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---
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 |