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--- |
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base_model: dmis-lab/biobert-v1.1 |
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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-finetuned-ner |
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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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# biobert-finetuned-ner |
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6965 |
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- Precision: 0.6381 |
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- Recall: 0.6865 |
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- F1: 0.6614 |
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- Accuracy: 0.8583 |
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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: 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: 10 |
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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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| No log | 1.0 | 305 | 0.4123 | 0.6110 | 0.6694 | 0.6389 | 0.8542 | |
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| 0.4534 | 2.0 | 610 | 0.4023 | 0.6259 | 0.6848 | 0.6540 | 0.8586 | |
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| 0.4534 | 3.0 | 915 | 0.4384 | 0.6369 | 0.6991 | 0.6666 | 0.8615 | |
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| 0.2438 | 4.0 | 1220 | 0.4799 | 0.6445 | 0.6941 | 0.6684 | 0.8615 | |
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| 0.1551 | 5.0 | 1525 | 0.5190 | 0.6464 | 0.6908 | 0.6678 | 0.8628 | |
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| 0.1551 | 6.0 | 1830 | 0.5772 | 0.6454 | 0.6751 | 0.6599 | 0.8597 | |
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| 0.1044 | 7.0 | 2135 | 0.6141 | 0.6413 | 0.6881 | 0.6639 | 0.8586 | |
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| 0.1044 | 8.0 | 2440 | 0.6587 | 0.6353 | 0.6945 | 0.6636 | 0.8590 | |
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| 0.0755 | 9.0 | 2745 | 0.6856 | 0.6357 | 0.6905 | 0.6620 | 0.8580 | |
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| 0.0604 | 10.0 | 3050 | 0.6965 | 0.6381 | 0.6865 | 0.6614 | 0.8583 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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