metadata
library_name: transformers
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biomedical-ner-all
results: []
biomedical-ner-all
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2652
- Precision: 0.5986
- Recall: 0.6849
- F1: 0.6388
- Accuracy: 0.9246
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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.2443 | 0.5849 | 0.6930 | 0.6344 | 0.9231 |
| No log | 2.0 | 250 | 0.2578 | 0.6041 | 0.6849 | 0.6420 | 0.9252 |
| No log | 3.0 | 375 | 0.2652 | 0.5986 | 0.6849 | 0.6388 | 0.9246 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0