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--- |
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license: apache-2.0 |
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datasets: |
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- AyoubChLin/CNN_News_Articles_2011-2022 |
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language: |
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- en |
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metrics: |
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- f1 |
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- accuracy |
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pipeline_tag: zero-shot-classification |
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tags: |
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- zero shot |
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- text classification |
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- news classification |
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--- |
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# Huggingface Model: BART-MNLI-ZeroShot-Text-Classification |
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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. |
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## Authors |
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This work was done by [CHERGUELAINE Ayoub](https://www.linkedin.com/in/ayoub-cherguelaine/) & [BOUBEKRI Faycal](https://www.linkedin.com/in/faycal-boubekri-832848199/) |
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## Original Model |
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[facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) |
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## Model Architecture |
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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. |
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## Dataset |
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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. |
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## Fine-tuning Parameters |
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The model was fine-tuned for 1 epoch on a maximum length of 256 tokens. The training took approximately 6 hours to complete. |
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## Evaluation Metrics |
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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. |
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# Usage |
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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: |
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```python |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/Bart-MNLI-CNN_news") |
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model = AutoModelForSequenceClassification.from_pretrained("AyoubChLin/Bart-MNLI-CNN_news") |
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classifier = pipeline( |
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"zero-shot-classification", |
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model=model, |
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tokenizer=tokenizer, |
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device=0 |
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) |
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``` |
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## Acknowledgments |
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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. |
