Update README.md

README.md

@@ -117,45 +117,37 @@ 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)
+
+ ## 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)
+
 
 
 <!-- This section describes the evaluation protocols and provides the results. -->