---
license: mit
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model
  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. -->

# NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2050
- Precision: 0.6079
- Recall: 0.5407
- F1: 0.5723
- Accuracy: 0.9528

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.2223          | 0.3125    | 0.1619 | 0.2133 | 0.9212   |
| No log        | 2.0   | 142  | 0.1599          | 0.5228    | 0.3539 | 0.4221 | 0.9446   |
| No log        | 3.0   | 213  | 0.1472          | 0.5298    | 0.4385 | 0.4798 | 0.9470   |
| No log        | 4.0   | 284  | 0.1441          | 0.5885    | 0.4729 | 0.5244 | 0.9514   |
| No log        | 5.0   | 355  | 0.1675          | 0.5654    | 0.5146 | 0.5388 | 0.9491   |
| No log        | 6.0   | 426  | 0.1592          | 0.5860    | 0.5082 | 0.5443 | 0.9521   |
| No log        | 7.0   | 497  | 0.1634          | 0.5621    | 0.5587 | 0.5604 | 0.9509   |
| 0.1349        | 8.0   | 568  | 0.1897          | 0.5803    | 0.5182 | 0.5475 | 0.9515   |
| 0.1349        | 9.0   | 639  | 0.1880          | 0.5699    | 0.5539 | 0.5618 | 0.9506   |
| 0.1349        | 10.0  | 710  | 0.1939          | 0.5923    | 0.5415 | 0.5657 | 0.9525   |
| 0.1349        | 11.0  | 781  | 0.1988          | 0.5863    | 0.5475 | 0.5662 | 0.9518   |
| 0.1349        | 12.0  | 852  | 0.2050          | 0.6079    | 0.5407 | 0.5723 | 0.9528   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1