metadata
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: mult_tf
results: []
mult_tf
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5180
- Accuracy: 0.8364
- F1: 0.8358
- Precision: 0.8355
- Recall: 0.8364
- Roc Auc: 0.9896
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: 1e-05
- train_batch_size: 640
- eval_batch_size: 1280
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 357 | 0.5694 | 0.8249 | 0.8243 | 0.8245 | 0.8249 | 0.9875 |
| 0.5397 | 2.0 | 714 | 0.5324 | 0.8324 | 0.8312 | 0.8313 | 0.8324 | 0.9890 |
| 0.523 | 3.0 | 1071 | 0.5193 | 0.8354 | 0.8348 | 0.8346 | 0.8354 | 0.9895 |
| 0.523 | 4.0 | 1428 | 0.5180 | 0.8364 | 0.8358 | 0.8355 | 0.8364 | 0.9896 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3