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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: 0.1879 |
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- Er: {'precision': 0.6520963425512935, 'recall': 0.7567287784679089, 'f1': 0.7005270723526592, 'number': 966} |
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- Isc: {'precision': 0.6759708737864077, 'recall': 0.6975579211020664, 'f1': 0.6865947611710324, 'number': 1597} |
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- Oc: {'precision': 0.6144200626959248, 'recall': 0.5714285714285714, 'f1': 0.5921450151057402, 'number': 686} |
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- Overall Precision: 0.6566 |
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- Overall Recall: 0.6885 |
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- Overall F1: 0.6722 |
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- Overall Accuracy: 0.9400 |
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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 | Er | Isc | Oc | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.2687 | 1.0 | 654 | 0.2022 | {'precision': 0.6098294884653962, 'recall': 0.629399585921325, 'f1': 0.6194600101884871, 'number': 966} | {'precision': 0.6234604105571847, 'recall': 0.6656230432060113, 'f1': 0.6438522107813446, 'number': 1597} | {'precision': 0.5617792421746294, 'recall': 0.4970845481049563, 'f1': 0.5274555297757154, 'number': 686} | 0.6080 | 0.6193 | 0.6136 | 0.9325 | |
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| 0.1623 | 2.0 | 1308 | 0.1819 | {'precision': 0.6175523349436393, 'recall': 0.7939958592132506, 'f1': 0.6947463768115941, 'number': 966} | {'precision': 0.6879526003949967, 'recall': 0.654351909830933, 'f1': 0.6707317073170731, 'number': 1597} | {'precision': 0.6374367622259697, 'recall': 0.5510204081632653, 'f1': 0.5910867865519938, 'number': 686} | 0.6530 | 0.6741 | 0.6633 | 0.9390 | |
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| 0.128 | 3.0 | 1962 | 0.1879 | {'precision': 0.6520963425512935, 'recall': 0.7567287784679089, 'f1': 0.7005270723526592, 'number': 966} | {'precision': 0.6759708737864077, 'recall': 0.6975579211020664, 'f1': 0.6865947611710324, 'number': 1597} | {'precision': 0.6144200626959248, 'recall': 0.5714285714285714, 'f1': 0.5921450151057402, 'number': 686} | 0.6566 | 0.6885 | 0.6722 | 0.9400 | |
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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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