In2Lab_WFU_DETECH_ate_span_pubmedbert_v1
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1159
- Precision: 0.8237
- Recall: 0.8168
- F1: 0.8202
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| No log | 1.0 | 61 | 0.1541 | 0.7421 | 0.6201 | 0.6757 |
| No log | 2.0 | 122 | 0.1262 | 0.7720 | 0.7468 | 0.7592 |
| No log | 3.0 | 183 | 0.1156 | 0.7987 | 0.7937 | 0.7962 |
| No log | 4.0 | 244 | 0.1134 | 0.8160 | 0.7958 | 0.8058 |
| No log | 5.0 | 305 | 0.1132 | 0.8276 | 0.8050 | 0.8162 |
| No log | 6.0 | 366 | 0.1119 | 0.8306 | 0.8017 | 0.8159 |
| No log | 7.0 | 427 | 0.1159 | 0.8237 | 0.8168 | 0.8202 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2