Update README.md

README.md

@@ -118,44 +118,44 @@ The model's performance is evaluated using the F1 score for NER. The predictions
 [More Information Needed]
 
 ## Evaluation
-  '''python
-  import torch
-  
-  from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
-  
-  import pandas as pd
-  
-  
-  model_checkpoint = "MassMin/xlm-roberta-base-finetuned-panx-de"  # Replace with your Hugging Face model name
-  
-  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-  
-  
-  tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-  
-  model = AutoModelForTokenClassification.from_pretrained(model_checkpoint).to(device)
-  
-  
-  ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, framework="pt", device=0 if torch.cuda.is_available() else -1)
-  
-  
-  def tag_text_with_pipeline(text, ner_pipeline):
-  
-      # Use the NER pipeline to get predictions
-      results = ner_pipeline(text)
-      
-      # Convert results to a DataFrame for easy viewing
-      df = pd.DataFrame(results)
-      df = df[['word', 'entity', 'score']]
-      df.columns = ['Tokens', 'Tags', 'Score']  # Rename columns for clarity
-      return df
-  
-  
-  text = "Jeff Dean works at Google in California."
-  
-  result = tag_text_with_pipeline(text, ner_pipeline)
-  
-  print(result)
+  #'''python
+    import torch
+    
+    from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+    
+    import pandas as pd
+    
+    
+    model_checkpoint = "MassMin/xlm-roberta-base-finetuned-panx-de"  # Replace with your Hugging Face model name
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    
+    model = AutoModelForTokenClassification.from_pretrained(model_checkpoint).to(device)
+    
+    
+    ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, framework="pt", device=0 if torch.cuda.is_available() else -1)
+    
+    
+    def tag_text_with_pipeline(text, ner_pipeline):
+    
+        # Use the NER pipeline to get predictions
+        results = ner_pipeline(text)
+        
+        # Convert results to a DataFrame for easy viewing
+        df = pd.DataFrame(results)
+        df = df[['word', 'entity', 'score']]
+        df.columns = ['Tokens', 'Tags', 'Score']  # Rename columns for clarity
+        return df
+    
+    
+    text = "Jeff Dean works at Google in California."
+    
+    result = tag_text_with_pipeline(text, ner_pipeline)
+    
+    print(result)
 
 
 <!-- This section describes the evaluation protocols and provides the results. -->