---
license: apache-2.0
base_model: bert-base-uncased
tags:
- text-classification
- bert
- english
model-index:
- name: BERT Classification
  results: []
language:
- en
pipeline_tag: text-classification
metrics:
- accuracy
---

# BERT Classification

## Model Overview

- **Model Name**: BERT Classification
- **Model Type**: Text Classification
- **Developer**: Mansoor Hamidzadeh
- **Framework**: Transformers
- **Language**: English
- **License**: Apache-2.0

## Model Description

This model is a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) designed for text classification tasks. It categorizes text into four labels:

- **Label 1**: Household
- **Label 2**: Books
- **Label 3**: Clothing & Accessories
- **Label 4**: Electronics

## Technical Details

- **Model Size**: 109M parameters
- **Tensor Type**: F32
- **File Format**: Safetensors

## How To Use 
```python
# Use a pipeline as a high-level helper
from transformers import pipeline

text=''
pipe = pipeline("text-classification", model="mansoorhamidzadeh/bert_classification")
pipe(text)

```
## Usage

The model is useful for categorizing product descriptions or similar text data into predefined labels.


## Citation

If you use this model in your research or applications, please cite it as follows:

```bibtex
@misc{mansoorhamidzadeh/bert_classification,
  author = {mansoorhamidzadeh},
  title = {English to Persian Translation using MT5-Small},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/mansoorhamidzadeh/bert_classification}},
}