
# 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/)

## Original Model
[facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli)

## Model Architecture
The BART-Large-MNLI model has 12 transformer layers, a hidden size of 1024, and 406 million parameters. It is pre-trained on the English Wikipedia and BookCorpus datasets, and fine-tuned on the Multi-Genre Natural Language Inference (MNLI) task.

## 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/Bart-MNLI-CNN_news")

model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/Bart-MNLI-CNN_news")
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.
{"language": ["en"], "license": "apache-2.0", "tags": ["zero shot", "text classification", "news classification"], "datasets": ["AyoubChLin/CNN_News_Articles_2011-2022"], "metrics": ["f1", "accuracy"], "pipeline_tag": "zero-shot-classification"}
transformers pytorch safetensors bart text-classification zero shot text classification news classification zero-shot-classification en dataset:AyoubChLin/CNN_News_Articles_2011-2022 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bart #text-classification #zero shot #text classification #news classification #zero-shot-classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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 & BOUBEKRI Faycal

## Original Model
facebook/bart-large-mnli

## Model Architecture
The BART-Large-MNLI model has 12 transformer layers, a hidden size of 1024, and 406 million parameters. It is pre-trained on the English Wikipedia and BookCorpus datasets, and fine-tuned on the Multi-Genre Natural Language Inference (MNLI) task.

## 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:


## 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.
# 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 & BOUBEKRI Faycal ## Original Model
facebook/bart-large-mnli ## Model Architecture
The BART-Large-MNLI model has 12 transformer layers, a hidden size of 1024, and 406 million parameters. It is pre-trained on the English Wikipedia and BookCorpus datasets, and fine-tuned on the Multi-Genre Natural Language Inference (MNLI) task. ## 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: ## 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.
TAGS
#transformers #pytorch #safetensors #bart #text-classification #zero shot #text classification #news classification #zero-shot-classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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 & BOUBEKRI Faycal ## Original Model
facebook/bart-large-mnli ## Model Architecture
The BART-Large-MNLI model has 12 transformer layers, a hidden size of 1024, and 406 million parameters. It is pre-trained on the English Wikipedia and BookCorpus datasets, and fine-tuned on the Multi-Genre Natural Language Inference (MNLI) task. ## 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: ## 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.
TAGS
#transformers #pytorch #safetensors #bart #text-classification #zero shot #text classification #news classification #zero-shot-classification #en #dataset-AyoubChLin/CNN_News_Articles_2011-2022 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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 & BOUBEKRI Faycal## Original Model
facebook/bart-large-mnli## Model Architecture
The BART-Large-MNLI model has 12 transformer layers, a hidden size of 1024, and 406 million parameters. It is pre-trained on the English Wikipedia and BookCorpus datasets, and fine-tuned on the Multi-Genre Natural Language Inference (MNLI) task.## 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:## 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.