--- license: mit base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext tags: - generated_from_trainer model-index: - name: JNLPBA_PubMedBERT_NER results: [] --- # JNLPBA_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: 0.1450 - Seqeval classification report: precision recall f1-score support DNA 0.75 0.83 0.79 955 RNA 0.80 0.83 0.82 1144 cell_line 0.76 0.79 0.78 5330 cell_type 0.86 0.91 0.88 2518 protein 0.87 0.85 0.86 926 micro avg 0.80 0.83 0.81 10873 macro avg 0.81 0.84 0.82 10873 weighted avg 0.80 0.83 0.81 10873 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.2726 | 1.0 | 582 | 0.1526 | precision recall f1-score support DNA 0.73 0.82 0.77 955 RNA 0.79 0.82 0.81 1144 cell_line 0.75 0.78 0.76 5330 cell_type 0.86 0.86 0.86 2518 protein 0.86 0.84 0.85 926 micro avg 0.79 0.81 0.80 10873 macro avg 0.80 0.82 0.81 10873 weighted avg 0.79 0.81 0.80 10873 | | 0.145 | 2.0 | 1164 | 0.1473 | precision recall f1-score support DNA 0.73 0.82 0.77 955 RNA 0.85 0.78 0.81 1144 cell_line 0.77 0.78 0.78 5330 cell_type 0.85 0.92 0.88 2518 protein 0.88 0.83 0.85 926 micro avg 0.80 0.82 0.81 10873 macro avg 0.81 0.83 0.82 10873 weighted avg 0.80 0.82 0.81 10873 | | 0.1276 | 3.0 | 1746 | 0.1450 | precision recall f1-score support DNA 0.75 0.83 0.79 955 RNA 0.80 0.83 0.82 1144 cell_line 0.76 0.79 0.78 5330 cell_type 0.86 0.91 0.88 2518 protein 0.87 0.85 0.86 926 micro avg 0.80 0.83 0.81 10873 macro avg 0.81 0.84 0.82 10873 weighted avg 0.80 0.83 0.81 10873 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0