---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Variome_0.0001_0404_ES6_strict_tok
  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_0.0001_0404_ES6_strict_tok

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: 0.1843
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9759

## 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: 0.0001
- 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.4144        | 0.13  | 25   | 0.1849          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1834        | 0.26  | 50   | 0.1818          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1924        | 0.39  | 75   | 0.1828          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1806        | 0.52  | 100  | 0.1817          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1699        | 0.65  | 125  | 0.1863          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1783        | 0.79  | 150  | 0.1812          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1747        | 0.92  | 175  | 0.1816          | 0.0       | 0.0    | 0.0 | 0.9759   |
| 0.1583        | 1.05  | 200  | 0.1843          | 0.0       | 0.0    | 0.0 | 0.9759   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3