Update README.md

README.md

@@ -19,8 +19,11 @@ This model is built on the [DistilBERT](https://huggingface.co/distilbert/distil
 The model processes input text to determine whether it is a statement or a question. It is used in the ilert Search Algorithem. 
 ### Training Data
 
-The model was trained on a diverse dataset containing examples of both statements and questions. The training process involved fine-tuning the pre-trained DistilBERT model on this specific classification task. The dataset included various types of questions (e.g., yes/no questions, wh-questions) and statements from different contexts to ensure robustness.
-* - 
+The model was trained on a diverse dataset containing examples of both statements and questions. The training process involved fine-tuning the pre-trained DistilBERT model on this specific classification task. The dataset included various types of questions and statements from different contexts to ensure robustness.
+
+* - Quora Question Keyword Pairs
+* - Questions vs Statements Classification
+* - ilert related Questions
 
 ### Performance
 
@@ -34,7 +37,7 @@ To use this model, you can load it through the Hugging Face `transformers` libra
 from transformers import pipeline
 
 # Load the model and tokenizer
-classifier = pipeline("text-classification", model="your-model-name")
+classifier = pipeline("text-classification", model="ilert/SoQbert")
 
 # Example texts
 texts = ["Is it going to rain today?", "It is a sunny day."]
@@ -45,5 +48,5 @@ results = classifier(texts)
 # Output the results
 for text, result in zip(texts, results):
     print(f"Text: {text}")
-    print(f"Classification: {result['label']} - Confidence: {result['score']:.4f}")
+    print(f"Classification: {result['label']}")
 ```