Instructions to use AIWizards/MultiPRIDE-DualEncoder-LPFT-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIWizards/MultiPRIDE-DualEncoder-LPFT-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIWizards/MultiPRIDE-DualEncoder-LPFT-it")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIWizards/MultiPRIDE-DualEncoder-LPFT-it") model = AutoModelForSequenceClassification.from_pretrained("AIWizards/MultiPRIDE-DualEncoder-LPFT-it") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: nickprock/setfit-italian-hate-speech | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| - precision | |
| - recall | |
| model-index: | |
| - name: MultiPRIDE-DualEncoder-LPFT-it | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # MultiPRIDE-DualEncoder-LPFT-it | |
| This model is a fine-tuned version of [nickprock/setfit-italian-hate-speech](https://huggingface.co/nickprock/setfit-italian-hate-speech) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0105 | |
| - Accuracy: 0.9693 | |
| - F1: 0.9180 | |
| - Precision: 0.9333 | |
| - Recall: 0.9032 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 1337 | |
| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | |
| | 0.1597 | 1.0 | 95 | 0.1206 | 0.8528 | 0.5385 | 0.6667 | 0.4516 | | |
| | 0.1218 | 2.0 | 190 | 0.0621 | 0.8834 | 0.7077 | 0.6765 | 0.7419 | | |
| | 0.0744 | 3.0 | 285 | 0.0270 | 0.9387 | 0.8438 | 0.8182 | 0.8710 | | |
| | 0.0431 | 4.0 | 380 | 0.0167 | 0.9632 | 0.9032 | 0.9032 | 0.9032 | | |
| | 0.0393 | 5.0 | 475 | 0.0128 | 0.9571 | 0.8889 | 0.875 | 0.9032 | | |
| | 0.0223 | 6.0 | 570 | 0.0116 | 0.9693 | 0.9180 | 0.9333 | 0.9032 | | |
| | 0.0432 | 7.0 | 665 | 0.0108 | 0.9693 | 0.9180 | 0.9333 | 0.9032 | | |
| | 0.0348 | 8.0 | 760 | 0.0105 | 0.9693 | 0.9180 | 0.9333 | 0.9032 | | |
| | 0.035 | 9.0 | 855 | 0.0105 | 0.9693 | 0.9180 | 0.9333 | 0.9032 | | |
| ### Framework versions | |
| - Transformers 4.57.3 | |
| - Pytorch 2.9.1+cu128 | |
| - Datasets 4.4.1 | |
| - Tokenizers 0.22.1 | |