--- license: mit base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext tags: - generated_from_trainer model-index: - name: CRAFT_PubMedBERT_NER results: [] --- # CRAFT_PubMedBERT_NER This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1043 - Seqeval classification report: precision recall f1-score support CHEBI 0.71 0.73 0.72 616 CL 0.85 0.89 0.87 1740 GGP 0.84 0.76 0.80 611 GO 0.89 0.90 0.90 3810 SO 0.81 0.83 0.82 8854 Taxon 0.58 0.60 0.59 284 micro avg 0.82 0.84 0.83 15915 macro avg 0.78 0.79 0.78 15915 weighted avg 0.82 0.84 0.83 15915 ## 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 | 0.1260 | precision recall f1-score support CHEBI 0.66 0.61 0.63 616 CL 0.81 0.86 0.83 1740 GGP 0.74 0.54 0.63 611 GO 0.86 0.89 0.87 3810 SO 0.73 0.78 0.76 8854 Taxon 0.47 0.57 0.52 284 micro avg 0.76 0.80 0.78 15915 macro avg 0.71 0.71 0.71 15915 weighted avg 0.76 0.80 0.78 15915 | | 0.182 | 2.0 | 695 | 0.1089 | precision recall f1-score support CHEBI 0.69 0.74 0.71 616 CL 0.84 0.88 0.86 1740 GGP 0.83 0.74 0.78 611 GO 0.88 0.90 0.89 3810 SO 0.79 0.82 0.81 8854 Taxon 0.57 0.60 0.58 284 micro avg 0.81 0.84 0.82 15915 macro avg 0.77 0.78 0.77 15915 weighted avg 0.81 0.84 0.82 15915 | | 0.0443 | 3.0 | 1041 | 0.1043 | precision recall f1-score support CHEBI 0.71 0.73 0.72 616 CL 0.85 0.89 0.87 1740 GGP 0.84 0.76 0.80 611 GO 0.89 0.90 0.90 3810 SO 0.81 0.83 0.82 8854 Taxon 0.58 0.60 0.59 284 micro avg 0.82 0.84 0.83 15915 macro avg 0.78 0.79 0.78 15915 weighted avg 0.82 0.84 0.83 15915 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0