🚀 sage-m2m100-1.2B Model
This model corrects spelling errors and typos in Russian text, bringing words to the language norm. It's based on the M2M100-1.2B model and fine - tuned for this specific task.
🚀 Quick Start
The sage-m2m100-1.2B model is designed to correct spelling errors and typos in Russian text. It was trained on an extensive dataset with artificially introduced errors, created from Russian - language Wikipedia and video transcripts.
✨ Features
- Spelling Correction: Corrects spelling errors and typos in Russian text.
- Fine - Tuned Model: Based on the pre - trained M2M100-1.2B model and fine - tuned for Russian spelling correction.
- Extensive Training Data: Trained on a large corpus with synthetic errors generated using the SAGE library.
📚 Documentation
Summary
The model corrects spelling errors and typos by normalizing all the words in the text to the Russian language norm. The corrector was trained based on the model M2M100-1.2B. An extensive dataset with “artificial” errors was used as the training corpus. The corpus was assembled from Russian - language Wikipedia and transcripts of Russian - language videos, and then typos and spelling errors were automatically introduced using the SAGE library. The model is a fine - tuned version of the pre - train.
Public references
Model Index
| Property |
Details |
| Model Name |
sage - m2m100 - 1.2B |
| Task Type |
text - generation |
| Datasets |
spellcheck_benchmark (RUSpellRU, MultidomainGold, MedSpellchecker, GitHubTypoCorpusRu) |
| Metrics |
Precision, Recall, F1 |
Results
| Dataset |
Precision |
Recall |
F1 |
| RUSpellRU |
88.8 |
71.5 |
79.2 |
| MultidomainGold |
63.8 |
61.1 |
62.4 |
| MedSpellchecker |
78.8 |
71.4 |
74.9 |
| GitHubTypoCorpusRu |
47.1 |
42.9 |
44.9 |
💻 Usage Examples
Basic Usage
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
path_to_model = "ai-forever/sage-m2m100-1.2B"
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)
Examples Table
| Input |
Output |
| Думю ешцъа лет череа 10 ретроспективно просматривотьэ то будкетцц мне невероя тна ин те р но |
Думаю что лет через 10 ретроспективно просматривать это будет мне невероятно интересно |
| 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… |
придя в МГТУ я был удивлен никого не обнаружив там |
📊 Metrics
Quality
The following are the 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 |
| sage - m2m100 - 1.2B |
88.8 |
71.5 |
79.2 |
| sage - ai - service |
93.5 |
82.4 |
87.6 |
| gpt - 3.5 - turbo |
39.6 |
62.3 |
48.5 |
| gpt - 4 |
69.5 |
81.0 |
74.8 |
| 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 |
| sage - m2m100 - 1.2B |
63.8 |
61.1 |
62.4 |
| sage - ai - service |
70.9 |
68.8 |
69.9 |
| gpt - 3.5 - turbo |
17.8 |
56.1 |
27.0 |
| gpt - 4 |
31.1 |
78.1 |
44.5 |
| Yandex.Speller |
52.9 |
51.4 |
52.2 |
| JamSpell |
25.7 |
30.6 |
28.0 |
| HunSpell |
16.2 |
40.1 |
23.0 |
MedSpellChecker
| Model |
Precision |
Recall |
F1 |
| sage - m2m100 - 1.2B |
78.8 |
71.4 |
74.9 |
| sage - ai - service |
73.4 |
76.2 |
74.9 |
| gpt - 3.5 - turbo |
15.1 |
53.6 |
23.5 |
| gpt - 4 |
48.9 |
88.7 |
63.1 |
| Yandex.Speller |
80.6 |
47.8 |
60.0 |
| JamSpell |
24.6 |
29.7 |
26.9 |
| HunSpell |
10.3 |
40.2 |
16.4 |
GitHubTypoCorpusRu
| Model |
Precision |
Recall |
F1 |
| sage - m2m100 - 1.2B |
47.1 |
42.9 |
44.9 |
| sage - ai - service |
76.1 |
51.2 |
61.2 |
| gpt - 3.5 - turbo |
23.7 |
43.9 |
30.8 |
| gpt - 4 |
34.7 |
60.5 |
44.1 |
| Yandex.Speller |
67.7 |
37.5 |
48.3 |
| JamSpell |
49.5 |
29.9 |
37.3 |
| HunSpell |
28.5 |
30.7 |
29.6 |
📦 Resources
📄 License
The model M2M100-1.2B, 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 the MIT license.
🔧 Technical Details
Specifications
| Property |
Details |
| File size |
5 Gb |
| Framework |
pytorch |
| Format |
AI Service |
| Version |
v2.0 |
| Developer |
SberDevices, AGI NLP |
📞 Contacts
nikita.martynov.98@list.ru
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