metadata
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ncbi
results: []
bert-finetuned-ncbi
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0584
- Precision: 0.8277
- Recall: 0.8729
- F1: 0.8497
- Accuracy: 0.9859
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: 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1091 | 1.0 | 680 | 0.0479 | 0.7906 | 0.8539 | 0.8210 | 0.9836 |
| 0.0338 | 2.0 | 1360 | 0.0484 | 0.7998 | 0.8679 | 0.8324 | 0.9852 |
| 0.0128 | 3.0 | 2040 | 0.0584 | 0.8277 | 0.8729 | 0.8497 | 0.9859 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2