--- base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer model-index: - name: JNLPBA_bioBERT_NER results: [] --- # JNLPBA_bioBERT_NER This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1433 - Seqeval classification report: precision recall f1-score support DNA 0.81 0.83 0.82 1593 RNA 0.79 0.83 0.81 1400 cell_line 0.77 0.82 0.79 1016 cell_type 0.98 0.96 0.97 37439 protein 0.85 0.86 0.85 2992 micro avg 0.95 0.94 0.95 44440 macro avg 0.84 0.86 0.85 44440 weighted avg 0.95 0.94 0.95 44440 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Seqeval classification report | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.2673 | 1.0 | 582 | 0.1492 | precision recall f1-score support DNA 0.79 0.81 0.80 1593 RNA 0.78 0.79 0.79 1400 cell_line 0.74 0.84 0.79 1016 cell_type 0.98 0.96 0.97 37439 protein 0.84 0.86 0.85 2992 micro avg 0.95 0.94 0.94 44440 macro avg 0.83 0.85 0.84 44440 weighted avg 0.95 0.94 0.94 44440 | | 0.1408 | 2.0 | 1164 | 0.1469 | precision recall f1-score support DNA 0.83 0.80 0.81 1593 RNA 0.78 0.83 0.80 1400 cell_line 0.75 0.83 0.79 1016 cell_type 0.98 0.96 0.97 37439 protein 0.84 0.87 0.85 2992 micro avg 0.95 0.94 0.95 44440 macro avg 0.84 0.86 0.85 44440 weighted avg 0.95 0.94 0.95 44440 | | 0.1237 | 3.0 | 1746 | 0.1433 | precision recall f1-score support DNA 0.81 0.83 0.82 1593 RNA 0.79 0.83 0.81 1400 cell_line 0.77 0.82 0.79 1016 cell_type 0.98 0.96 0.97 37439 protein 0.85 0.86 0.85 2992 micro avg 0.95 0.94 0.95 44440 macro avg 0.84 0.86 0.85 44440 weighted avg 0.95 0.94 0.95 44440 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0