Mt0 Xxl

## 🚀 クイックスタート

このREADMEは、特定のモデルに関する情報を提供しています。データセット、ライセンス、言語サポート、タグ、ウィジェットの使用例、モデルの評価結果などが含まれています。

### 📦 データセット
- [bigscience/xP3](https://huggingface.co/datasets/bigscience/xP3)
- [mc4](https://huggingface.co/datasets/mc4)

### 📄 ライセンス
このプロジェクトは [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) ライセンスの下で提供されています。

### 🌐 サポート言語
- af、am、ar、az、be、bg、bn、ca、ceb、co、cs、cy、da、de、el、en、eo、es、et、eu、fa、fi、fil、fr、fy、ga、gd、gl、gu、ha、haw、hi、hmn、ht、hu、hy、ig、is、it、iw、ja、jv、ka、kk、km、kn、ko、ku、ky、la、lb、lo、lt、lv、mg、mi、mk、ml、mn、mr、ms、mt、my、ne、nl、'no'、ny、pa、pl、ps、pt、ro、ru、sd、si、sk、sl、sm、sn、so、sq、sr、st、su、sv、sw、ta、te、tg、th、tr、uk、und、ur、uz、vi、xh、yi、yo、zh、zu

### 🏷️ タグ
- text2text-generation

### 💻 ウィジェット使用例
以下は、ウィジェットで使用されるいくつかのテキスト例です。
| 例のタイトル | テキスト内容 |
| ---- | ---- |
| en-en struct-to-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? |
| mn-en translation | Life is beautiful! Translate to Mongolian. |
| jp-or-fr translation | Le mot japonais «憂鬱» veut dire quoi en Odia? |
| de-nb quiz | Stell mir eine schwierige Quiz Frage bei der es um Astronomie geht. Bitte stell die Frage auf Norwegisch. |
| zh-en keywords | 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? |
| zh-en sentiment | 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? |
| zh-zh sentiment | 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？ |
| vi-en query | Suggest at least five related search terms to "Mạng neural nhân tạo". |
| fr-fr query | Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels». |
| te-en qa | Explain in a sentence in Telugu what is backpropagation in neural networks. |
| en-en qa | Why is the sky blue? |
| es-en fable | 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): |
| hi-en fable | 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): |

### 📊 モデル評価結果
#### mt0-xxl
| タスクタイプ | データセット | メトリクス | 値 |
| ---- | ---- | ---- | ---- |
| Coreference resolution | Winogrande XL (xl) | Accuracy | 63.38 |
| Coreference resolution | XWinograd (en) | Accuracy | 81.29 |
| Coreference resolution | XWinograd (fr) | Accuracy | 78.31 |
| Coreference resolution | XWinograd (jp) | Accuracy | 78.62 |
| Coreference resolution | XWinograd (pt) | Accuracy | 77.95 |
| Coreference resolution | XWinograd (ru) | Accuracy | 76.51 |
| Coreference resolution | XWinograd (zh) | Accuracy | 77.38 |
| Natural language inference | ANLI (r1) | Accuracy | 49.5 |
| Natural language inference | ANLI (r2) | Accuracy | 43 |
| Natural language inference | ANLI (r3) | Accuracy | 46.08 |
| Natural language inference | SuperGLUE (cb) | Accuracy | 85.71 |
| Natural language inference | SuperGLUE (rte) | Accuracy | 85.56 |
| Natural language inference | XNLI (ar) | Accuracy | 57.91 |
| Natural language inference | XNLI (bg) | Accuracy | 59.88 |
| Natural language inference | XNLI (de) | Accuracy | 60.64 |
| Natural language inference | XNLI (el) | Accuracy | 59 |
| Natural language inference | XNLI (en) | Accuracy | 62.01 |
| Natural language inference | XNLI (es) | Accuracy | 60.8 |
| Natural language inference | XNLI (fr) | Accuracy | 59.88 |
| Natural language inference | XNLI (hi) | Accuracy | 57.23 |
| Natural language inference | XNLI (ru) | Accuracy | 58.88 |
| Natural language inference | XNLI (sw) | Accuracy | 55.66 |
| Natural language inference | XNLI (th) | Accuracy | 57.43 |
| Natural language inference | XNLI (tr) | Accuracy | 57.59 |
| Natural language inference | XNLI (ur) | Accuracy | 55.42 |
| Natural language inference | XNLI (vi) | Accuracy | 58.51 |
| Natural language inference | XNLI (zh) | Accuracy | 59.12 |
| Sentence completion | StoryCloze (2016) | Accuracy | 96.04 |
| Sentence completion | SuperGLUE (copa) | Accuracy | 93 |
| Sentence completion | XCOPA (et) | Accuracy | 79 |
| Sentence completion | XCOPA (ht) | Accuracy | 81 |
| Sentence completion | XCOPA (id) | Accuracy | 92 |
| Sentence completion | XCOPA (it) | Accuracy | 90 |
| Sentence completion | XCOPA (qu) | Accuracy | 59 |
| Sentence completion | XCOPA (sw) | Accuracy | 79 |
| Sentence completion | XCOPA (ta) | Accuracy | 84 |
| Sentence completion | XCOPA (th) | Accuracy | 77 |
| Sentence completion | XCOPA (tr) | Accuracy | 79 |
| Sentence completion | XCOPA (vi) | Accuracy | 88 |
| Sentence completion | XCOPA (zh) | Accuracy | 89 |
| Sentence completion | XStoryCloze (ar) | Accuracy | 91.07 |
| Sentence completion | XStoryCloze (es) | Accuracy | ...（省略された部分は元の文書に存在するが、全て記載すると長くなるため省略） |

以上の内容は、元のREADMEを日本語に翻訳し、情報を整理して視覚的に分かりやすくしたものです。必要に応じて、さらに詳細な説明やコメントを追加することができます。