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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/biogpt |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ncbi_disease |
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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: biogpt-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: ncbi_disease |
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type: ncbi_disease |
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config: ncbi_disease |
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split: validation |
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args: ncbi_disease |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6861233480176211 |
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- name: Recall |
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type: recall |
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value: 0.7966751918158568 |
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- name: F1 |
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type: f1 |
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value: 0.7372781065088758 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9789725305890933 |
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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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# biogpt-finetuned-ner |
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This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1003 |
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- Precision: 0.6861 |
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- Recall: 0.7967 |
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- F1: 0.7373 |
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- Accuracy: 0.9790 |
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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: 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: 3 |
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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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| 0.058 | 1.0 | 679 | 0.0737 | 0.6191 | 0.7941 | 0.6958 | 0.9757 | |
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| 0.0473 | 2.0 | 1358 | 0.0845 | 0.6953 | 0.7762 | 0.7335 | 0.9785 | |
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| 0.0143 | 3.0 | 2037 | 0.1003 | 0.6861 | 0.7967 | 0.7373 | 0.9790 | |
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### Framework versions |
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- Transformers 4.51.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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