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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: 0.1522 |
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- Loc: {'precision': 0.7952488687782805, 'recall': 0.703, 'f1': 0.7462845010615711, 'number': 1000} |
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- Misc: {'precision': 0.6310931641188348, 'recall': 0.6640364188163884, 'f1': 0.6471458148476782, 'number': 3295} |
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- Org: {'precision': 0.6708074534161491, 'recall': 0.6792452830188679, 'f1': 0.6749999999999999, 'number': 477} |
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- Per: {'precision': 0.7778738115816768, 'recall': 0.7772020725388601, 'f1': 0.7775377969762419, 'number': 1158} |
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- Overall Precision: 0.6869 |
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- Overall Recall: 0.6939 |
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- Overall F1: 0.6904 |
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- Overall Accuracy: 0.9567 |
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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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| 0.1195 | 1.0 | 2044 | 0.1425 | {'precision': 0.750620347394541, 'recall': 0.605, 'f1': 0.6699889258028793, 'number': 1000} | {'precision': 0.6498784300104203, 'recall': 0.5678300455235205, 'f1': 0.606090055069647, 'number': 3295} | {'precision': 0.6763392857142857, 'recall': 0.6352201257861635, 'f1': 0.6551351351351351, 'number': 477} | {'precision': 0.6595744680851063, 'recall': 0.7763385146804835, 'f1': 0.7132090440301467, 'number': 1158} | 0.6692 | 0.6202 | 0.6438 | 0.9511 | |
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| 0.0736 | 2.0 | 4088 | 0.1387 | {'precision': 0.7714604236343366, 'recall': 0.692, 'f1': 0.7295730100158145, 'number': 1000} | {'precision': 0.6479814115596864, 'recall': 0.6770864946889226, 'f1': 0.6622143069159989, 'number': 3295} | {'precision': 0.7018348623853211, 'recall': 0.6415094339622641, 'f1': 0.6703176341730558, 'number': 477} | {'precision': 0.7717484926787253, 'recall': 0.7737478411053541, 'f1': 0.7727468736524364, 'number': 1158} | 0.6948 | 0.6956 | 0.6952 | 0.9575 | |
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| 0.0499 | 3.0 | 6132 | 0.1522 | {'precision': 0.7952488687782805, 'recall': 0.703, 'f1': 0.7462845010615711, 'number': 1000} | {'precision': 0.6310931641188348, 'recall': 0.6640364188163884, 'f1': 0.6471458148476782, 'number': 3295} | {'precision': 0.6708074534161491, 'recall': 0.6792452830188679, 'f1': 0.6749999999999999, 'number': 477} | {'precision': 0.7778738115816768, 'recall': 0.7772020725388601, 'f1': 0.7775377969762419, 'number': 1158} | 0.6869 | 0.6939 | 0.6904 | 0.9567 | |
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### Framework versions |
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- Transformers 4.28.1 |
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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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