---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_5e-05_29_03
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Variome_5e-05_29_03

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1054
- Precision: 0.5304
- Recall: 0.4586
- F1: 0.4919
- Accuracy: 0.9843

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5733        | 5.0   | 25   | 0.1581          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1685        | 10.0  | 50   | 0.1553          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1691        | 15.0  | 75   | 0.1547          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1606        | 20.0  | 100  | 0.1335          | 0.0       | 0.0    | 0.0    | 0.9794   |
| 0.1144        | 25.0  | 125  | 0.1044          | 0.2151    | 0.1504 | 0.1770 | 0.9802   |
| 0.0795        | 30.0  | 150  | 0.1023          | 0.2       | 0.1504 | 0.1717 | 0.9804   |
| 0.0612        | 35.0  | 175  | 0.1102          | 0.4118    | 0.2105 | 0.2786 | 0.9831   |
| 0.0465        | 40.0  | 200  | 0.0991          | 0.4158    | 0.3158 | 0.3590 | 0.9840   |
| 0.0352        | 45.0  | 225  | 0.0995          | 0.4653    | 0.3534 | 0.4017 | 0.9838   |
| 0.0281        | 50.0  | 250  | 0.0969          | 0.4685    | 0.3910 | 0.4262 | 0.9838   |
| 0.0223        | 55.0  | 275  | 0.0976          | 0.5684    | 0.4060 | 0.4737 | 0.9853   |
| 0.0183        | 60.0  | 300  | 0.0992          | 0.5093    | 0.4135 | 0.4564 | 0.9848   |
| 0.0154        | 65.0  | 325  | 0.0996          | 0.5816    | 0.4286 | 0.4935 | 0.9858   |
| 0.0131        | 70.0  | 350  | 0.1007          | 0.5221    | 0.4436 | 0.4797 | 0.9842   |
| 0.0109        | 75.0  | 375  | 0.1023          | 0.5130    | 0.4436 | 0.4758 | 0.9842   |
| 0.0094        | 80.0  | 400  | 0.1037          | 0.5566    | 0.4436 | 0.4937 | 0.9851   |
| 0.0085        | 85.0  | 425  | 0.1054          | 0.5304    | 0.4586 | 0.4919 | 0.9843   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2