metadata
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_2e-05_29_03
results: []
Variome_2e-05_29_03
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: 0.0928
- Precision: 0.5437
- Recall: 0.4211
- F1: 0.4746
- Accuracy: 0.9852
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
1.1552 | 5.0 | 25 | 0.1636 | 0.0 | 0.0 | 0.0 | 0.9794 |
0.1671 | 10.0 | 50 | 0.1345 | 0.0 | 0.0 | 0.0 | 0.9794 |
0.1297 | 15.0 | 75 | 0.1076 | 0.2683 | 0.0827 | 0.1264 | 0.9806 |
0.0995 | 20.0 | 100 | 0.1047 | 0.24 | 0.1353 | 0.1731 | 0.9810 |
0.0845 | 25.0 | 125 | 0.0987 | 0.2289 | 0.1429 | 0.1759 | 0.9813 |
0.0722 | 30.0 | 150 | 0.1001 | 0.2558 | 0.1654 | 0.2009 | 0.9816 |
0.0642 | 35.0 | 175 | 0.0994 | 0.3117 | 0.1805 | 0.2286 | 0.9821 |
0.0564 | 40.0 | 200 | 0.0938 | 0.3204 | 0.2481 | 0.2797 | 0.9817 |
0.0481 | 45.0 | 225 | 0.0935 | 0.4070 | 0.2632 | 0.3196 | 0.9833 |
0.0416 | 50.0 | 250 | 0.0913 | 0.4167 | 0.3383 | 0.3734 | 0.9836 |
0.0363 | 55.0 | 275 | 0.0911 | 0.4653 | 0.3534 | 0.4017 | 0.9847 |
0.0321 | 60.0 | 300 | 0.0909 | 0.4495 | 0.3684 | 0.4050 | 0.9842 |
0.0293 | 65.0 | 325 | 0.0918 | 0.5361 | 0.3910 | 0.4522 | 0.9852 |
0.0269 | 70.0 | 350 | 0.0936 | 0.5444 | 0.3684 | 0.4395 | 0.9853 |
0.0251 | 75.0 | 375 | 0.0936 | 0.5833 | 0.4211 | 0.4891 | 0.9858 |
0.0242 | 80.0 | 400 | 0.0920 | 0.5534 | 0.4286 | 0.4831 | 0.9854 |
0.0232 | 85.0 | 425 | 0.0928 | 0.5612 | 0.4135 | 0.4762 | 0.9855 |
0.0216 | 90.0 | 450 | 0.0928 | 0.5437 | 0.4211 | 0.4746 | 0.9852 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2