AyoubChLin
/

distilBART-mnli-cnn_news

@@ -14,28 +14,28 @@ tags:
 - 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 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/)
-## Original Model
 [valhalla/distilbart-mnli-12-1](https://huggingface.co/valhalla/distilbart-mnli-12-1)
-## Model Architecture
 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.
-## 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 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:
 ```python
@@ -51,5 +51,5 @@ classifier = pipeline(
     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.

 - news classification
 ---
+# distilBART-MNLI for ZeroShot-Text-Classification fine tuned on cnn news article
 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/)
+#### Original Model
 [valhalla/distilbart-mnli-12-1](https://huggingface.co/valhalla/distilbart-mnli-12-1)
+#### Model Architecture
 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.
+#### 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 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:
 ```python
     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.