metadata

datasets:
  - bigscience/xP3
  - mc4
license: apache-2.0
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
pipeline_tag: text2text-generation
widget:
  - 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-large
    results:
      - task:
          type: Coreference resolution
        dataset:
          type: winogrande
          name: Winogrande XL (xl)
          config: xl
          split: validation
          revision: a80f460359d1e9a67c006011c94de42a8759430c
        metrics:
          - type: Accuracy
            value: 51.78
      - task:
          type: Coreference resolution
        dataset:
          type: Muennighoff/xwinograd
          name: XWinograd (en)
          config: en
          split: test
          revision: 9dd5ea5505fad86b7bedad667955577815300cee
        metrics:
          - type: Accuracy
            value: 54.8
      - task:
          type: Coreference resolution
        dataset:
          type: Muennighoff/xwinograd
          name: XWinograd (fr)
          config: fr
          split: test
          revision: 9dd5ea5505fad86b7bedad667955577815300cee
        metrics:
          - type: Accuracy
            value: 56.63
      - task:
          type: Coreference resolution
        dataset:
          type: Muennighoff/xwinograd
          name: XWinograd (jp)
          config: jp
          split: test
          revision: 9dd5ea5505fad86b7bedad667955577815300cee
        metrics:
          - type: Accuracy
            value: 53.08
      - task:
          type: Coreference resolution
        dataset:
          type: Muennighoff/xwinograd
          name: XWinograd (pt)
          config: pt
          split: test
          revision: 9dd5ea5505fad86b7bedad667955577815300cee
        metrics:
          - type: Accuracy
            value: 56.27
      - task:
          type: Coreference resolution
        dataset:
          type: Muennighoff/xwinograd
          name: XWinograd (ru)
          config: ru
          split: test
          revision: 9dd5ea5505fad86b7bedad667955577815300cee
        metrics:
          - type: Accuracy
            value: 55.56
      - task:
          type: Coreference resolution
        dataset:
          type: Muennighoff/xwinograd
          name: XWinograd (zh)
          config: zh
          split: test
          revision: 9dd5ea5505fad86b7bedad667955577815300cee
        metrics:
          - type: Accuracy
            value: 54.37
      - task:
          type: Natural language inference
        dataset:
          type: anli
          name: ANLI (r1)
          config: r1
          split: validation
          revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
        metrics:
          - type: Accuracy
            value: 33.3
      - task:
          type: Natural language inference
        dataset:
          type: anli
          name: ANLI (r2)
          config: r2
          split: validation
          revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
        metrics:
          - type: Accuracy
            value: 34.7
      - task:
          type: Natural language inference
        dataset:
          type: anli
          name: ANLI (r3)
          config: r3
          split: validation
          revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
        metrics:
          - type: Accuracy
            value: 34.75
      - task:
          type: Natural language inference
        dataset:
          type: super_glue
          name: SuperGLUE (cb)
          config: cb
          split: validation
          revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
        metrics:
          - type: Accuracy
            value: 51.79
      - task:
          type: Natural language inference
        dataset:
          type: super_glue
          name: SuperGLUE (rte)
          config: rte
          split: validation
          revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
        metrics:
          - type: Accuracy
            value: 64.26
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (ar)
          config: ar
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 42.61
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (bg)
          config: bg
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 43.94
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (de)
          config: de
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 44.18
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (el)
          config: el
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 43.94
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (en)
          config: en
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 44.26
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (es)
          config: es
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 45.34
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (fr)
          config: fr
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 42.01
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (hi)
          config: hi
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 41.89
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (ru)
          config: ru
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 42.13
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (sw)
          config: sw
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 40.08
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (th)
          config: th
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 40.8
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (tr)
          config: tr
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 41.29
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (ur)
          config: ur
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 39.88
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (vi)
          config: vi
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 41.81
      - task:
          type: Natural language inference
        dataset:
          type: xnli
          name: XNLI (zh)
          config: zh
          split: validation
          revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
        metrics:
          - type: Accuracy
            value: 40.84
      - task:
          type: Sentence completion
        dataset:
          type: story_cloze
          name: StoryCloze (2016)
          config: '2016'
          split: validation
          revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
        metrics:
          - type: Accuracy
            value: 59.49
      - task:
          type: Sentence completion
        dataset:
          type: super_glue
          name: SuperGLUE (copa)
          config: copa
          split: validation
          revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
        metrics:
          - type: Accuracy
            value: 65
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (et)
          config: et
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 56
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (ht)
          config: ht
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 62
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (id)
          config: id
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 61
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (it)
          config: it
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 63
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (qu)
          config: qu
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 57
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (sw)
          config: sw
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 54
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (ta)
          config: ta
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 62
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (th)
          config: th
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 57
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (tr)
          config: tr
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 57
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (vi)
          config: vi
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 63
      - task:
          type: Sentence completion
        dataset:
          type: xcopa
          name: XCOPA (zh)
          config: zh
          split: validation
          revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
        metrics:
          - type: Accuracy
            value: 58
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (ar)
          config: ar
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 56.59
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (es)
          config: es
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 55.72
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (eu)
          config: eu
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 52.61
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (hi)
          config: hi
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 52.15
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (id)
          config: id
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 54.67
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (my)
          config: my
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 51.69
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (ru)
          config: ru
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 53.74
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (sw)
          config: sw
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 55.53
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (te)
          config: te
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 57.18
      - task:
          type: Sentence completion
        dataset:
          type: Muennighoff/xstory_cloze
          name: XStoryCloze (zh)
          config: zh
          split: validation
          revision: 8bb76e594b68147f1a430e86829d07189622b90d
        metrics:
          - type: Accuracy
            value: 59.5

Model Summary
Use
Limitations
Training
Evaluation
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:

Multitask finetuned on xP3. Recommended for prompting in English.
Parameters	300M	580M	1.2B	3.7B	13B	560M	1.1B	1.7B	3B	7.1B	176B
Finetuned Model	mt0-small	mt0-base	mt0-large	mt0-xl	mt0-xxl	bloomz-560m	bloomz-1b1	bloomz-1b7	bloomz-3b	bloomz-7b1	bloomz
Multitask finetuned on xP3mt. Recommended for prompting in non-English.
Finetuned Model					mt0-xxl-mt					bloomz-7b1-mt	bloomz-mt
Multitask finetuned on P3. Released for research purposes only. Strictly inferior to above models!
Finetuned Model					mt0-xxl-p3					bloomz-7b1-p3	bloomz-p3
Original pretrained checkpoints. Not recommended.
Pretrained Model	mt5-small	mt5-base	mt5-large	mt5-xl	mt5-xxl	bloom-560m	bloom-1b1	bloom-1b7	bloom-3b	bloom-7b1	bloom

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

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

checkpoint = "bigscience/mt0-large"

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]))

GPU

Click to expand

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

checkpoint = "bigscience/mt0-large"

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]))

GPU in 8bit

Click to expand

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

checkpoint = "bigscience/mt0-large"

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]))

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-large, also refer to the config.json file
Finetuning steps: 25000
Finetuning tokens: 4.62 billion
Precision: bfloat16

Hardware

TPUs: TPUv4-64

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.

Citation

@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}
}

bigscience
/

mt0-large

Table of Contents

Model Summary

Use

Intended use

How to use

CPU

GPU

GPU in 8bit

Limitations

Training

Model

Hardware

Software

Evaluation

Citation