--- base_model: allenai/scibert_scivocab_uncased tags: - generated_from_trainer model-index: - name: JNLPBA_SciBERT_NER results: [] --- # JNLPBA_SciBERT_NER This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1472 - Seqeval classification report: precision recall f1-score support DNA 0.83 0.89 0.86 2106 RNA 0.88 0.89 0.88 3516 cell_line 0.74 0.80 0.77 526 cell_type 0.78 0.83 0.80 1475 protein 0.98 0.97 0.98 37428 micro avg 0.96 0.96 0.96 45051 macro avg 0.84 0.87 0.86 45051 weighted avg 0.96 0.96 0.96 45051 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.233 | 1.0 | 582 | 0.1513 | precision recall f1-score support DNA 0.82 0.89 0.85 2106 RNA 0.87 0.89 0.88 3516 cell_line 0.72 0.79 0.76 526 cell_type 0.79 0.78 0.79 1475 protein 0.98 0.97 0.98 37428 micro avg 0.95 0.95 0.95 45051 macro avg 0.84 0.87 0.85 45051 weighted avg 0.95 0.95 0.95 45051 | | 0.138 | 2.0 | 1164 | 0.1486 | precision recall f1-score support DNA 0.85 0.85 0.85 2106 RNA 0.89 0.87 0.88 3516 cell_line 0.71 0.80 0.75 526 cell_type 0.77 0.82 0.79 1475 protein 0.98 0.97 0.98 37428 micro avg 0.96 0.95 0.95 45051 macro avg 0.84 0.86 0.85 45051 weighted avg 0.96 0.95 0.96 45051 | | 0.1191 | 3.0 | 1746 | 0.1472 | precision recall f1-score support DNA 0.83 0.89 0.86 2106 RNA 0.88 0.89 0.88 3516 cell_line 0.74 0.80 0.77 526 cell_type 0.78 0.83 0.80 1475 protein 0.98 0.97 0.98 37428 micro avg 0.96 0.96 0.96 45051 macro avg 0.84 0.87 0.86 45051 weighted avg 0.96 0.96 0.96 45051 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0