metadata

license: cc-by-nc-sa-4.0
datasets:
  - wi_locness
  - matejklemen/falko_merlin
  - paws
  - paws-x
  - asset
language:
  - en
  - de
  - es
  - ar
  - ja
  - ko
  - zh
metrics:
  - bleu
  - rouge
  - sari
  - accuracy
library_name: transformers
widget:
  - text: >-
      Umschreiben sie den satz: When I grow up, I start to understand what he
      said is quite right.
    example_title: GEC (de|en)
  - text: >-
      문장의 간단한 버전 작성: Cuando se pueden mantener tasas de flujo comparables, los
      resultados son altos.
    example_title: Simplification (ko|es)
  - text: 'Paraphrase this: いちごは物語を紹介し、読者をイベントに導くと彼は言った。'
    example_title: Paraphrase (en|ja)
pipeline_tag: text2text-generation

Model Card for mEdIT-xl

The medit-xl model was obtained by fine-tuning the MBZUAI/bactrian-x-llama-7b-lora model on the mEdIT dataset.

Paper: mEdIT: Multilingual Text Editing via Instruction Tuning

Authors: Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar

Model Details

Model Description

Language(s) (NLP): Arabic, Chinese, English, German, Japanese, Korean, Spanish
Finetuned from model: MBZUAI/bactrian-x-llama-7b-lora

Model Sources

Repository: https://github.com/vipulraheja/medit
Paper: https://arxiv.org/abs/2402.16472v1

How to use

Given an edit instruction and an original text, our model can generate the edited version of the text.

Specifically, our models support both multi-lingual and cross-lingual text revision. Note that the input and output texts are always in the same language. The monolingual vs. cross-lingual setting is determined by comparing the language of the edit instruction in relation to the language of the input text.

Instruction format

Adherence to the following instruction format is essential; failure to do so may result in the model producing less-than-ideal results.

instruction_tokens = [
    "Instruction",
    "Anweisung",
    ...
]

input_tokens = [
    "Input",
    "Aporte",
    ...
]

output_tokens = [
    "Output",
    "Produzione",
    ...
]

task_descriptions = [
    "Fix grammatical errors in this sentence",  # <-- GEC task
    "Umschreiben Sie den Satz",                 # <-- Paraphrasing
    ...
]

The entire list of possible instructions, input/output tokens, and task descriptions can be found in the Appendix of our paper.

prompt_template = """### <instruction_token>:\n<task_description>\n### <input_token>:\n<input>\n### <output_token>:\n\n"""

Note that the tokens and the task description need not be in the language of the input (in the case of cross-lingual revision).

Run the model

Make sure you have the following libraries installed:

- peft
- protobuf
- sentencepiece
- tokenizers
- torch
- transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "grammarly/medit-xl"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

# English GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nI has small cat ,\n### 出力:\n\n'

inputs = tokenizer(prompt, return_tensors='pt')

outputs = model.generate(**inputs, max_new_tokens=20)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# --> I have a small cat ,

# German GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nIch haben eines kleines Katze ,\n### 出力:\n\n'

# ...
# --> Ich habe eine kleine Katze ,

Software

https://github.com/vipulraheja/medit

Citation

BibTeX:

@article{raheja2023medit,
      title={mEdIT: mEdIT: Multilingual Text Editing via Instruction Tuning}, 
      author={Vipul Raheja and Dimitris Alikaniotis and Vivek Kulkarni and Bashar Alhafni and Dhruv Kumar},
      year={2024},
      eprint={2402.16472v1},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

APA: Raheja, V., Alikaniotis, D., Kulkarni, V., Alhafni, B., & Kumar, D. (2024). MEdIT: Multilingual Text Editing via Instruction Tuning. ArXiv. /abs/2402.16472