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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 and it is a BERT-Base config.

HeBERT was trained on three dataset:

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

<div> We evaluated the model on downstream tasks: emotions recognition and sentiment analysis.

Emotion UGC Data Description

Our UGC data include comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~4000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, expectation , fear, joy, sadness, surprise and trust.

For our robustness analyses, we also collected and annotated two additional datasets. The first contains a random set of comments taken from our in-domain dataset (that is, comments that were posted on Covid-related news articles). The second is a random set of comments taken from an out-of-domain dataset containing comments that were posted in response to non-Covid-related articles from the same news sites. An additional explanation can be found in section 5.1 of our article. The percentage of sentences in which each emotion appeared is found in the table below.

	anger	disgust	expectation	fear	happy	sadness	surprise	trust	sentiment
Main Dataset	0.78	0.83	0.58	0.45	0.12	0.59	0.17	0.11	0.25
Random Comments from the Corpus	0.79	0.87	0.46	0.17	0.03	0.30	0.00	0.03	0.02
Out of Domain	0.76	0.89	0.62	0.10	0.08	0.36	0.02	0.13	0.12

All the datasets can be found on "data.zip" in this git (where each row stands for a different annotator of a sentence). The agreed score which we used to train and test our models, can be found in the column 'agreed score' (if we found sufficient agreement). See our article for more details on the annotation process. If you use our datasets please cite us (can be found below).

Performance

Emotion Recognition

emotion	f1-score	precision	recall
anger	0.96	0.99	0.93
disgust	0.97	0.98	0.96
expectation	0.82	0.80	0.87
fear	0.79	0.88	0.72
happy	0.90	0.97	0.84
sadness	0.90	0.86	0.94
sentiment	0.88	0.90	0.87
surprise	0.40	0.44	0.37
trust	0.83	0.86	0.80

The above metrics for positive class (meaning, the emotion is reflected in text) for the main dataset

Sentiment (Polarity) Analysis

	precision	recall	f1-score
natural	0.83	0.56	0.67
positive	0.96	0.92	0.94
negative	0.97	0.99	0.98
accuracy			0.97
macro avg	0.92	0.82	0.86
weighted avg	0.96	0.97	0.96

How to use

For Emotion Recognition Model

An online model can be found at huggingface spaces or as colab notebook

# !pip install pyplutchik==0.0.7
# !pip install transformers==4.14.1

!git clone https://github.com/avichaychriqui/HeBERT.git
from HeBERT.src.HebEMO import *
HebEMO_model = HebEMO()

HebEMO_model.hebemo(input_path = 'examples/text_example.txt')
# return analyzed pandas.DataFrame  

hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True)

For masked-LM model (can be fine-tunned to any down-stream task)

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT")
model = AutoModel.from_pretrained("avichr/heBERT")

from transformers import pipeline
fill_mask = pipeline(
    "fill-mask",
    model="avichr/heBERT",
    tokenizer="avichr/heBERT"
)
fill_mask("הקורונה לקחה את [MASK] ולנו לא נשאר דבר.")

For sentiment classification model (polarity ONLY):

from transformers import AutoTokenizer, AutoModel, pipeline
tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer
model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis")

# how to use?
sentiment_analysis = pipeline(
    "sentiment-analysis",
    model="avichr/heBERT_sentiment_analysis",
    tokenizer="avichr/heBERT_sentiment_analysis",
    return_all_scores = True
)

sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')	
>>>  [[{'label': 'natural', 'score': 0.9978172183036804},
>>>  {'label': 'positive', 'score': 0.0014792329166084528},
>>>  {'label': 'negative', 'score': 0.0007035882445052266}]]

sentiment_analysis('קפה זה טעים')
>>>  [[{'label': 'natural', 'score': 0.00047328314394690096},
>>>  {'label': 'possitive', 'score': 0.9994067549705505},
>>>  {'label': 'negetive', 'score': 0.00011996887042187154}]]

sentiment_analysis('אני לא אוהב את העולם')
>>>  [[{'label': 'natural', 'score': 9.214012970915064e-05}, 
>>>  {'label': 'possitive', 'score': 8.876807987689972e-05}, 
>>>  {'label': 'negetive', 'score': 0.9998190999031067}]]

Our model is also available on AWS! for more information visit AWS' git

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. Colab notebook

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')

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. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.

@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={INFORMS Journal on Data Science},
  year={2022}
}