
# 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
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.
{"language": ["en"], "license": "apache-2.0", "tags": ["zero shot", "text classification", "news classification"], "datasets": ["AyoubChLin/CNN_News_Articles_2011-2022"], "metrics": ["accuracy", "f1"], "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 


# 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 & BOUBEKRI Faycal

#### Original Model
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:


#### 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.
# 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 & BOUBEKRI Faycal #### Original Model
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: #### 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 
 # 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 & BOUBEKRI Faycal #### Original Model
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: #### 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 
# 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 & BOUBEKRI Faycal#### Original Model
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:#### 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.