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

HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config.

HeBert was trained on three dataset:

Named-entity recognition (NER)

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

How to use

    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')

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-tunned to any down-stream task).

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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