Update README.md

README.md

@@ -8,4 +8,42 @@ pipeline_tag: text-classification
 tags:
 - neural-search
 - neural-search-query-classification
----
+---
+
+## Model Details
+
+This model is built on the [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) architecture, specifically utilizing the `distilbert-base-uncased` variant, and is designed to classify text into two categories: statements and questions. It leverages the strengths of the DistilBERT model, known for its efficiency and performance, to accurately discern between declarative statements and interrogative questions.
+
+### Model Description
+
+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.
+* - 
+
+### Performance
+
+The performance of the model was evaluated using standard metrics for classification tasks, including accuracy, precision, recall, and F1 score. The results indicate that the model performs well in distinguishing between statements and questions, making it a reliable tool for text classification tasks in natural language processing.
+
+### Usage
+
+To use this model, you can load it through the Hugging Face `transformers` library and use it for text classification. Here is an example of how to use the model in Python:
+
+```python
+from transformers import pipeline
+
+# Load the model and tokenizer
+classifier = pipeline("text-classification", model="your-model-name")
+
+# Example texts
+texts = ["Is it going to rain today?", "It is a sunny day."]
+
+# Classify texts
+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}")
+```