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Update README.md

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@@ -51,10 +51,20 @@ from transformers import AutoTokenizer, AutoModelForMaskedLM
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  tokenizer = AutoTokenizer.from_pretrained("eventdata-utd/ConfliBERT-scr-uncased-BBC_News")
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  model = AutoModelForMaskedLM.from_pretrained("eventdata-utd/ConfliBERT-scr-uncased-BBC_News")
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- # Example of usage
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  text = "The government of [MASK] was overthrown in a coup."
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  input_ids = tokenizer.encode(text, return_tensors='pt')
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  outputs = model(input_ids)
 
 
 
 
 
 
 
 
 
 
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  ```
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  ## Limitations and Bias
 
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  tokenizer = AutoTokenizer.from_pretrained("eventdata-utd/ConfliBERT-scr-uncased-BBC_News")
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  model = AutoModelForMaskedLM.from_pretrained("eventdata-utd/ConfliBERT-scr-uncased-BBC_News")
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+ # Example of usage for masking task
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  text = "The government of [MASK] was overthrown in a coup."
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  input_ids = tokenizer.encode(text, return_tensors='pt')
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  outputs = model(input_ids)
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+
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+ # Example for using the ConfliBERT-cont-cased-20news model
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+ tokenizer = AutoTokenizer.from_pretrained("eventdata-utd/ConfliBERT-cont-cased-20news")
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+ model = AutoModelForMaskedLM.from_pretrained("eventdata-utd/ConfliBERT-cont-cased-20news")
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+
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+ # Example of usage for binary classification task
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+ text = "The President of Brunei asked for protestors to remain peaceful during the upcoming Independence Day holiday."
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+ input_ids = tokenizer.encode(text, return_tensors='pt')
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+ outputs = model(input_ids)
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+
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  ```
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  ## Limitations and Bias