heBERT_NER Open-source Hebrew Language Model - Free Deployment with Support for Polarity Analysis and Sentiment Recognition

Hebert NER

Developed by avichr

HeBERT is a Hebrew pretrained language model based on the BERT architecture, supporting tasks such as polarity analysis and sentiment recognition.

Large Language Model

Transformers

#Hebrew Pretraining #Sentiment Analysis #Named Entity Recognition

Downloads 435

Release Time : 3/2/2022

Model Overview

HeBERT is a pretrained language model specifically designed for Hebrew, based on the BERT-Base architecture. It supports various natural language processing tasks, including named entity recognition, sentiment analysis, and sentiment recognition.

Model Features

Hebrew-specific

Specifically designed and optimized for Hebrew, excelling in Hebrew NLP tasks.

Multi-task Support

Supports various natural language processing tasks, including NER, sentiment analysis, and sentiment recognition.

Large-scale Training Data

Trained on multiple Hebrew datasets including OSCAR, Wikipedia, and specially collected sentiment data.

Model Capabilities

Named Entity Recognition

Sentiment Analysis

Sentiment Recognition

Masked Language Modeling

Use Cases

Text Analysis

Social Media Sentiment Analysis

Analyze the sentiment tendencies of Hebrew social media content.

Named Entity Recognition

Identify names of people, organizations, and locations in Hebrew text.

Evaluated using F1 score

🚀 HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition

HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture with a BERT-Base configuration. This model aims to provide effective solutions for polarity analysis and emotion recognition in Hebrew text.

HeBERT Logo

🚀 Quick Start

HeBERT is trained on three datasets:

A Hebrew version of OSCAR: Approximately 9.8 GB of data, including 1 billion words and over 20.8 million sentences.
A Hebrew dump of Wikipedia: Approximately 650 MB of data, including over 63 million words and 3.8 million sentences.
Emotion User Generated Content (UGC) data collected for the purpose of this study (described below).

✨ Features

Named-entity recognition (NER)

The model has the ability to classify named entities in text, such as persons' names, organizations, and locations. It was tested on a labeled dataset from Ben Mordecai and M Elhadad (2005) and evaluated using the F1-score.

💻 Usage Examples

Basic Usage

from transformers import pipeline

# how to use?
NER = pipeline(
    "token-classification",
    model="avichr/heBERT_NER",
    tokenizer="avichr/heBERT_NER",
)
NER('דויד לומד באוניברסיטה העברית שבירושלים')

📚 Documentation

Other tasks

Emotion Recognition Model. An online model can be found at huggingface spaces or as colab notebook.
Sentiment Analysis.
masked-LM model (can be fine-tuned to any down-stream task).

📄 License

Contact us

Avichay Chriqui
Inbal yahav
The Coller Semitic Languages AI Lab

Thank you, תודה, شكرا

If you used this model please cite us as:

Chriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909.

@article{chriqui2021hebert,
  title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition},
  author={Chriqui, Avihay and Yahav, Inbal},
  journal={arXiv preprint arXiv:2102.01909},
  year={2021}
}

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