/sequence_data_{i}.csv",
index=False)
dataset = load_dataset("csv",
data_files=[os.path.join("./dataset",file) for file in os.listdir("./dataset") if file.endswith(".csv")],
split="train",
streaming=True).with_format("torch")
data_loader = DataLoader(dataset,
batch_size=1024,
num_workers=NUM_OF_WORKERS)
result = pd.DataFrame()
for i, batch in tqdm(enumerate(data_loader)):
result = pd.concat([result,
pd.DataFrame(batch)],
axis=0)
result.to_csv(f"num_work_{NUM_OF_WORKERS}.csv", index=False)
```
## Expected results
data do not duplicate
## Actual results
data duplicate NUM_OF_WORKERS = 16
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:datasets==1.14.0
- Platform:transformers==4.11.3
- Python version:3.8
- PyArrow version:
|
CLOSED
| 2021-12-13T03:43:17
| 2022-12-14T16:04:22
| 2022-12-14T16:04:22
|
https://github.com/huggingface/datasets/issues/3423
|
cloudyuyuyu
| 14
|
[
"bug",
"streaming"
] |
3,422
|
Error about load_metric
|
## Describe the bug
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1371, in load_metric
metric = metric_cls(
TypeError: 'NoneType' object is not callable
## Steps to reproduce the bug
```python
metric = load_metric("glue", "sst2")
```
## Environment info
- `datasets` version: 1.16.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 6.0.1
|
CLOSED
| 2021-12-13T02:49:51
| 2022-01-07T14:06:47
| 2022-01-07T14:06:47
|
https://github.com/huggingface/datasets/issues/3422
|
jiacheng-ye
| 1
|
[
"bug"
] |
3,419
|
`.to_json` is extremely slow after `.select`
|
## Describe the bug
Saving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
original = load_dataset("squad", split="train")
original.to_json("from_original.json") # Takes 0 seconds
selected_subset1 = original.select([i for i in range(len(original))])
selected_subset1.to_json("from_select1.json") # Takes 212 seconds
selected_subset2 = original.select([i for i in range(int(len(original) / 2))])
selected_subset2.to_json("from_select2.json") # Takes 90 seconds
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master (https://github.com/huggingface/datasets/commit/6090f3cfb5c819f441dd4a4bb635e037c875b044)
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27
- Python version: 3.9.7
- PyArrow version: 6.0.0
|
OPEN
| 2021-12-11T01:36:31
| 2021-12-21T15:49:07
| null |
https://github.com/huggingface/datasets/issues/3419
|
eladsegal
| 6
|
[
"bug"
] |
3,416
|
disaster_response_messages unavailable
|
## Dataset viewer issue for '* disaster_response_messages*'
**Link:** https://huggingface.co/datasets/disaster_response_messages
Dataset unavailable. Link dead: https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
Am I the one who added this dataset ?No
|
CLOSED
| 2021-12-10T13:49:17
| 2021-12-14T14:38:29
| 2021-12-14T14:38:29
|
https://github.com/huggingface/datasets/issues/3416
|
sacdallago
| 1
|
[
"dataset-viewer"
] |
3,415
|
Non-deterministic tests: CI tests randomly fail
|
## Describe the bug
Some CI tests fail randomly.
1. In https://github.com/huggingface/datasets/pull/3375/commits/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =
```
2. After re-running the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):
- On Linux:
```
=========================== short test summary info ============================
FAILED tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped
= 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =
```
- On Windows:
```
=========================== short test summary info ===========================
FAILED tests/test_load.py::test_load_dataset_builder_for_community_dataset_without_script
= 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =
```
The test `tests/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.
3. After re-running again the CI (without any change in the code) in https://github.com/huggingface/datasets/pull/3375/commits/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.
|
CLOSED
| 2021-12-10T06:08:59
| 2022-03-31T16:38:51
| 2022-03-31T16:38:51
|
https://github.com/huggingface/datasets/issues/3415
|
albertvillanova
| 2
|
[
"bug"
] |
3,411
|
[chinese wwm] load_datasets behavior not as expected when using run_mlm_wwm.py script
|
## Describe the bug
Model I am using (Bert, XLNet ...): bert-base-chinese
The problem arises when using:
* [https://github.com/huggingface/transformers/blob/master/examples/research_projects/mlm_wwm/run_mlm_wwm.py] the official example scripts: `rum_mlm_wwm.py`
The tasks I am working on is: pretraining whole word masking with my own dataset and ref.json file
I tried follow the run_mlm_wwm.py procedure to do whole word masking on pretraining task. my file is in .txt form, where one line represents one sample, with `9,264,784` chinese lines in total. the ref.json file is also contains 9,264,784 lines of whole word masking reference data for my chinese corpus. but when I try to adapt the run_mlm_wwm.py script, it shows that somehow after
`datasets["train"] = load_dataset(...`
`len(datasets["train"])` returns `9,265,365`
then, after `tokenized_datasets = datasets.map(...`
`len(tokenized_datasets["train"])` returns `9,265,279`
I'm really confused and tried to trace code by myself but can't know what happened after a week trial.
I want to know what happened in the `load_dataset()` function and `datasets.map` here and how did I get more lines of data than I input. so I'm here to ask.
## To reproduce
Sorry that I can't provide my data here since it did not belong to me. but I'm sure I remove the blank lines.
## Expected behavior
I expect the code run as it should. but the AssertionError in line 167 keeps raise as the line of reference json and datasets['train'] differs.
Thanks for your patient reading!
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 3.0.0
|
OPEN
| 2021-12-09T17:54:35
| 2021-12-22T11:21:33
| null |
https://github.com/huggingface/datasets/issues/3411
|
hyusterr
| 2
|
[
"bug"
] |
3,408
|
Typo in Dataset viewer error message
|
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
When creating an empty dataset repo, the Dataset Preview provides a helpful message that no files were found. There is a tiny typo in that message: "ressource" should be "resource"
Am I the one who added this dataset ?
N/A
|
CLOSED
| 2021-12-09T14:34:02
| 2021-12-22T11:02:53
| 2021-12-22T11:02:53
|
https://github.com/huggingface/datasets/issues/3408
|
lewtun
| 1
|
[
"dataset-viewer"
] |
3,405
|
ZIP format inference does not work when files located in a dir inside the archive
|
## Describe the bug
When a zipped file contains archived files within a directory, the function `infer_module_for_data_files_in_archives` does not work.
It only works for files located in the root directory of the ZIP file.
## Steps to reproduce the bug
```python
infer_module_for_data_files_in_archives(["path/to/zip/file.zip"], False)
```
|
CLOSED
| 2021-12-08T12:32:15
| 2021-12-08T13:03:29
| 2021-12-08T13:03:29
|
https://github.com/huggingface/datasets/issues/3405
|
albertvillanova
| 0
|
[
"bug"
] |
3,404
|
Optimize ZIP format inference
|
**Is your feature request related to a problem? Please describe.**
When hundreds of ZIP files are present in a dataset, format inference takes too long.
See: https://github.com/bigscience-workshop/data_tooling/issues/232#issuecomment-986685497
**Describe the solution you'd like**
Iterate over a maximum number of files.
CC: @lhoestq
|
CLOSED
| 2021-12-07T18:44:49
| 2021-12-14T17:08:41
| 2021-12-14T17:08:41
|
https://github.com/huggingface/datasets/issues/3404
|
albertvillanova
| 0
|
[
"enhancement"
] |
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