albert - fa - base - v2 - sentiment - binary Open - source Model - Supports Self - supervised Learning for Persian Language Representation

Home

Albert Fa Base V2 Sentiment Binary

Developed by m3hrdadfi

The Persian ALBERT model is a lightweight BERT for self-supervised learning of Persian language representations

Large Language Model

Transformers

OtherOpen Source License:Apache-2.0 #Lightweight BERT #Persian Sentiment Analysis #Self-supervised Learning

Downloads 124

Release Time : 3/2/2022

Model Overview

This model is the first attempt at implementing ALBERT for Persian, trained based on Google's ALBERT BASE 2.0 version, covering various writing styles of Persian text.

Model Features

Lightweight Design

As an ALBERT model, it has fewer parameters and higher efficiency compared to traditional BERT.

Extensive Data Coverage

Training data covers various writing styles of Persian text including scientific, fiction, and news.

Sentiment Analysis Optimization

Specially optimized and tested for Persian sentiment analysis tasks.

Model Capabilities

Persian Text Understanding

Sentiment Classification

Language Representation Learning

Use Cases

Sentiment Analysis

E-commerce Review Analysis

Analyze sentiment tendencies in user reviews on platforms like Digikala.

Achieved an F1 score of 87.56% on the combined dataset.

Restaurant Review Analysis

Analyze user sentiment in reviews on food delivery platforms like SnappFood.

🚀 ALBERT Persian

A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language

ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, similar to the approach used for ParsBERT. You can also call it "Bert_Kucholo".

Please follow the ALBERT-Persian repo for the latest information about previous and current models.

🚀 Quick Start

This project focuses on applying ALBERT to the Persian language, offering a lightweight BERT - based model for self - supervised learning of Persian language representations.

✨ Features

Persian Sentiment [Digikala, SnappFood, DeepSentiPers]

It aims to classify text, such as comments, based on their emotional bias. We tested three well - known datasets for this task: Digikala user comments, SnappFood user comments, and DeepSentiPers in two binary - form and multi - form types.

Results

The model obtained an F1 score of 87.56% for a composition of all three datasets into binary labels Negative and Positive.

📚 Documentation

BibTeX entry and citation info

Please cite in publications as the following:

@misc{ALBERTPersian,
  author = {Mehrdad Farahani},
  title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}

@article{ParsBERT,
    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
    journal={ArXiv},
    year={2020},
    volume={abs/2005.12515}
}

📄 License

This project is licensed under the Apache - 2.0 license.

❓ Questions?

Post a Github issue on the ALBERT-Persian repo.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご