README.md

---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- Multilabel
metrics:
- f1
- accuracy
- roc_auc
model-index:
- name: bert-base-uncased-Research_Articles_Multilabel
  results: []
pipeline_tag: text-classification
---

# bert-base-uncased-Research_Articles_Multilabel

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).

It achieves the following results on the evaluation set:
- Loss: 0.2039
- F1: 0.8405
- Roc Auc: 0.8976
- Accuracy: 0.7082

## Model description

Here is the link to my code for this model: https://github.com/DunnBC22/NLP_Projects/blob/main/Multilabel%20Classification/Research%20Articles/Research%20Articles%20-%20Multilabel%20Classification%20-%20Bert-Base-Uncased.ipynb

## Intended uses & limitations

This model could be used to read labels with printed text. You are more than welcome to use it, but remember that it is at your own risk/peril.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/shivanandmn/multilabel-classification-dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2425        | 1.0   | 2097 | 0.1948          | 0.8348 | 0.8921  | 0.7067   |
| 0.1739        | 2.0   | 4194 | 0.1986          | 0.8348 | 0.8926  | 0.7072   |
| 0.1328        | 3.0   | 6291 | 0.2039          | 0.8405 | 0.8976  | 0.7082   |

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3