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---
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}},
}
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