---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_2e-05_ES2
  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. -->

# tmvar_2e-05_ES2

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: 0.0184
- Precision: 0.8368
- Recall: 0.8595
- F1: 0.848
- Accuracy: 0.9962

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5018        | 1.47  | 25   | 0.1002          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0852        | 2.94  | 50   | 0.0509          | 0.9286    | 0.0703 | 0.1307 | 0.9852   |
| 0.0373        | 4.41  | 75   | 0.0283          | 0.5485    | 0.6108 | 0.5780 | 0.9918   |
| 0.0256        | 5.88  | 100  | 0.0204          | 0.6429    | 0.7297 | 0.6835 | 0.9938   |
| 0.0123        | 7.35  | 125  | 0.0188          | 0.8063    | 0.8324 | 0.8191 | 0.9956   |
| 0.008         | 8.82  | 150  | 0.0171          | 0.7979    | 0.8324 | 0.8148 | 0.9958   |
| 0.0047        | 10.29 | 175  | 0.0158          | 0.8010    | 0.8919 | 0.8440 | 0.9962   |
| 0.0037        | 11.76 | 200  | 0.0171          | 0.8511    | 0.8649 | 0.8579 | 0.9964   |
| 0.0025        | 13.24 | 225  | 0.0184          | 0.8368    | 0.8595 | 0.848  | 0.9962   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2