Update README.md

README.md

@@ -27,49 +27,49 @@ 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)
 ```
-	from transformers import AutoTokenizer, AutoModel
-	tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT")
-	model = AutoModel.from_pretrained("avichr/heBERT")
+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.")
+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}]]
+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](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda)
 
 
@@ -83,7 +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}