---
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 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.