Update README.md

README.md

@@ -120,15 +120,19 @@ The model's performance is evaluated using the F1 score for NER. The predictions
 ## Evaluation
 '''python
 import torch
+
 from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
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 import pandas as pd
 
 
 model_checkpoint = "MassMin/xlm-roberta-base-finetuned-panx-de"  # Replace with your Hugging Face model name
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 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
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 model = AutoModelForTokenClassification.from_pretrained(model_checkpoint).to(device)
 
 
@@ -136,6 +140,7 @@ ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, framework="pt",
 
 
 def tag_text_with_pipeline(text, ner_pipeline):
+
     # Use the NER pipeline to get predictions
     results = ner_pipeline(text)
     
@@ -147,9 +152,12 @@ def tag_text_with_pipeline(text, ner_pipeline):
 
 
 text = "Jeff Dean works at Google in California."
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 result = tag_text_with_pipeline(text, ner_pipeline)
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 print(result)
-'''
+
+
 <!-- This section describes the evaluation protocols and provides the results. -->
 
 ### Testing Data, Factors & Metrics