--- base_model: medicalai/ClinicalBERT tags: - generated_from_trainer model-index: - name: CRAFT_ClinicalBERT_NER results: [] --- # CRAFT_ClinicalBERT_NER This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1733 - Seqeval classification report: precision recall f1-score support CHEBI 0.68 0.66 0.67 1365 CL 0.55 0.50 0.52 284 GGP 0.87 0.81 0.84 4632 GO 0.66 0.65 0.65 8852 SO 0.68 0.50 0.58 616 Taxon 0.81 0.73 0.77 986 micro avg 0.72 0.69 0.71 16735 macro avg 0.71 0.64 0.67 16735 weighted avg 0.73 0.69 0.71 16735 ## 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 | 0.1894 | precision recall f1-score support CHEBI 0.64 0.56 0.60 1365 CL 0.53 0.35 0.42 284 GGP 0.84 0.77 0.81 4632 GO 0.60 0.61 0.60 8852 SO 0.53 0.46 0.49 616 Taxon 0.78 0.66 0.71 986 micro avg 0.68 0.64 0.66 16735 macro avg 0.65 0.57 0.61 16735 weighted avg 0.68 0.64 0.66 16735 | | 0.2231 | 2.0 | 695 | 0.1740 | precision recall f1-score support CHEBI 0.69 0.63 0.66 1365 CL 0.56 0.44 0.49 284 GGP 0.83 0.79 0.81 4632 GO 0.65 0.65 0.65 8852 SO 0.68 0.47 0.55 616 Taxon 0.81 0.72 0.76 986 micro avg 0.71 0.68 0.69 16735 macro avg 0.70 0.62 0.65 16735 weighted avg 0.71 0.68 0.69 16735 | | 0.0813 | 3.0 | 1041 | 0.1733 | precision recall f1-score support CHEBI 0.68 0.66 0.67 1365 CL 0.55 0.50 0.52 284 GGP 0.87 0.81 0.84 4632 GO 0.66 0.65 0.65 8852 SO 0.68 0.50 0.58 616 Taxon 0.81 0.73 0.77 986 micro avg 0.72 0.69 0.71 16735 macro avg 0.71 0.64 0.67 16735 weighted avg 0.73 0.69 0.71 16735 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0