Update README.md

README.md

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 ---
 license: apache-2.0
+datasets:
+- AyoubChLin/CNN_News_Articles_2011-2022
+language:
+- en
+metrics:
+- f1
+- accuracy
+pipeline_tag: zero-shot-classification
+tags:
+- zero shot
+- text classification
+- news classification
 ---
+
+# Huggingface Model: BART-MNLI-ZeroShot-Text-Classification
+This is a Huggingface model fine-tuned on the CNN news dataset for zero-shot text classification task using BART-MNLI. The model achieved an f1 score of 94% and an accuracy of 94% on the CNN test dataset with a maximum length of 128 tokens.
+
+## Authors
+This work was done by [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/) & [BOUBEKRI Faycal](https://www.linkedin.com/in/faycal-boubekri-832848199/)
+
+## Model Architecture
+The model architecture is based on the BART-MNLI transformer model. BART (Bidirectional and Auto-Regressive Transformers) is a denoising autoencoder that is pre-trained on a large corpus of text and fine-tuned on downstream natural language processing tasks.
+
+## Dataset
+The CNN news dataset was used for fine-tuning the model. This dataset contains news articles from the CNN website and is labeled into 6 categories, including politics, health, entertainment, tech, travel, world, and sports.
+
+## Fine-tuning Parameters
+The model was fine-tuned for 1 epoch on a maximum length of 256 tokens. The training took approximately 6 hours to complete.
+
+## Evaluation Metrics
+The model achieved an f1 score of 94% and an accuracy of 94% on the CNN test dataset with a maximum length of 128 tokens.
+
+# Usage
+The model can be used for zero-shot text classification tasks on news articles. It can be accessed via the Huggingface Transformers library using the following code:
+
+```python
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/DistilBart_cnn_zeroShot")
+
+model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/DistilBart_cnn_zeroShot")
+classifier = pipeline(
+    "zero-shot-classification",
+    model=model,
+    tokenizer=tokenizer,
+    device=0
+)
+```
+## Acknowledgments
+We would like to acknowledge the Huggingface team for their open-source implementation of transformer models and the CNN news dataset for providing the labeled dataset for fine-tuning.