metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: im-bin-tf-abstr
results: []
im-bin-tf-abstr
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1908
- Accuracy: 0.9222
- F1: 0.9220
- Precision: 0.9267
- Recall: 0.9174
- Roc Auc: 0.9781
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 | 375 | 0.2136 | 0.9124 | 0.9131 | 0.9087 | 0.9175 | 0.9733 |
| 0.3086 | 2.0 | 750 | 0.1971 | 0.9195 | 0.9190 | 0.9277 | 0.9104 | 0.9770 |
| 0.1917 | 3.0 | 1125 | 0.1908 | 0.9222 | 0.9220 | 0.9267 | 0.9174 | 0.9781 |
| 0.1791 | 4.0 | 1500 | 0.1909 | 0.9224 | 0.9224 | 0.9247 | 0.9202 | 0.9785 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3