The open-source Persian comprehension model bert-fa-base-uncased-sentiment-deepsentipers-multi has a reorganized vocabulary and more functions.

Bert Fa Base Uncased Sentiment Deepsentipers Multi

Developed by HooshvareLab

A Transformer-based Persian language understanding model, reconstructed vocabulary and fine-tuned on a new Persian corpus, offering more features.

Large Language Model OtherOpen Source License:Apache-2.0 #Persian Sentiment Analysis #Multi-class Model #User Comment Processing

Downloads 80

Release Time : 3/2/2022

Model Overview

ParsBERT is a Transformer-based Persian language understanding model, primarily used for natural language processing tasks such as sentiment analysis.

Model Features

Reconstructed Vocabulary

Reconstructed vocabulary in v2.0, improving model performance.

Multi-domain Adaptation

Fine-tuned on a new Persian corpus, enhancing usability across other domains.

Multi-class Support

Supports multi-class and binary classification tasks for Persian sentiment analysis.

Model Capabilities

Persian Text Understanding

Sentiment Analysis

Multi-class Tasks

Binary Classification Tasks

Use Cases

Sentiment Analysis

Digikala User Comment Analysis

Analyze sentiment tendencies in user comments on the e-commerce platform Digikala.

SnappFood User Comment Analysis

Analyze sentiment tendencies in user comments on the food delivery platform SnappFood.

DeepSentiPers Analysis

Perform multi-class or binary sentiment analysis on digital product user opinions.

F1 Score 71.31 (Multi-class)/92.42 (Binary)

🚀 ParsBERT (v2.0)

A Transformer-based Model for Persian Language Understanding

We reconstructed the vocabulary and fine-tuned ParsBERT v1.1 on new Persian corpora to offer additional functionalities for using ParsBERT in other scenarios. For the latest information on previous and current models, please follow the ParsBERT repository.

🚀 Quick Start

✨ Features

Persian Sentiment [Digikala, SnappFood, DeepSentiPers]

This feature 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 both binary and multi - form types.

DeepSentiPers

DeepSentiPers, a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes: two positive classes (happy and delighted), two negative classes (furious and angry), and one neutral class. Thus, this dataset can be used for both multi - class and binary classification. In binary classification, the neutral class and its corresponding sentences are removed from the dataset.

Binary Classification:

Negative (Furious + Angry)
Positive (Happy + Delighted)

Multi - Class Classification:

Furious
Angry
Neutral
Happy
Delighted

Label	#
Furious	236
Angry	1357
Neutral	2874
Happy	2848
Delighted	2516

Download: You can download the dataset from:

📚 Documentation

Results

The following table summarizes the F1 score obtained by ParsBERT compared to other models and architectures.

Dataset	ParsBERT v2	ParsBERT v1	mBERT	DeepSentiPers
SentiPers (Multi Class)	71.31*	71.11	-	69.33
SentiPers (Binary Class)	92.42*	92.13	-	91.98

💻 Usage Examples

Task	Notebook
Sentiment Analysis

📄 License

This project is licensed under the Apache - 2.0 license.

BibTeX entry and citation info

Please cite in publications as the following:

@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}
}

❓ Questions?

Post a Github issue on the ParsBERT Issues repository.

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