|
|
--- |
|
|
license: mit |
|
|
--- |
|
|
BERT-based Text Classification Model |
|
|
This model is a fine-tuned version of the bert-base-uncased model, specifically adapted for text classification across a diverse set of categories. The model has been trained on a rich dataset collected from multiple sources, including the News Category Dataset on Kaggle and various other websites. |
|
|
|
|
|
The model classifies text into one of the following 12 categories: |
|
|
|
|
|
Food |
|
|
Videogames & Shows |
|
|
Kids and fun |
|
|
Homestyle |
|
|
Travel |
|
|
Health |
|
|
Charity |
|
|
Electronics & Technology |
|
|
Sports |
|
|
Cultural & Music |
|
|
Education |
|
|
Convenience |
|
|
The model has demonstrated robust performance with an accuracy of 0.721459, F1 score of 0.659451, precision of 0.707620, and recall of 0.635155. |
|
|
|
|
|
Model Architecture |
|
|
The model leverages the BertForSequenceClassification architecture, It has been fine-tuned on the aforementioned dataset, with the following key configuration parameters: |
|
|
|
|
|
Hidden size: 768 |
|
|
Number of attention heads: 12 |
|
|
Number of hidden layers: 12 |
|
|
Max position embeddings: 512 |
|
|
Type vocab size: 2 |
|
|
Vocab size: 30522 |
|
|
The model uses the GELU activation function in its hidden layers and applies dropout with a probability of 0.1 to the attention probabilities to prevent overfitting. |
