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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: camembert-ner-finetuned-jul |
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results: [] |
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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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# camembert-ner-finetuned-jul |
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This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0716 |
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- Loc: {'precision': 0.7296511627906976, 'recall': 0.7943037974683544, 'f1': 0.7606060606060605, 'number': 316} |
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- Misc: {'precision': 0.7857142857142857, 'recall': 0.39285714285714285, 'f1': 0.5238095238095237, 'number': 56} |
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- Org: {'precision': 0.7745098039215687, 'recall': 0.7821782178217822, 'f1': 0.7783251231527093, 'number': 303} |
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- Per: {'precision': 0.8176100628930818, 'recall': 0.8074534161490683, 'f1': 0.8125000000000001, 'number': 322} |
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- Overall Precision: 0.7731 |
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- Overall Recall: 0.7723 |
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- Overall F1: 0.7727 |
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- Overall Accuracy: 0.9826 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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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 | Loc | Misc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| No log | 1.0 | 476 | 0.0740 | {'precision': 0.6106442577030813, 'recall': 0.689873417721519, 'f1': 0.6478454680534919, 'number': 316} | {'precision': 0.6666666666666666, 'recall': 0.2857142857142857, 'f1': 0.4, 'number': 56} | {'precision': 0.665680473372781, 'recall': 0.7425742574257426, 'f1': 0.7020280811232449, 'number': 303} | {'precision': 0.7469879518072289, 'recall': 0.7701863354037267, 'f1': 0.7584097859327217, 'number': 322} | 0.6727 | 0.7091 | 0.6904 | 0.9794 | |
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| 0.1185 | 2.0 | 952 | 0.0647 | {'precision': 0.7383720930232558, 'recall': 0.8037974683544303, 'f1': 0.7696969696969697, 'number': 316} | {'precision': 0.6363636363636364, 'recall': 0.375, 'f1': 0.47191011235955066, 'number': 56} | {'precision': 0.7966101694915254, 'recall': 0.7755775577557755, 'f1': 0.785953177257525, 'number': 303} | {'precision': 0.8158730158730159, 'recall': 0.7981366459627329, 'f1': 0.8069073783359498, 'number': 322} | 0.7771 | 0.7693 | 0.7732 | 0.9831 | |
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| 0.0509 | 3.0 | 1428 | 0.0716 | {'precision': 0.7296511627906976, 'recall': 0.7943037974683544, 'f1': 0.7606060606060605, 'number': 316} | {'precision': 0.7857142857142857, 'recall': 0.39285714285714285, 'f1': 0.5238095238095237, 'number': 56} | {'precision': 0.7745098039215687, 'recall': 0.7821782178217822, 'f1': 0.7783251231527093, 'number': 303} | {'precision': 0.8176100628930818, 'recall': 0.8074534161490683, 'f1': 0.8125000000000001, 'number': 322} | 0.7731 | 0.7723 | 0.7727 | 0.9826 | |
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### Framework versions |
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- Transformers 4.29.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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