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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- biobert_json |
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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: bert-base-uncased-finetuned-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: biobert_json |
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type: biobert_json |
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config: Biobert_json |
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split: validation |
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args: Biobert_json |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9437070282658518 |
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- name: Recall |
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type: recall |
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value: 0.9691575953711876 |
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- name: F1 |
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type: f1 |
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value: 0.9562630025642267 |
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- name: Accuracy |
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type: accuracy |
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value: 0.977555086732302 |
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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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# bert-base-uncased-finetuned-ner |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the biobert_json dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1107 |
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- Precision: 0.9437 |
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- Recall: 0.9692 |
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- F1: 0.9563 |
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- Accuracy: 0.9776 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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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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| 0.4381 | 1.0 | 612 | 0.1172 | 0.9235 | 0.9536 | 0.9383 | 0.9689 | |
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| 0.1389 | 2.0 | 1224 | 0.1117 | 0.9247 | 0.9731 | 0.9483 | 0.9717 | |
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| 0.0935 | 3.0 | 1836 | 0.0962 | 0.9433 | 0.9662 | 0.9546 | 0.9769 | |
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| 0.0758 | 4.0 | 2448 | 0.0926 | 0.9408 | 0.9736 | 0.9569 | 0.9771 | |
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| 0.0536 | 5.0 | 3060 | 0.0958 | 0.9404 | 0.9722 | 0.9561 | 0.9769 | |
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| 0.0476 | 6.0 | 3672 | 0.1029 | 0.9418 | 0.9681 | 0.9548 | 0.9761 | |
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| 0.0395 | 7.0 | 4284 | 0.1023 | 0.9425 | 0.9720 | 0.9570 | 0.9769 | |
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| 0.0375 | 8.0 | 4896 | 0.1091 | 0.9426 | 0.9695 | 0.9559 | 0.9771 | |
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| 0.0299 | 9.0 | 5508 | 0.1080 | 0.9451 | 0.9693 | 0.9570 | 0.9778 | |
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| 0.0266 | 10.0 | 6120 | 0.1107 | 0.9437 | 0.9692 | 0.9563 | 0.9776 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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