--- base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer model-index: - name: CRAFT_bioBERT_NER results: [] --- # CRAFT_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: 0.1106 - Seqeval classification report: precision recall f1-score support CHEBI 0.83 0.76 0.80 1109 CL 0.91 0.90 0.90 3871 GGP 0.76 0.66 0.71 600 GO 0.87 0.84 0.85 1061 SO 0.99 0.99 0.99 87954 Taxon 0.83 0.87 0.85 3104 micro avg 0.98 0.97 0.97 97699 macro avg 0.87 0.84 0.85 97699 weighted avg 0.98 0.97 0.97 97699 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 347 | 0.1141 | precision recall f1-score support CHEBI 0.82 0.65 0.72 1109 CL 0.90 0.87 0.89 3871 GGP 0.75 0.62 0.68 600 GO 0.88 0.77 0.82 1061 SO 0.99 0.99 0.99 87954 Taxon 0.79 0.88 0.83 3104 micro avg 0.97 0.97 0.97 97699 macro avg 0.86 0.80 0.82 97699 weighted avg 0.97 0.97 0.97 97699 | | 0.1705 | 2.0 | 695 | 0.1121 | precision recall f1-score support CHEBI 0.86 0.73 0.79 1109 CL 0.90 0.90 0.90 3871 GGP 0.73 0.65 0.69 600 GO 0.87 0.82 0.85 1061 SO 0.99 0.99 0.99 87954 Taxon 0.79 0.89 0.84 3104 micro avg 0.97 0.97 0.97 97699 macro avg 0.86 0.83 0.84 97699 weighted avg 0.97 0.97 0.97 97699 | | 0.04 | 3.0 | 1041 | 0.1106 | precision recall f1-score support CHEBI 0.83 0.76 0.80 1109 CL 0.91 0.90 0.90 3871 GGP 0.76 0.66 0.71 600 GO 0.87 0.84 0.85 1061 SO 0.99 0.99 0.99 87954 Taxon 0.83 0.87 0.85 3104 micro avg 0.98 0.97 0.97 97699 macro avg 0.87 0.84 0.85 97699 weighted avg 0.98 0.97 0.97 97699 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0