---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_5e-05_250
  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_250

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: 0.0679
- Precision: 0.6097
- Recall: 0.5389
- F1: 0.5721
- Accuracy: 0.9860

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5834        | 0.35  | 25   | 0.1849          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1856        | 0.69  | 50   | 0.1791          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1611        | 1.04  | 75   | 0.1698          | 0.0       | 0.0    | 0.0    | 0.9760   |
| 0.1471        | 1.39  | 100  | 0.1219          | 0.1478    | 0.0290 | 0.0485 | 0.9764   |
| 0.1117        | 1.74  | 125  | 0.1133          | 0.1784    | 0.1426 | 0.1585 | 0.9767   |
| 0.1071        | 2.08  | 150  | 0.1030          | 0.2899    | 0.2220 | 0.2515 | 0.9789   |
| 0.0844        | 2.43  | 175  | 0.0977          | 0.3838    | 0.2750 | 0.3204 | 0.9805   |
| 0.087         | 2.78  | 200  | 0.0884          | 0.4084    | 0.3903 | 0.3991 | 0.9815   |
| 0.0785        | 3.12  | 225  | 0.0803          | 0.4895    | 0.4176 | 0.4507 | 0.9833   |
| 0.0647        | 3.47  | 250  | 0.0784          | 0.5545    | 0.4518 | 0.4979 | 0.9842   |
| 0.0592        | 3.82  | 275  | 0.0740          | 0.5655    | 0.5013 | 0.5315 | 0.9847   |
| 0.0525        | 4.17  | 300  | 0.0725          | 0.5916    | 0.5158 | 0.5511 | 0.9854   |
| 0.0515        | 4.51  | 325  | 0.0698          | 0.5861    | 0.5115 | 0.5463 | 0.9853   |
| 0.0483        | 4.86  | 350  | 0.0691          | 0.5994    | 0.5201 | 0.5569 | 0.9855   |
| 0.047         | 5.21  | 375  | 0.0702          | 0.5905    | 0.5209 | 0.5535 | 0.9855   |
| 0.0429        | 5.56  | 400  | 0.0693          | 0.5986    | 0.5286 | 0.5615 | 0.9858   |
| 0.0435        | 5.9   | 425  | 0.0673          | 0.5951    | 0.5397 | 0.5661 | 0.9858   |
| 0.0418        | 6.25  | 450  | 0.0676          | 0.5949    | 0.5329 | 0.5622 | 0.9858   |
| 0.038         | 6.6   | 475  | 0.0679          | 0.6013    | 0.5397 | 0.5689 | 0.9860   |
| 0.0355        | 6.94  | 500  | 0.0679          | 0.6097    | 0.5389 | 0.5721 | 0.9860   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2