Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- AyoubChLin/CNN_News_Articles_2011-2022
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: zero-shot-classification
 ---
+
+# DistilBERT for Zero Shot Classification
+
+This repository contains a DistilBERT model trained for zero-shot classification on CNN articles. The model has been evaluated on CNN articles and achieved an accuracy of 0.956 and an F1 score of 0.955.
+
+## Model Details
+- Architecture: DistilBERT
+- Training Data: CNN articles
+- Accuracy: 0.956
+- F1 Score: 0.955
+
+## Usage
+
+To use this model for zero-shot classification, you can follow the steps below:
+
+
+
+1. Load the trained model:
+   ```python
+   from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+    tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/DistilBERT_ZeroShot")
+
+    model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/DistilBERT_ZeroShot")
+
+   ```
+
+4. Classify text using zero-shot classification:
+   ```python
+   from transformers import pipeline
+
+   # Create a zero-shot classification pipeline
+   classifier = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer)
+
+   # Classify a sentence
+   sentence = "The latest scientific breakthroughs in medicine"
+   candidate_labels = ["politics", "sports", "technology", "business"]
+
+   result = classifier(sentence, candidate_labels)
+
+   print(result)
+   ```
+
+   The output will be a dictionary containing the classified label and the corresponding classification score.
+
+## About the Author
+
+This work was created by Ayoub Cherguelaine.
+
+If you have any questions or suggestions regarding this repository or the trained model, feel free to reach out to Ayoub Cherguelaine.