metadata
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: test
results: []
test
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: 0.4018
- Accuracy: 0.8207
- Precision: 0.8202
- Recall: 0.8207
- 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4749 | 1.0 | 417 | 0.4018 | 0.8207 | 0.8202 | 0.8207 | 0.8202 |
| 0.0976 | 2.0 | 834 | 0.4443 | 0.8189 | 0.8234 | 0.8189 | 0.8197 |
| 0.0061 | 3.0 | 1251 | 0.7378 | 0.8213 | 0.8233 | 0.8213 | 0.8219 |
| 0.3159 | 4.0 | 1668 | 0.9154 | 0.8094 | 0.8092 | 0.8094 | 0.8092 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0