--- license: mit base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext tags: - generated_from_trainer model-index: - name: BC5CDR_PubMedBERT_NER results: [] --- # BC5CDR_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: 0.0783 - Seqeval classification report: precision recall f1-score support Chemical 0.99 0.98 0.98 103336 Disease 0.76 0.86 0.81 3447 micro avg 0.98 0.98 0.98 106783 macro avg 0.87 0.92 0.89 106783 weighted avg 0.98 0.98 0.98 106783 ## 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 | 143 | 0.0952 | precision recall f1-score support Chemical 0.99 0.97 0.98 103336 Disease 0.68 0.88 0.76 3447 micro avg 0.97 0.97 0.97 106783 macro avg 0.83 0.92 0.87 106783 weighted avg 0.98 0.97 0.97 106783 | | No log | 2.0 | 286 | 0.0804 | precision recall f1-score support Chemical 0.99 0.98 0.98 103336 Disease 0.75 0.86 0.80 3447 micro avg 0.98 0.97 0.97 106783 macro avg 0.87 0.92 0.89 106783 weighted avg 0.98 0.97 0.98 106783 | | No log | 3.0 | 429 | 0.0783 | precision recall f1-score support Chemical 0.99 0.98 0.98 103336 Disease 0.76 0.86 0.81 3447 micro avg 0.98 0.98 0.98 106783 macro avg 0.87 0.92 0.89 106783 weighted avg 0.98 0.98 0.98 106783 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0