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
.
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..."]
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.
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