Update README.md

README.md

@@ -1,16 +1,16 @@
 ## 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 [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805). <br>
+HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture and it is BERT-Base config [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805). <br>
 
-HeBert was trained on three dataset: 
-1. A Hebrew version of OSCAR [(Ortiz, 2019)](https://oscar-corpus.com/): ~9.8 GB of data, including 1 billion words and over 20.8 millions sentences. 
-2. A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 millions words and 3.8 millions sentences
-3. Emotion UGC data that was collected for the purpose of this study. (described below)
-We evaluated the model on emotion recognition and sentiment analysis, for a downstream tasks. 
+HeBert was trained on three datasets: 
+1. A Hebrew version of OSCAR [(Ortiz, 2019)](https://oscar-corpus.com/): ~9.8 GB of data, including 1 billion words and over 20.8 million sentences. 
+2. A Hebrew dump of Wikipedia: ~650 MB of data, including over 63 million words and 3.8 million sentences
+3. Emotion UGC data was collected for the purpose of this study. (described below)
+We evaluated the model on emotion recognition and sentiment analysis, for downstream tasks. 
 
 ### Emotion UGC Data Description
-Our User Genrated Content (UGC) is comments written on articles collected from 3 major news sites, between January 2020 to August 2020,. Total data size ~150 MB of data, including over 7 millions words and 350K sentences.
-4000 sentences annotated by crowd members (3-10 annotators per sentence) for 8 emotions (anger, disgust, expectation , fear, happy, sadness, surprise and trust) and overall sentiment / polarity<br>
-In order to valid the annotation, we search an agreement between raters to emotion in each sentence using krippendorff's alpha [(krippendorff, 1970)](https://journals.sagepub.com/doi/pdf/10.1177/001316447003000105). We left sentences that got alpha > 0.7. Note that while we found a general agreement between raters about emotion like happy, trust and disgust, there are few emotion with general disagreement about them, apparently given the complexity of finding them in the text (e.g. expectation and surprise).
+Our User-Generated Content (UGC) is comments written on articles collected from 3 major news sites, between January 2020 to August 2020, Total data size of ~150 MB of data, including over 7 million words and 350K sentences.
+4000 sentences annotated by crowd members (3-10 annotators per sentence) for 8 emotions (anger, disgust, expectation, fear, happy, sadness, surprise, and trust) and overall sentiment/polarity <br>
+In order to validate the annotation, we search for an agreement between raters to emotion in each sentence using Krippendorff's alpha [(krippendorff, 1970)](https://journals.sagepub.com/doi/pdf/10.1177/001316447003000105). We left sentences that got alpha > 0.7. Note that while we found a general agreement between raters about emotions like happiness, trust, and disgust, there are few emotions with general disagreement about them, apparently given the complexity of finding them in the text (e.g. expectation and surprise).
 
 ### Performance
 #### sentiment analysis  
@@ -26,47 +26,47 @@ In order to valid the annotation, we search an agreement between raters to emoti
 
 ## How to use
 ### 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.")
+\tfrom transformers import AutoTokenizer, AutoModel
+\ttokenizer = AutoTokenizer.from_pretrained("avichr/heBERT")
+\tmodel = AutoModel.from_pretrained("avichr/heBERT")
+\t
+\tfrom transformers import pipeline
+\tfill_mask = pipeline(
+\t    "fill-mask",
+\t    model="avichr/heBERT",
+\t    tokenizer="avichr/heBERT"
+\t)
+\tfill_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}]]
+\tfrom transformers import AutoTokenizer, AutoModel, pipeline
+\ttokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer
+\tmodel = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis")
+\t
+\t# how to use?
+\tsentiment_analysis = pipeline(
+\t    "sentiment-analysis",
+\t    model="avichr/heBERT_sentiment_analysis",
+\t    tokenizer="avichr/heBERT_sentiment_analysis",
+\t    return_all_scores = True
+\t)
+\t
+\tsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t
+\t>>>  [[{'label': 'natural', 'score': 0.9978172183036804},
+\t>>>  {'label': 'positive', 'score': 0.0014792329166084528},
+\t>>>  {'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}]]
+\tsentiment_analysis('קפה זה טעים')
+\t>>>  [[{'label': 'natural', 'score': 0.00047328314394690096},
+\t>>>  {'label': 'possitive', 'score': 0.9994067549705505},
+\t>>>  {'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}]]
+\tsentiment_analysis('אני לא אוהב את העולם')
+\t>>>  [[{'label': 'natural', 'score': 9.214012970915064e-05}, 
+\t>>>  {'label': 'possitive', 'score': 8.876807987689972e-05}, 
+\t>>>  {'label': 'negetive', 'score': 0.9998190999031067}]]
 
-	
+\t
 Our model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda)
 
 
@@ -80,7 +80,7 @@ our git: https://github.com/avichaychriqui/HeBERT
 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},
+  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}