metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-paper-classifier
results: []
bert-paper-classifier
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: 1.8059
- Accuracy: 0.586
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: 5e-05
- train_batch_size: 64
- 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 | Accuracy |
|---|---|---|---|---|
| 1.8012 | 1.0 | 16 | 1.8824 | 0.584 |
| 1.4111 | 2.0 | 32 | 1.8159 | 0.568 |
| 1.3755 | 3.0 | 48 | 1.8059 | 0.586 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3