mansoorhamidzadeh/bert_classification

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

# 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:

@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}},
}