| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - jxner | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: medicine-ner | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| dataset: | |
| name: jxner | |
| type: jxner | |
| config: wnut_17 | |
| split: test | |
| args: wnut_17 | |
| metrics: | |
| - name: Precision | |
| type: precision | |
| value: 0.0 | |
| - name: Recall | |
| type: recall | |
| value: 0.0 | |
| - name: F1 | |
| type: f1 | |
| value: 0.0 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.859375 | |
| <!-- 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. --> | |
| # medicine-ner | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the jxner dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7996 | |
| - Precision: 0.0 | |
| - Recall: 0.0 | |
| - F1: 0.0 | |
| - Accuracy: 0.8594 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | |
| | No log | 1.0 | 1 | 0.8644 | 0.0 | 0.0 | 0.0 | 0.8594 | | |
| | No log | 2.0 | 2 | 0.7996 | 0.0 | 0.0 | 0.0 | 0.8594 | | |
| ### Framework versions | |
| - Transformers 4.27.3 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 2.10.1 | |
| - Tokenizers 0.13.2 | |