ai-forever / RuM2M100-418M

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Model's Last Updated: August 02 2024
text2text-generation

Introduction of RuM2M100-418M

Model Details of RuM2M100-418M

RuM2M100-418M model

Summary

The model corrects spelling errors and typos by bringing all the words in the text to the norm of the Russian language. The proofreader was trained based on the M2M100-418M model. An extensive dataset with “artificial” errors was taken as a training corpus: the corpus was assembled on the basis of the Russian-language Wikipedia and transcripts of Russian-language videos, then typos and spelling errors were automatically introduced into it using the functionality of the SAGE library .

Public references
Examples
Input Output
Думю ешцъа лет череа 10 ретроспективно просматривотьэ то будкетцц мне невероя тна ин те р но Думаю, еш цъа лет через 10 ретроспективно просматривать, що буде ТЦ. Мне невероятна нтерно.
Основая цель мероприятия - практическая отработка навыков по оказанию помощи гражданам, попавшим в ДТП, а также повышение и совершенствование уровня профессиональной подготовки сотрудников МЧС при проведении аварийно-спасательных работ по ликвидации последствий дорожно-транспортных проишествий, сокращение временных показателей реагирования. Основная цель мероприятия - практическая отработка навыков по оказанию помощи гражданам, попавшим в ДТП, а также повышение и совершенствование уровня профессиональной подготовки сотрудников МЧС при проведении аварийно-спасательных работ по ликвидации последствий дорожно-транспортных происшествий, сокращение временных показателей реагирования.
прийдя в МГТУ я был удивлен никого необноружив там… прийдя в МГТУ я был удивлен никого не обнаружив там...
Metrics
Quality

Below are automatic metrics for determining the correctness of the spell checkers. We compare our solution with both open automatic spell checkers and the ChatGPT family of models on all four available datasets:

  • RUSpellRU : texts collected from ( LiveJournal ), with manually corrected typos and errors;
  • MultidomainGold : examples from 7 text sources, including the open web, news, social media, reviews, subtitles, policy documents and literary works;
  • MedSpellChecker : texts with errors from medical anamnesis;
  • GitHubTypoCorpusRu : spelling errors and typos in commits from GitHub ;

RUSpellRU

Model Precision Recall F1
M2M100-418M 57.7 61.2 59.4
ChatGPT gpt-3.5-turbo-0301 55.8 75.3 64.1
ChatGPT gpt-4-0314 57.0 75.9 63.9
ChatGPT text-davinci-003 55.9 75.3 64.2
Yandex.Speller 83.0 59.8 69.5
JamSpell 42.1 32.8 36.9
HunSpell 31.3 34.9 33.0

MultidomainGold

Model Precision Recall F1
M2M100-418M 32.8 56.3 41.5
ChatGPT gpt-3.5-turbo-0301 33.8 72.1 46.0
ChatGPT gpt-4-0314 34.0 73.2 46.4
ChatGPT text-davinci-003 33.6 72.0 45.8
Yandex.Speller 52.9 51.4 52.2
JamSpell 25.7 30.6 28.0
HunSpell 16.2 40.1 23.0

MedSpellChecker

Модель Precision Recall F1
M2M100-418M 23.2 64.5 34.1
ChatGPT gpt-3.5-turbo-0301 53.2 67.6 59.6
ChatGPT gpt-4-0314 54.2 69.4 60.9
ChatGPT text-davinci-003 47.8 68.4 56.3
Yandex.Speller 80.6 47.8 60.0
JamSpell 24.6 29.7 26.9
HunSpell 10.3 40.2 16.4

GitHubTypoCorpusRu

Модель Precision Recall F1
M2M100-418M 27.5 42.6 33.4
ChatGPT gpt-3.5-turbo-0301 43.8 57.0 49.6
ChatGPT gpt-4-0314 45.2 58.2 51.0
ChatGPT text-davinci-003 46.5 58.1 51.7
Yandex.Speller 67.7 37.5 48.3
JamSpell 49.5 29.9 37.3
HunSpell 28.5 30.7 29.6
How to use
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

path_to_model = "ai-forever/RuM2M100-418M"

model = M2M100ForConditionalGeneration.from_pretrained(path_to_model)
tokenizer = M2M100Tokenizer.from_pretrained(path_to_model, src_lang="ru", tgt_lang="ru")

sentence = "прийдя в МГТУ я был удивлен никого необноружив там…"

encodings = tokenizer(sentence, return_tensors="pt")
generated_tokens = model.generate(
        **encodings, forced_bos_token_id=tokenizer.get_lang_id("ru"))
answer = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(answer)

# ["прийдя в МГТУ я был удивлен никого не обнаружив там..."]
Resources
License

Model M2M100-418M , on the basis of which our solution is made, and its source code are supplied under the MIT open license. Our solution also comes with MIT license.

Specifications
  • File size: 2 Gb;
  • Framework: pytorch
  • Format: AI Service
  • Version: v1.0
  • Developer: SberDevices, AGI NLP
Contacts

nikita.martynov.98@list.ru

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