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--- |
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library_name: transformers |
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license: mit |
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base_model: almanach/camembertav2-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: camembertav2-base-frenchNER_3entities |
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results: [] |
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--- |
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``` |
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{'LOC': {'precision': 0.9341895320551385, |
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'recall': 0.9516260108445131, |
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'f1': 0.9428271615530316, |
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'number': 75061}, |
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'O': {'precision': 0.9953844747581743, |
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'recall': 0.9930766705362066, |
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'f1': 0.9942292334305959, |
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'number': 932066}, |
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'ORG': {'precision': 0.8804077936494026, |
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'recall': 0.8825734282116606, |
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'f1': 0.8814892808048901, |
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'number': 34149}, |
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'PER': {'precision': 0.9657491578607356, |
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'recall': 0.973339689331225, |
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'f1': 0.9695295670905427, |
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'number': 86008}, |
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'overall_precision': 0.985463290528385, |
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'overall_recall': 0.985463290528385, |
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'overall_f1': 0.985463290528385, |
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'overall_accuracy': 0.985463290528385} |
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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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should probably proofread and complete it, then remove this comment. --> |
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# camembertav2-base-frenchNER_3entities |
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This model is a fine-tuned version of [almanach/camembertav2-base](https://huggingface.co/almanach/camembertav2-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0880 |
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- Precision: 0.9859 |
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- Recall: 0.9859 |
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- F1: 0.9859 |
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- Accuracy: 0.9859 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0335 | 1.0 | 43650 | 0.0854 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | |
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| 0.0169 | 2.0 | 87300 | 0.0821 | 0.9854 | 0.9854 | 0.9854 | 0.9854 | |
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| 0.0103 | 3.0 | 130950 | 0.0880 | 0.9859 | 0.9859 | 0.9859 | 0.9859 | |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.1 |
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