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sage-m2m100-1.2B
ai-forever/sage-m2m100-1.2B
sage-m2m100-1.2B is a machine learning model from ai-forever. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
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1.1B
9 GB on disk
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At a glance
- Library
- transformers
- License
- mit
- Model type
- m2m_100
- Access
- Public
- Created
- Mar 11, 2024
- Updated
- Apr 3, 2024
- SHA
- 1773f13b
Datasets
Model card
From the Hugging Face model README
sage-m2m100-1.2B model
Summary
The model corrects spelling errors and typos by bringing all the words in the text to the norm of the Russian language. Corrector was trained based on the model M2M100-1.2B. 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 library SAGE. The model is the fine-tuned version of the pre-train.
Public references
- SAGE library announcement, DataFest 2023
- Paper about synthetic error generation methods, Dialogue 2023
- SAGE EACL 2024 paper
Examples
| 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
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 |
|---|---|---|---|
| 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 |
How to use
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)
#["прийдя в МГТУ я был удивлен никого не обнаружив там..."]
Resources
- SAGE library, GitHub
- sage-fredt5-large, HuggingFace
- sage-fredt5-distilled-95m, HuggingFace
- sage-m2m100-1.2B, HuggingFace
- sage-mt5-large, HuggingFace
License
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 MIT license.
Specifications
- File size: 5 Gb;
- Framework: pytorch
- Format: AI Service
- Version: v2.0
- Developer: SberDevices, AGI NLP
Contacts
- Library
- transformers
- Type
- m2m_100
- License
- mit
- Languages
- ru
- Created
- Mar 11, 2024
- Updated
- Apr 3, 2024