| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: microsoft/biogpt |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - ncbi_disease |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: bert-finetuned-ner |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: ncbi_disease |
| | type: ncbi_disease |
| | config: ncbi_disease |
| | split: validation |
| | args: ncbi_disease |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.08217270194986072 |
| | - name: Recall |
| | type: recall |
| | value: 0.07496823379923762 |
| | - name: F1 |
| | type: f1 |
| | value: 0.07840531561461794 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9369870473375083 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # bert-finetuned-ner |
| |
|
| | This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2151 |
| | - Precision: 0.0822 |
| | - Recall: 0.0750 |
| | - F1: 0.0784 |
| | - Accuracy: 0.9370 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 2e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.3388 | 1.0 | 679 | 0.2280 | 0.0292 | 0.0254 | 0.0272 | 0.9312 | |
| | | 0.2425 | 2.0 | 1358 | 0.2161 | 0.0612 | 0.0572 | 0.0591 | 0.9345 | |
| | | 0.1811 | 3.0 | 2037 | 0.2151 | 0.0822 | 0.0750 | 0.0784 | 0.9370 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.51.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 3.5.0 |
| | - Tokenizers 0.21.1 |
| | |
