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Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC

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+ ---
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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: Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC
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+ results: []
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+ ---
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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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+
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+ # Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE-Aug-NoEWC
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+
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+ This model is a fine-tuned version of [StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE2-NoEWC](https://huggingface.co/StivenLancheros/Biobert-base-cased-v1.2-finetuned-ner-CRAFT-EN-FR-ES-IT-DE2-NoEWC) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4245
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+ - Precision: 0.7158
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+ - Recall: 0.7763
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+ - F1: 0.7448
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+ - Accuracy: 0.9230
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3e-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: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.4287 | 1.0 | 1360 | 0.4776 | 0.6125 | 0.7809 | 0.6865 | 0.8944 |
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+ | 0.274 | 2.0 | 2720 | 0.4727 | 0.6370 | 0.7705 | 0.6974 | 0.9026 |
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+ | 0.2219 | 3.0 | 4080 | 0.4405 | 0.6790 | 0.7695 | 0.7215 | 0.9134 |
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+ | 0.1964 | 4.0 | 5440 | 0.4245 | 0.7158 | 0.7763 | 0.7448 | 0.9230 |
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+ | 0.1599 | 5.0 | 6800 | 0.4378 | 0.7029 | 0.7732 | 0.7364 | 0.9204 |
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+ | 0.1489 | 6.0 | 8160 | 0.4324 | 0.7018 | 0.7749 | 0.7365 | 0.9196 |
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+ | 0.1342 | 7.0 | 9520 | 0.4534 | 0.7131 | 0.7867 | 0.7481 | 0.9216 |
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+ | 0.12 | 8.0 | 10880 | 0.4532 | 0.7230 | 0.7865 | 0.7534 | 0.9243 |
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+ | 0.1135 | 9.0 | 12240 | 0.4688 | 0.7155 | 0.7705 | 0.7420 | 0.9206 |
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+ | 0.107 | 10.0 | 13600 | 0.4713 | 0.7214 | 0.7863 | 0.7524 | 0.9241 |
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+ | 0.1004 | 11.0 | 14960 | 0.4594 | 0.7278 | 0.7823 | 0.7541 | 0.9251 |
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+ | 0.093 | 12.0 | 16320 | 0.4564 | 0.7343 | 0.7937 | 0.7628 | 0.9275 |
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+ | 0.0862 | 13.0 | 17680 | 0.4699 | 0.7242 | 0.7970 | 0.7588 | 0.9262 |
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+ | 0.0788 | 14.0 | 19040 | 0.4745 | 0.7397 | 0.7935 | 0.7657 | 0.9285 |
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+ | 0.0785 | 15.0 | 20400 | 0.4822 | 0.7389 | 0.7998 | 0.7682 | 0.9283 |
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+ | 0.0711 | 16.0 | 21760 | 0.5031 | 0.7255 | 0.7964 | 0.7593 | 0.9264 |
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+ | 0.073 | 17.0 | 23120 | 0.5038 | 0.7317 | 0.7976 | 0.7632 | 0.9274 |
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+ | 0.0695 | 18.0 | 24480 | 0.4860 | 0.7411 | 0.8016 | 0.7702 | 0.9296 |
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+ | 0.0649 | 19.0 | 25840 | 0.4978 | 0.7376 | 0.7977 | 0.7665 | 0.9285 |
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+ | 0.0605 | 20.0 | 27200 | 0.4983 | 0.7371 | 0.7974 | 0.7661 | 0.9286 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.2
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+ - Pytorch 1.13.0+cu117
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.2