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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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- generated_from_trainer |
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datasets: |
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- drugtemist-en-75-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: drugtemist-en-75-ner |
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type: drugtemist-en-75-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: 0.921028466483012 |
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- name: Recall |
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type: recall |
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value: 0.934762348555452 |
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- name: F1 |
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type: f1 |
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value: 0.9278445883441258 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9986883598917199 |
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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 drugtemist-en-75-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0083 |
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- Precision: 0.9210 |
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- Recall: 0.9348 |
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- F1: 0.9278 |
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- Accuracy: 0.9987 |
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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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| 0.0189 | 1.0 | 504 | 0.0052 | 0.8712 | 0.9394 | 0.9040 | 0.9984 | |
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| 0.0047 | 2.0 | 1008 | 0.0048 | 0.9253 | 0.9236 | 0.9244 | 0.9987 | |
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| 0.0027 | 3.0 | 1512 | 0.0059 | 0.9252 | 0.9226 | 0.9239 | 0.9986 | |
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| 0.0015 | 4.0 | 2016 | 0.0065 | 0.9342 | 0.9264 | 0.9303 | 0.9987 | |
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| 0.0011 | 5.0 | 2520 | 0.0073 | 0.9073 | 0.9394 | 0.9231 | 0.9986 | |
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| 0.0005 | 6.0 | 3024 | 0.0090 | 0.9191 | 0.9217 | 0.9204 | 0.9984 | |
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| 0.0007 | 7.0 | 3528 | 0.0084 | 0.9074 | 0.9310 | 0.9190 | 0.9986 | |
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| 0.0004 | 8.0 | 4032 | 0.0085 | 0.9093 | 0.9338 | 0.9214 | 0.9986 | |
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| 0.0003 | 9.0 | 4536 | 0.0080 | 0.9186 | 0.9357 | 0.9271 | 0.9987 | |
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| 0.0002 | 10.0 | 5040 | 0.0083 | 0.9210 | 0.9348 | 0.9278 | 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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