---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tmvar_5e-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_5e-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: 0.0189
- Precision: 0.8469
- Recall: 0.8973
- F1: 0.8714
- Accuracy: 0.9971

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3852        | 1.47  | 25   | 0.1019          | 0.0       | 0.0    | 0.0    | 0.9843   |
| 0.0775        | 2.94  | 50   | 0.0398          | 0.2812    | 0.3892 | 0.3265 | 0.9863   |
| 0.0327        | 4.41  | 75   | 0.0243          | 0.4740    | 0.4919 | 0.4828 | 0.9910   |
| 0.02          | 5.88  | 100  | 0.0191          | 0.7656    | 0.7946 | 0.7798 | 0.9954   |
| 0.0084        | 7.35  | 125  | 0.0229          | 0.7766    | 0.7892 | 0.7828 | 0.9952   |
| 0.0045        | 8.82  | 150  | 0.0172          | 0.8351    | 0.8486 | 0.8418 | 0.9964   |
| 0.0023        | 10.29 | 175  | 0.0190          | 0.9148    | 0.8703 | 0.8920 | 0.9968   |
| 0.0015        | 11.76 | 200  | 0.0189          | 0.8469    | 0.8973 | 0.8714 | 0.9971   |


### Framework versions

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