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}},
}
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Base model
google-bert/bert-base-uncased