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--- |
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language: en |
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tags: |
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- text-classification |
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- pytorch |
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- tensorflow |
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datasets: |
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- ag_news |
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license: mit |
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widget: |
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- text: "Armed conflict has been a near-constant policial and economic burden." |
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- text: "Tom Brady won his seventh Super Bowl last night." |
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- text: "Dow falls more than 100 points after disappointing jobs data" |
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- text: "A new moon has been discovered in Jupter's orbit." |
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--- |
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# distilbert-base-uncased-agnews-student |
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## Model Description |
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This model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using [this |
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script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation). |
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It is the result of the demo notebook |
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[here](https://colab.research.google.com/drive/1mjBjd0cR8G57ZpsnFCS3ngGyo5nCa9ya?usp=sharing), where more details |
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about the model can be found. |
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- Teacher model: [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) |
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- Teacher hypothesis template: `"This text is about {}."` |
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## Intended Usage |
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The model can be used like any other model trained on AG's News, but will likely not perform as well as a model |
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trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model |
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can be distilled to a more efficient student. |
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