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@@ -69,7 +69,7 @@ language:
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  - my
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  - ne
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  - nl
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- - no
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  - ny
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  - pa
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  - pl
@@ -108,65 +108,78 @@ language:
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  tags:
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  - text2text-generation
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  widget:
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- - text: "<table>
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- <tr>
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- <th>Name</th>
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- <th>Explanation</th>
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- <th>Example models</th>
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- </tr>
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- <tr>
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- <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
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- <td>Mixture of 13 training tasks in 46 languages with English prompts</td>
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- <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
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- </tr>
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- <tr>
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- <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
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- <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
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- <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
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- </tr>
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- <tr>
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- <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
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- <td>xP3 + our evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
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- <td></td>
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- </tr>
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- <tr>
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- <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
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- <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
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- <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
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- </tr>
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- <tr>
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- <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
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- <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
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- <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
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- </tr>
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- </table>
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- Which dataset has the most tasks?"
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- example_title: "en-en struct-to-text"
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- - text: "Life is beautiful! Translate to Mongolian."
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- example_title: "mn-en translation"
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- - text: "Le mot japonais «憂鬱» veut dire quoi en Odia?"
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- example_title: "jp-or-fr translation"
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- - text: "Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte stell die Frage auf Norwegisch."
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- example_title: "de-nb quiz"
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- - text: "We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages.
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- What are the keywords in Chinese?"
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- example_title: "zh-en keywords"
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- - text: "一个传奇的开端,一个不灭的神话,这不仅仅是一部电影,而是作为一个走进新时代的标签,永远彪炳史册。Would you rate the previous review as positive, neutral or negative?"
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- example_title: "zh-en sentiment"
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- - text: "一个传奇的开端,一个不灭的神话,这不仅仅是一部电影,而是作为一个走进新时代的标签,永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?"
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- example_title: "zh-zh sentiment"
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- - text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
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- example_title: "vi-en query"
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- - text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
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- example_title: "fr-fr query"
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- - text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
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- example_title: "te-en qa"
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- - text: "Why is the sky blue?"
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- example_title: "en-en qa"
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- - text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):"
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- example_title: "es-en fable"
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- - text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):"
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- example_title: "hi-en fable"
 
 
 
 
 
 
 
 
 
 
 
 
 
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  model-index:
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  - name: mt0-xxl
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  results:
@@ -268,7 +281,7 @@ model-index:
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  revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
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  metrics:
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  - type: Accuracy
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- value: 43.0
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  - task:
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  type: Natural language inference
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  dataset:
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  revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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  metrics:
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  - type: Accuracy
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- value: 59.0
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  - task:
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  type: Natural language inference
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  dataset:
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  dataset:
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  type: story_cloze
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  name: StoryCloze (2016)
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- config: "2016"
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  split: validation
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  revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
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  metrics:
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  revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
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  metrics:
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  - type: Accuracy
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- value: 93.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 79.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 81.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 92.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 90.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 59.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 79.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 84.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 77.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 79.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 88.0
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  - task:
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  type: Sentence completion
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  dataset:
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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- value: 89.0
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  - task:
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  type: Sentence completion
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  dataset:
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  metrics:
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  - type: Accuracy
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  value: 93.85
 
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  ---
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  ![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true)
 
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  - my
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  - ne
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  - nl
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+ - 'no'
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  - ny
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  - pa
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  - pl
 
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  tags:
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  - text2text-generation
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  widget:
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+ - text: >-
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+ <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th>
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+ </tr> <tr> <td><a
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+ href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture
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+ of 13 training tasks in 46 languages with English prompts</td> <td><a
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+ href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a
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+ href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr>
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+ <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
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+ <td>Mixture of 13 training tasks in 46 languages with prompts in 20
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+ languages (machine-translated from English)</td> <td><a
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+ href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a
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+ href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr>
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+ <tr> <td><a
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+ href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
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+ <td>xP3 + our evaluation datasets adding an additional 3 tasks for a total
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+ of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr>
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+ <td><a
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+ href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
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+ <td><a
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+ href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a>
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+ processed version of xP3</td> <td><a
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+ href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr>
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+ <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
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+ <td>Repreprocessed version of the English-only <a
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+ href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training
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+ tasks</td> <td><a
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+ href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a
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+ href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr>
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+ </table> Which dataset has the most tasks?
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+ example_title: en-en struct-to-text
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+ - text: Life is beautiful! Translate to Mongolian.
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+ example_title: mn-en translation
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+ - text: Le mot japonais «憂鬱» veut dire quoi en Odia?
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+ example_title: jp-or-fr translation
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+ - text: >-
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+ Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte
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+ stell die Frage auf Norwegisch.
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+ example_title: de-nb quiz
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+ - text: >-
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+ We present BLOOMZ & mT0, a family of models capable of following human
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+ instructions in dozens of languages zero-shot. We finetune BLOOM & mT5
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+ pretrained multilingual language models on our crosslingual task mixture
153
+ (xP3) and find our resulting models capable of crosslingual generalization
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+ to unseen tasks & languages. What are the keywords in Chinese?
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+ example_title: zh-en keywords
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+ - text: >-
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+ 一个传奇的开端,一个不灭的神话,这不仅仅是一部电影,而是作为一个走进新时代的标签,永远彪炳史册。Would you rate the previous
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+ review as positive, neutral or negative?
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+ example_title: zh-en sentiment
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+ - text: 一个传奇的开端,一个不灭的神话,这不仅仅是一部电影,而是作为一个走进新时代的标签,永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
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+ example_title: zh-zh sentiment
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+ - text: Suggest at least five related search terms to "Mạng neural nhân tạo".
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+ example_title: vi-en query
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+ - text: >-
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+ Proposez au moins cinq mots clés concernant «Réseau de neurones
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+ artificiels».
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+ example_title: fr-fr query
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+ - text: Explain in a sentence in Telugu what is backpropagation in neural networks.
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+ example_title: te-en qa
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+ - text: Why is the sky blue?
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+ example_title: en-en qa
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+ - text: >-
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+ Write a fairy tale about a troll saving a princess from a dangerous dragon.
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+ The fairy tale is a masterpiece that has achieved praise worldwide and its
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+ moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
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+ example_title: es-en fable
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+ - text: >-
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+ Write a fable about wood elves living in a forest that is suddenly invaded
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+ by ogres. The fable is a masterpiece that has achieved praise worldwide and
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+ its moral is "Violence is the last refuge of the incompetent". Fable (in
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+ Hindi):
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+ example_title: hi-en fable
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  model-index:
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  - name: mt0-xxl
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  results:
 
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  revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
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  metrics:
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  - type: Accuracy
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+ value: 43
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  - task:
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  type: Natural language inference
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  dataset:
 
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  revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
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  metrics:
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  - type: Accuracy
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+ value: 59
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  - task:
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  type: Natural language inference
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  dataset:
 
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  dataset:
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  type: story_cloze
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  name: StoryCloze (2016)
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+ config: '2016'
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  split: validation
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  revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
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  metrics:
 
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  revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
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  metrics:
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  - type: Accuracy
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+ value: 93
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 79
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 81
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 92
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 90
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 59
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 79
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 84
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 77
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 79
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 88
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  - task:
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  type: Sentence completion
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  dataset:
 
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  revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
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  metrics:
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  - type: Accuracy
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+ value: 89
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  - task:
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  type: Sentence completion
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  dataset:
 
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  metrics:
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  - type: Accuracy
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  value: 93.85
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+ pipeline_tag: text2text-generation
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  ---
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  ![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true)