Update README.md

README.md

@@ -26,47 +26,50 @@ In order to validate the annotation, we search for an agreement between raters t
 
 ## How to use
 ### For masked-LM model (can be fine-tunned to any down-stream task)
-\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.")
+```
+	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):
-\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}]]
-
-\tsentiment_analysis('קפה זה טעים')
-\t>>>  [[{'label': 'natural', 'score': 0.00047328314394690096},
-\t>>>  {'label': 'possitive', 'score': 0.9994067549705505},
-\t>>>  {'label': 'negetive', 'score': 0.00011996887042187154}]]
+```
+	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}]]
 
-\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}]]
+	sentiment_analysis('קפה זה טעים')
+	>>>  [[{'label': 'natural', 'score': 0.00047328314394690096},
+	>>>  {'label': 'possitive', 'score': 0.9994067549705505},
+	>>>  {'label': 'negetive', 'score': 0.00011996887042187154}]]
 
-\t
+	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](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda)
 
 
@@ -80,7 +83,7 @@ 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}