metadata

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

# 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}},
}