---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Yepes_2e-05_29_03
  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. -->

# Yepes_2e-05_29_03

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: 0.1453
- Precision: 0.5461
- Recall: 0.4375
- F1: 0.4858
- Accuracy: 0.9769

## 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: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9751        | 5.0   | 25   | 0.2060          | 0.0       | 0.0    | 0.0    | 0.9697   |
| 0.1835        | 10.0  | 50   | 0.1590          | 0.0       | 0.0    | 0.0    | 0.9697   |
| 0.1271        | 15.0  | 75   | 0.1311          | 0.1447    | 0.125  | 0.1341 | 0.9691   |
| 0.093         | 20.0  | 100  | 0.1335          | 0.3043    | 0.1989 | 0.2405 | 0.9739   |
| 0.0739        | 25.0  | 125  | 0.1274          | 0.3615    | 0.2670 | 0.3072 | 0.9744   |
| 0.0554        | 30.0  | 150  | 0.1267          | 0.5       | 0.3239 | 0.3931 | 0.9761   |
| 0.0419        | 35.0  | 175  | 0.1283          | 0.4458    | 0.4205 | 0.4327 | 0.9753   |
| 0.0336        | 40.0  | 200  | 0.1343          | 0.4958    | 0.3352 | 0.4000 | 0.9756   |
| 0.0264        | 45.0  | 225  | 0.1314          | 0.5303    | 0.3977 | 0.4545 | 0.9770   |
| 0.022         | 50.0  | 250  | 0.1309          | 0.5468    | 0.4318 | 0.4825 | 0.9776   |
| 0.0185        | 55.0  | 275  | 0.1372          | 0.5468    | 0.4318 | 0.4825 | 0.9776   |
| 0.0163        | 60.0  | 300  | 0.1383          | 0.5315    | 0.4318 | 0.4765 | 0.9767   |
| 0.0146        | 65.0  | 325  | 0.1413          | 0.5486    | 0.4489 | 0.4937 | 0.9770   |
| 0.0131        | 70.0  | 350  | 0.1407          | 0.5781    | 0.4205 | 0.4868 | 0.9776   |
| 0.012         | 75.0  | 375  | 0.1428          | 0.5821    | 0.4432 | 0.5032 | 0.9776   |
| 0.011         | 80.0  | 400  | 0.1453          | 0.5461    | 0.4375 | 0.4858 | 0.9769   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2