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 # 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.
+This is a Huggingface model fine-tuned on the CNN news dataset for zero-shot text classification task using DistilBART-MNLI. The model achieved an f1 score of 93% and an accuracy of 93% 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.
+The model architecture is based on the DistilBART-MNLI transformer model. DistilBART is a smaller and faster version of BART 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.
@@ -30,7 +30,7 @@ The CNN news dataset was used for fine-tuning the model. This dataset contains n
 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.
+The model achieved an f1 score of 93% and an accuracy of 93% 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: