Update README.md (#5)

- Update README.md (4333b95e7db378106ace36e8a25fea88edd871fe)

README.md

@@ -69,7 +69,7 @@ language:
 - my
 - ne
 - nl
-- no
+- 'no'
 - ny
 - pa
 - pl
@@ -108,65 +108,78 @@ language:
 tags:
 - text2text-generation
 widget:
-- text: "<table>
-  <tr>
-<th>Name</th>
-<th>Explanation</th>
-<th>Example models</th>
-</tr>
-<tr>
-<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> 
-<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
-<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
-</tr>
-<tr>
-<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> 
-<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
-<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>
-</tr>
-<tr>
-<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> 
-<td>xP3 + our evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
-<td></td>
-</tr>
-<tr>
-<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> 
-<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
-<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
-</tr>
-<tr>
-<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> 
-<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
-<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>
-</tr>
-</table>
-Which dataset has the most tasks?"
-  example_title: "en-en struct-to-text"
-- text: "Life is beautiful! Translate to Mongolian."
-  example_title: "mn-en translation"
-- text: "Le mot japonais «憂鬱» veut dire quoi en Odia?"
-  example_title: "jp-or-fr translation"
-- text: "Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte stell die Frage auf Norwegisch."
-  example_title: "de-nb quiz"
-- 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.
-What are the keywords in Chinese?"
-  example_title: "zh-en keywords"  
-- 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?"
-  example_title: "zh-en sentiment"
-- 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？"
-  example_title: "zh-zh sentiment"
-- text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
-  example_title: "vi-en query"
-- text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
-  example_title: "fr-fr query"
-- text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
-  example_title: "te-en qa"
-- text: "Why is the sky blue?"
-  example_title: "en-en qa"
-- 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):"
-  example_title: "es-en fable"
-- 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):"
-  example_title: "hi-en fable"
+- text: >-
+    <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th>
+    </tr> <tr> <td><a
+    href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>  <td>Mixture
+    of 13 training tasks in 46 languages with English prompts</td> <td><a
+    href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a
+    href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr>
+    <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> 
+    <td>Mixture of 13 training tasks in 46 languages with prompts in 20
+    languages (machine-translated from English)</td> <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> </tr>
+    <tr> <td><a
+    href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> 
+    <td>xP3 + our evaluation datasets adding an additional 3 tasks for a total
+    of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr>
+    <td><a
+    href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> 
+    <td><a
+    href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a>
+    processed version of xP3</td> <td><a
+    href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr>
+    <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> 
+    <td>Repreprocessed version of the English-only <a
+    href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training
+    tasks</td> <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> </tr>
+    </table> Which dataset has the most tasks?
+  example_title: en-en struct-to-text
+- text: Life is beautiful! Translate to Mongolian.
+  example_title: mn-en translation
+- text: Le mot japonais «憂鬱» veut dire quoi en Odia?
+  example_title: jp-or-fr translation
+- text: >-
+    Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte
+    stell die Frage auf Norwegisch.
+  example_title: de-nb quiz
+- 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. What are the keywords in Chinese?
+  example_title: zh-en keywords
+- 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?
+  example_title: zh-en sentiment
+- 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？
+  example_title: zh-zh sentiment
+- text: Suggest at least five related search terms to "Mạng neural nhân tạo".
+  example_title: vi-en query
+- text: >-
+    Proposez au moins cinq mots clés concernant «Réseau de neurones
+    artificiels».
+  example_title: fr-fr query
+- text: Explain in a sentence in Telugu what is backpropagation in neural networks.
+  example_title: te-en qa
+- text: Why is the sky blue?
+  example_title: en-en qa
+- 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):
+  example_title: es-en fable
+- 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):
+  example_title: hi-en fable
 model-index:
 - name: mt0-xxl
   results:
@@ -268,7 +281,7 @@ model-index:
       revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
     metrics:
     - type: Accuracy
-      value: 43.0
+      value: 43
   - task:
       type: Natural language inference
     dataset:
@@ -345,7 +358,7 @@ model-index:
       revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
     metrics:
     - type: Accuracy
-      value: 59.0
+      value: 59
   - task:
       type: Natural language inference
     dataset:
@@ -472,7 +485,7 @@ model-index:
     dataset:
       type: story_cloze
       name: StoryCloze (2016)
-      config: "2016"
+      config: '2016'
       split: validation
       revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
     metrics:
@@ -488,7 +501,7 @@ model-index:
       revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
     metrics:
     - type: Accuracy
-      value: 93.0
+      value: 93
   - task:
       type: Sentence completion
     dataset:
@@ -499,7 +512,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 79.0
+      value: 79
   - task:
       type: Sentence completion
     dataset:
@@ -510,7 +523,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 81.0
+      value: 81
   - task:
       type: Sentence completion
     dataset:
@@ -521,7 +534,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 92.0
+      value: 92
   - task:
       type: Sentence completion
     dataset:
@@ -532,7 +545,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 90.0
+      value: 90
   - task:
       type: Sentence completion
     dataset:
@@ -543,7 +556,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 59.0
+      value: 59
   - task:
       type: Sentence completion
     dataset:
@@ -554,7 +567,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 79.0
+      value: 79
   - task:
       type: Sentence completion
     dataset:
@@ -565,7 +578,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 84.0
+      value: 84
   - task:
       type: Sentence completion
     dataset:
@@ -576,7 +589,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 77.0
+      value: 77
   - task:
       type: Sentence completion
     dataset:
@@ -587,7 +600,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 79.0
+      value: 79
   - task:
       type: Sentence completion
     dataset:
@@ -598,7 +611,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 88.0
+      value: 88
   - task:
       type: Sentence completion
     dataset:
@@ -609,7 +622,7 @@ model-index:
       revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
     metrics:
     - type: Accuracy
-      value: 89.0
+      value: 89
   - task:
       type: Sentence completion
     dataset:
@@ -720,6 +733,7 @@ model-index:
     metrics:
     - type: Accuracy
       value: 93.85
+pipeline_tag: text2text-generation
 ---
 
 ![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true)