Update README.md

README.md

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 # XLM-RoBERTa Token Classification for Named Entity Recognition (NER)
-This model is a fine-tuned version of XLM-RoBERTa (xlm-roberta-base) for Named Entity Recognition (NER) tasks. It has been trained on the PAN-X subset of the XTREME dataset for four languages: German (de), French (fr), Italian (it), and English (en). The model identifies the following entity types:
+This model is a fine-tuned version of XLM-RoBERTa (xlm-roberta-base) for Named Entity Recognition (NER) tasks. It has been trained on the PAN-X subset of the XTREME dataset for  German Language . The model identifies the following entity types:
 
 PER: Person names
 ORG: Organization names
@@ -118,7 +118,37 @@ The model's performance is evaluated using the F1 score for NER. The predictions
 [More Information Needed]
 
 ## Evaluation
-
+('''import torch
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+import pandas as pd
+
+# Load the fine-tuned XLM-RoBERTa model and tokenizer from Hugging Face
+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")
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForTokenClassification.from_pretrained(model_checkpoint).to(device)
+
+# Create the NER pipeline
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, framework="pt", device=0 if torch.cuda.is_available() else -1)
+
+# Define the helper function to use the NER pipeline
+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
+
+# Example usage
+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. -->
 
 ### Testing Data, Factors & Metrics