README.md

---
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.

## Example 

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
from scipy.special import expit

MODEL = "PavanDeepak/Topic_Classification"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
class_mapping = model.config.id2label

text = "I love chicken manchuria"
tokens = tokenizer(text, return_tensors="pt")
output = model(**tokens)

scores = output.logits[0][0].detach().numpy()
scores = expit(scores)
predictions = (scores >= 0.5) * 1

for i in range(len(predictions)):
    if predictions[i]:
        print(class_mapping[i])
```python


## Output:

* Food
* Videogames & Shows
* Homestyle
* Travel
* Health