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

# tmvar_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: 0.0104
- Precision: 0.8718
- Recall: 0.9189
- F1: 0.8947
- Accuracy: 0.9977

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2897        | 1.0   | 25   | 0.0896          | 0.0       | 0.0    | 0.0    | 0.9858   |
| 0.0759        | 2.0   | 50   | 0.0302          | 0.5522    | 0.4    | 0.4639 | 0.9898   |
| 0.0347        | 3.0   | 75   | 0.0175          | 0.6789    | 0.6973 | 0.688  | 0.9945   |
| 0.0174        | 4.0   | 100  | 0.0133          | 0.76      | 0.8216 | 0.7896 | 0.9962   |
| 0.0084        | 5.0   | 125  | 0.0125          | 0.805     | 0.8703 | 0.8364 | 0.9967   |
| 0.0048        | 6.0   | 150  | 0.0090          | 0.8859    | 0.8811 | 0.8835 | 0.9977   |
| 0.0025        | 7.0   | 175  | 0.0097          | 0.8382    | 0.9243 | 0.8792 | 0.9977   |
| 0.0017        | 8.0   | 200  | 0.0089          | 0.8529    | 0.9405 | 0.8946 | 0.9980   |
| 0.0015        | 9.0   | 225  | 0.0099          | 0.8357    | 0.9351 | 0.8827 | 0.9979   |
| 0.0012        | 10.0  | 250  | 0.0104          | 0.8522    | 0.9351 | 0.8918 | 0.9979   |
| 0.0011        | 11.0  | 275  | 0.0104          | 0.8798    | 0.8703 | 0.875  | 0.9972   |
| 0.0009        | 12.0  | 300  | 0.0098          | 0.8718    | 0.9189 | 0.8947 | 0.9977   |
| 0.0007        | 13.0  | 325  | 0.0100          | 0.8718    | 0.9189 | 0.8947 | 0.9977   |
| 0.0006        | 14.0  | 350  | 0.0104          | 0.8718    | 0.9189 | 0.8947 | 0.9977   |


### Framework versions

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