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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: michiyasunaga/BioLinkBERT-base |
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tags: |
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- token-classification |
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- generated_from_trainer |
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datasets: |
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- Rodrigo1771/drugtemist-en-9-ner |
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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: output |
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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: Rodrigo1771/drugtemist-en-9-ner |
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type: Rodrigo1771/drugtemist-en-9-ner |
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config: DrugTEMIST English NER |
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split: validation |
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args: DrugTEMIST English NER |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9297597042513863 |
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- name: Recall |
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type: recall |
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value: 0.9375582479030755 |
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- name: F1 |
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type: f1 |
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value: 0.9336426914153132 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9987999888371054 |
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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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# output |
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-9-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0046 |
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- Precision: 0.9298 |
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- Recall: 0.9376 |
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- F1: 0.9336 |
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- Accuracy: 0.9988 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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: 10.0 |
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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 | 437 | 0.0047 | 0.8995 | 0.9254 | 0.9123 | 0.9985 | |
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| 0.0144 | 2.0 | 874 | 0.0053 | 0.8960 | 0.9310 | 0.9132 | 0.9985 | |
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| 0.0038 | 3.0 | 1311 | 0.0046 | 0.9298 | 0.9376 | 0.9336 | 0.9988 | |
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| 0.0022 | 4.0 | 1748 | 0.0055 | 0.9202 | 0.9245 | 0.9224 | 0.9986 | |
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| 0.0019 | 5.0 | 2185 | 0.0053 | 0.9118 | 0.9348 | 0.9231 | 0.9986 | |
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| 0.0014 | 6.0 | 2622 | 0.0054 | 0.9194 | 0.9254 | 0.9224 | 0.9986 | |
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| 0.0009 | 7.0 | 3059 | 0.0073 | 0.9324 | 0.9254 | 0.9289 | 0.9986 | |
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| 0.0009 | 8.0 | 3496 | 0.0065 | 0.9341 | 0.9254 | 0.9298 | 0.9987 | |
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| 0.0005 | 9.0 | 3933 | 0.0069 | 0.9326 | 0.9292 | 0.9309 | 0.9987 | |
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| 0.0004 | 10.0 | 4370 | 0.0071 | 0.9249 | 0.9292 | 0.9270 | 0.9987 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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