
![xmtf](https://github.com/bigscience-workshop/xmtf/blob/master/xmtf_banner.png?raw=true)

#  Table of Contents

1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)

# Model Summary

> 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.

- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
- **Languages:** Refer to [mc4](https://huggingface.co/datasets/mc4) for pretraining & [xP3](https://huggingface.co/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**

<div class="max-w-full overflow-auto">
<table>
  <tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
</tr>
<tr>
<td>Parameters</td>
<td>300M</td>
<td>580M</td>
<td>1.2B</td>
<td>3.7B</td>
<td>13B</td>
<td>560M</td>
<td>1.1B</td>
<td>1.7B</td>
<td>3B</td>
<td>7.1B</td>
<td>176B</td>
</tr>
<tr>
<td>Finetuned Model</td>
<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>  
<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
</tr>
  <tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
</tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
</tr>
<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
<tr>
<td>Pretrained Model</td>
<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>  
<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
</tr>
</table>
</div>



# Use

## Intended use

We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper: 
- 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?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- 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):
- Explain in a sentence in Telugu what is backpropagation in neural networks.

**Feel free to share your generations in the Community tab!**

## How to use

### CPU

<details>
<summary> Click to expand </summary>

```python
# pip install -q transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

checkpoint = "bigscience/mt0-xl"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)

inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

</details>

### GPU

<details>
<summary> Click to expand </summary>

```python
# pip install -q transformers accelerate
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

checkpoint = "bigscience/mt0-xl"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")

inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

</details>

### GPU in 8bit

<details>
<summary> Click to expand </summary>

```python
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

checkpoint = "bigscience/mt0-xl"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)

inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

</details>

<!-- Necessary for whitespace -->
###

# Limitations

**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".

# Training

## Model

- **Architecture:** Same as [mt5-xl](https://huggingface.co/google/mt5-xl), also refer to the `config.json` file
- **Finetuning steps:** 10000
- **Finetuning tokens:** 1.85 billion
- **Precision:** bfloat16

## Hardware

- **TPUs:** TPUv4-128

## Software

- **Orchestration:** [T5X](https://github.com/google-research/t5x)
- **Neural networks:** [Jax](https://github.com/google/jax)

# Evaluation

We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.

# Citation
```bibtex
@misc{muennighoff2022crosslingual,
      title={Crosslingual Generalization through Multitask Finetuning}, 
      author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
      year={2022},
      eprint={2211.01786},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["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"], "license": "apache-2.0", "datasets": ["bigscience/xP3", "mc4"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\u4e00\u4e2a\u4f20\u5947\u7684\u5f00\u7aef\uff0c\u4e00\u4e2a\u4e0d\u706d\u7684\u795e\u8bdd\uff0c\u8fd9\u4e0d\u4ec5\u4ec5\u662f\u4e00\u90e8\u7535\u5f71\uff0c\u800c\u662f\u4f5c\u4e3a\u4e00\u4e2a\u8d70\u8fdb\u65b0\u65f6\u4ee3\u7684\u6807\u7b7e\uff0c\u6c38\u8fdc\u5f6a\u70b3\u53f2\u518c\u3002Would you rate the previous review as positive, neutral or negative?", "example_title": "zh-en sentiment"}, {"text": "\u4e00\u4e2a\u4f20\u5947\u7684\u5f00\u7aef\uff0c\u4e00\u4e2a\u4e0d\u706d\u7684\u795e\u8bdd\uff0c\u8fd9\u4e0d\u4ec5\u4ec5\u662f\u4e00\u90e8\u7535\u5f71\uff0c\u800c\u662f\u4f5c\u4e3a\u4e00\u4e2a\u8d70\u8fdb\u65b0\u65f6\u4ee3\u7684\u6807\u7b7e\uff0c\u6c38\u8fdc\u5f6a\u70b3\u53f2\u518c\u3002\u4f60\u8ba4\u4e3a\u8fd9\u53e5\u8bdd\u7684\u7acb\u573a\u662f\u8d5e\u626c\u3001\u4e2d\u7acb\u8fd8\u662f\u6279\u8bc4\uff1f", "example_title": "zh-zh sentiment"}, {"text": "Suggest at least five related search terms to \"M\u1ea1ng neural nh\u00e2n t\u1ea1o\".", "example_title": "vi-en query"}, {"text": "Proposez au moins cinq mots cl\u00e9s concernant \u00abR\u00e9seau de neurones artificiels\u00bb.", "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"}], "duplicated_from": "bigscience/mt0-xl", "model-index": [{"name": "mt0-xl", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"name": "Winogrande XL (xl)", "type": "winogrande", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 52.49}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (en)", "type": "Muennighoff/xwinograd", "config": "en", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 61.89}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (fr)", "type": "Muennighoff/xwinograd", "config": "fr", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 59.04}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (jp)", "type": "Muennighoff/xwinograd", "config": "jp", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 60.27}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (pt)", "type": "Muennighoff/xwinograd", "config": "pt", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 66.16}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (ru)", "type": "Muennighoff/xwinograd", "config": "ru", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 59.05}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (zh)", "type": "Muennighoff/xwinograd", "config": "zh", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 62.9}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "ANLI (r1)", "type": "anli", "config": "r1", "split": "validation", "revision": "9dbd830a06fea8b1c49d6e5ef2004a08d9f45094"}, "metrics": [{"type": "Accuracy", "value": 38.2}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "ANLI (r2)", "type": "anli", "config": "r2", "split": "validation", "revision": "9dbd830a06fea8b1c49d6e5ef2004a08d9f45094"}, "metrics": [{"type": "Accuracy", "value": 34.8}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "ANLI (r3)", "type": "anli", "config": "r3", "split": "validation", "revision": "9dbd830a06fea8b1c49d6e5ef2004a08d9f45094"}, "metrics": [{"type": "Accuracy", "value": 39}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "SuperGLUE (cb)", "type": "super_glue", "config": "cb", "split": "validation", "revision": "9e12063561e7e6c79099feb6d5a493142584e9e2"}, "metrics": [{"type": "Accuracy", "value": 85.71}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "SuperGLUE (rte)", "type": "super_glue", "config": "rte", "split": "validation", "revision": "9e12063561e7e6c79099feb6d5a493142584e9e2"}, "metrics": [{"type": "Accuracy", "value": 78.7}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (ar)", "type": "xnli", "config": "ar", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 51.85}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (bg)", "type": "xnli", "config": "bg", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 54.18}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (de)", "type": "xnli", "config": "de", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 54.78}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (el)", "type": "xnli", "config": "el", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 53.78}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (en)", "type": "xnli", "config": "en", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 56.83}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (es)", "type": "xnli", "config": "es", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 54.78}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (fr)", "type": "xnli", "config": "fr", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 54.22}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (hi)", "type": "xnli", "config": "hi", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 50.24}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (ru)", "type": "xnli", "config": "ru", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 53.09}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (sw)", "type": "xnli", "config": "sw", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 49.6}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (th)", "type": "xnli", "config": "th", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 52.13}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (tr)", "type": "xnli", "config": "tr", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 50.56}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (ur)", "type": "xnli", "config": "ur", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 47.91}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (vi)", "type": "xnli", "config": "vi", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 53.21}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (zh)", "type": "xnli", "config": "zh", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 50.64}]}, {"task": {"type": "Program synthesis"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval", "config": "None", "split": "test", "revision": "e8dc562f5de170c54b5481011dd9f4fa04845771"}, "metrics": [{"type": "Pass@1", "value": 0}, {"type": "Pass@10", "value": 0}, {"type": "Pass@100", "value": 0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "StoryCloze (2016)", "type": "story_cloze", "config": "2016", "split": "validation", "revision": "e724c6f8cdf7c7a2fb229d862226e15b023ee4db"}, "metrics": [{"type": "Accuracy", "value": 79.1}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "SuperGLUE (copa)", "type": "super_glue", "config": "copa", "split": "validation", "revision": "9e12063561e7e6c79099feb6d5a493142584e9e2"}, "metrics": [{"type": "Accuracy", "value": 72}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (et)", "type": "xcopa", "config": "et", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 70}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (ht)", "type": "xcopa", "config": "ht", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 66}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (id)", "type": "xcopa", "config": "id", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 71}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (it)", "type": "xcopa", "config": "it", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 70}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (qu)", "type": "xcopa", "config": "qu", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 56}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (sw)", "type": "xcopa", "config": "sw", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 53}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (ta)", "type": "xcopa", "config": "ta", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 64}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (th)", "type": "xcopa", "config": "th", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 60}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (tr)", "type": "xcopa", "config": "tr", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 58}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (vi)", "type": "xcopa", "config": "vi", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 68}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (zh)", "type": "xcopa", "config": "zh", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 65}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (ar)", "type": "Muennighoff/xstory_cloze", "config": "ar", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 70.09}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (es)", "type": "Muennighoff/xstory_cloze", "config": "es", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 77.17}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (eu)", "type": "Muennighoff/xstory_cloze", "config": "eu", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 69.03}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (hi)", "type": "Muennighoff/xstory_cloze", "config": "hi", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 71.08}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (id)", "type": "Muennighoff/xstory_cloze", "config": "id", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 75.71}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (my)", "type": "Muennighoff/xstory_cloze", "config": "my", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 65.65}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (ru)", "type": "Muennighoff/xstory_cloze", "config": "ru", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 74.85}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (sw)", "type": "Muennighoff/xstory_cloze", "config": "sw", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 71.14}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (te)", "type": "Muennighoff/xstory_cloze", "config": "te", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 68.89}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (zh)", "type": "Muennighoff/xstory_cloze", "config": "zh", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 72.93}]}]}]}
transformers pytorch mt5 text2text-generation 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 dataset:bigscience/xP3 dataset:mc4 arxiv:2211.01786 license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #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 #dataset-bigscience/xP3 #dataset-mc4 #arxiv-2211.01786 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

!xmtf


Table of Contents
=================


1. Model Summary
2. Use
3. Limitations
4. Training
5. Evaluation
6. Citation


Model Summary
=============



> 
> 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.
> 
> 
> 


* Repository: bigscience-workshop/xmtf
* Paper: Crosslingual Generalization through Multitask Finetuning
* Point of Contact: Niklas Muennighoff
* Languages: Refer to mc4 for pretraining & xP3 for finetuning language proportions. It understands both pretraining & finetuning languages.
* BLOOMZ & mT0 Model Family:





Use
===


Intended use
------------


We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:


* 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?
* Suggest at least five related search terms to "Mạng neural nhân tạo".
* 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):
* Explain in a sentence in Telugu what is backpropagation in neural networks.


Feel free to share your generations in the Community tab!


How to use
----------


### CPU



 Click to expand 

### GPU



 Click to expand 

### GPU in 8bit



 Click to expand 

### 


Limitations
===========


Prompt Engineering: The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".


Training
========


Model
-----


* Architecture: Same as mt5-xl, also refer to the 'URL' file
* Finetuning steps: 10000
* Finetuning tokens: 1.85 billion
* Precision: bfloat16


Hardware
--------


* TPUs: TPUv4-128


Software
--------


* Orchestration: T5X
* Neural networks: Jax


Evaluation
==========


We refer to Table 7 from our paper & bigscience/evaluation-results for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.



### CPU



 Click to expand ### GPU



 Click to expand ### GPU in 8bit



 Click to expand ### 


Limitations
===========


Prompt Engineering: The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".


Training
========


Model
-----


* Architecture: Same as mt5-xl, also refer to the 'URL' file
* Finetuning steps: 10000
* Finetuning tokens: 1.85 billion
* Precision: bfloat16


Hardware
--------


* TPUs: TPUv4-128


Software
--------


* Orchestration: T5X
* Neural networks: Jax


Evaluation
==========


We refer to Table 7 from our paper & bigscience/evaluation-results for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
TAGS
#transformers #pytorch #mt5 #text2text-generation #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 #dataset-bigscience/xP3 #dataset-mc4 #arxiv-2211.01786 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### CPU



 Click to expand ### GPU



 Click to expand ### GPU in 8bit



 Click to expand ### 


Limitations
===========


Prompt Engineering: The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".


Training
========


Model
-----


* Architecture: Same as mt5-xl, also refer to the 'URL' file
* Finetuning steps: 10000
* Finetuning tokens: 1.85 billion
* Precision: bfloat16


Hardware
--------


* TPUs: TPUv4-128


Software
--------


* Orchestration: T5X
* Neural networks: Jax


Evaluation
==========


We refer to Table 7 from our paper & bigscience/evaluation-results for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
TAGS
#transformers #pytorch #mt5 #text2text-generation #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 #dataset-bigscience/xP3 #dataset-mc4 #arxiv-2211.01786 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### CPU



 Click to expand### GPU



 Click to expand### GPU in 8bit



 Click to expand### 


Limitations
===========


Prompt Engineering: The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".


Training
========


Model
-----


* Architecture: Same as mt5-xl, also refer to the 'URL' file
* Finetuning steps: 10000
* Finetuning tokens: 1.85 billion
* Precision: bfloat16


Hardware
--------


* TPUs: TPUv4-128


Software
--------


* Orchestration: T5X
* Neural networks: Jax


Evaluation
==========


We refer to Table 7 from our paper & bigscience/evaluation-results for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.