metadata
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.
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