Summarization
Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use autoevaluate/summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="autoevaluate/summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("autoevaluate/summarization") model = AutoModelForSeq2SeqLM.from_pretrained("autoevaluate/summarization") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#9
by librarian-bot - opened
README.md
CHANGED
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@@ -8,20 +8,21 @@ datasets:
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- autoevaluate/xsum-sample
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metrics:
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- rouge
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model-index:
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- name: summarization
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results:
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- task:
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name: Sequence-to-sequence Language Modeling
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type: text2text-generation
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dataset:
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name: xsum
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type: xsum
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args: default
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metrics:
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type: rouge
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value: 23.9405
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- autoevaluate/xsum-sample
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metrics:
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- rouge
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base_model: t5-small
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model-index:
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- name: summarization
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results:
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- task:
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type: text2text-generation
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name: Sequence-to-sequence Language Modeling
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dataset:
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name: xsum
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type: xsum
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args: default
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metrics:
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- type: rouge
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value: 23.9405
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name: Rouge1
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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