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add zero shot pipeline tags & examples (#7983)
* add zero shot pipeline tags * rm default and fix yaml format * rm DS_Store * add bart large default * don't add more typos Co-authored-by: Julien Chaumond <chaumond@gmail.com> * add multiple multilingual examples * improve multilingual examples for single-label Co-authored-by: Julien Chaumond <chaumond@gmail.com>
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@ -2,4 +2,7 @@
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license: mit
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license: mit
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thumbnail: https://huggingface.co/front/thumbnails/facebook.png
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thumbnail: https://huggingface.co/front/thumbnails/facebook.png
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pipeline_tag: zero-shot-classification
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pipeline_tag: zero-shot-classification
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widget:
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- text: "November 3 is weeks away and Biden's lead is only growing."
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labels: "politics, economics, public health, elections"
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---
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---
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@ -5,8 +5,7 @@ tags:
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- pytorch
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- pytorch
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datasets:
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datasets:
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- yahoo-answers
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- yahoo-answers
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widget:
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pipeline_tag: zero-shot-classification
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- text: "Who are you voting for in 2020? <sep> This text is about politics."
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---
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---
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# bart-lage-mnli-yahoo-answers
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# bart-lage-mnli-yahoo-answers
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@ -5,11 +5,17 @@ tags:
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- pytorch
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- pytorch
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- tensorflow
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- tensorflow
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datasets:
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datasets:
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- mnli
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- multi_nli
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- xnli
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- xnli
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widget:
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- text: "За кого вы голосуете в 2020 году? <sep> This text is about politique."
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license: mit
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license: mit
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pipeline_tag: zero-shot-classification
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widget:
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- text: "За кого вы голосуете в 2020 году?"
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labels: "politique étrangère, Europe, élections, affaires, politique"
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- text: "لمن تصوت في 2020؟"
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labels: "السياسة الخارجية, أوروبا, الانتخابات, الأعمال, السياسة"
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- text: "2020'de kime oy vereceksiniz?"
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labels: "dış politika, Avrupa, seçimler, ticaret, siyaset"
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---
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---
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# xlm-roberta-large-xnli
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# xlm-roberta-large-xnli
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@ -115,4 +121,3 @@ This model was pre-trained on set of 100 languages, as described in
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MNLI train set and the XNLI validation and test sets. Finally, it was trained for one additional epoch on only XNLI
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MNLI train set and the XNLI validation and test sets. Finally, it was trained for one additional epoch on only XNLI
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data where the translations for the premise and hypothesis are shuffled such that the premise and hypothesis for
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data where the translations for the premise and hypothesis are shuffled such that the premise and hypothesis for
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each example come from the same original English example but the premise and hypothesis are of different languages.
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each example come from the same original English example but the premise and hypothesis are of different languages.
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@ -4,6 +4,7 @@ datasets:
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tags:
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tags:
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- distilbart
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- distilbart
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- distilbart-mnli
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- distilbart-mnli
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pipeline_tag: zero-shot-classification
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---
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---
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# DistilBart-MNLI
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# DistilBart-MNLI
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@ -4,6 +4,7 @@ datasets:
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tags:
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tags:
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- distilbart
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- distilbart
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- distilbart-mnli
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- distilbart-mnli
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pipeline_tag: zero-shot-classification
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---
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---
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# DistilBart-MNLI
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# DistilBart-MNLI
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@ -4,6 +4,7 @@ datasets:
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tags:
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tags:
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- distilbart
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- distilbart
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- distilbart-mnli
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- distilbart-mnli
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pipeline_tag: zero-shot-classification
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---
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---
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# DistilBart-MNLI
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# DistilBart-MNLI
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@ -4,6 +4,7 @@ datasets:
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tags:
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tags:
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- distilbart
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- distilbart
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- distilbart-mnli
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- distilbart-mnli
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pipeline_tag: zero-shot-classification
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---
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---
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# DistilBart-MNLI
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# DistilBart-MNLI
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