sage-v1.1.0

ai-forever/sage-v1.1.0

sage-v1.1.0 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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1.7B

13.9 GB on disk

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At a glance

Library: transformers
License: mit
Model type: t5
Access: Public
Created: Apr 18, 2024
Updated: Nov 19, 2024
SHA: 0677d3c4

Model card

From the Hugging Face model README

sage-v1.1.0

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Summary

The model corrects spelling and punctuation errors and typos by bringing all the words in the text to the norm of the Russian language. Corrector had been trained based on the model FRED-T5-1.7B. 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.

Public references

SAGE library announcement, DataFest 2023
Paper about synthetic error generation methods, Dialogue 2023
SAGE EACL 2024 paper

Examples

Input	Output
И не чсно прохожим в этот день непогожйи почему я веселый такйо	И не ясно прохожим в этот день непогожий, почему я веселый такой.
Каждй день воттак делой, и спена балеть нибудет. А вотак каждый день ниделай	Каждый день вот так делай и спина болеть не будет. А вот так каждый день не делай.
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

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	Pr. (spell)	Rec. (spell)	F1 (spell)	Pr. (punc)	Rec. (punc)	F1 (punc)	Pr. (case)	Rec. (case)	F1 (case)
sage-v1.1.0	90.3	86.3	88.2	90.3	86.6	88.4	95.2	95.9	95.6
sage-fredt5-large	57.3	68.0	62.2	86.7	46.1	60.2	92.1	67.8	78.1
sage-fredt5-large (ft)	88.4	80.9	84.5	88.2	85.3	86.8	95.5	94.0	94.7
gpt-3.5-turbo	33.6	58.5	42.7	85.9	64.6	73.7	84.9	73.9	79.0
gpt-4	54.9	76.7	64.0	84.0	82.3	83.2	91.5	90.2	90.9

MultidomainGold

Model	Pr. (spell)	Rec. (spell)	F1 (spell)	Pr. (punc)	Rec. (punc)	F1 (punc)	Pr. (case)	Rec. (case)	F1 (case)
sage-v1.1.0	81.6	77.7	79.6	70.2	67.5	68.8	80.5	80.5	80.5
sage-fredt5-large	43.4	49.7	46.3	21.8	21.3	21.6	58.8	23.9	34.0
sage-fredt5-large (ft)	80.3	75.1	77.6	69.0	66.5	67.7	78.6	80.0	79.3
gpt-3.5-turbo	18.8	48.1	27.1	42.0	31.8	36.2	47.1	51.3	49.1
gpt-4	25.4	68.0	37.0	57.8	54.3	56.0	54.0	67.5	60.0

MedSpellChecker

Model	Pr. (spell)	Rec. (spell)	F1 (spell)	Pr. (punc)	Rec. (punc)	F1 (punc)	Pr. (case)	Rec. (case)	F1 (case)
sage-v1.1.0	71.3	73.5	72.4	75.1	69.2	72.0	80.9	72.8	76.6
sage-fredt5-large	35.2	54.5	42.8	19.2	13.2	15.7	48.7	36.8	41.9
sage-fredt5-large (ft)	72.5	72.2	72.3	74.6	66.4	70.3	79.3	85.1	82.1
gpt-3.5-turbo	14.7	45.9	22.3	69.9	52.3	59.8	26.4	41.8	32.3
gpt-4	37.8	72.3	49.6	81.4	64.3	71.9	73.0	62.1	67.1

GitHubTypoCorpusRu

Model	Pr. (spell)	Rec. (spell)	F1 (spell)	Pr. (punc)	Rec. (punc)	F1 (punc)	Pr. (case)	Rec. (case)	F1 (case)
sage-v1.1.0	70.8	56.3	62.7	48.9	35.8	41.4	32.9	45.3	38.1
sage-fredt5-large	46.0	46.6	46.3	22.7	18.3	20.2	12.0	13.2	12.6
sage-fredt5-large (ft)	67.5	53.2	59.5	48.5	38.0	42.6	37.3	50.0	42.7
gpt-3.5-turbo	23.7	38.7	29.4	37.6	23.3	28.7	19.6	35.9	25.3
gpt-4	27.0	52.8	35.7	45.9	32.6	38.2	25.7	36.8	30.2

How to use

import re
import torch
from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("ai-forever/FRED-T5-1.7B")
model = T5ForConditionalGeneration.from_pretrained("ai-forever/sage-v1.1.0")

model.to('cuda')

tokenizer_config = {
            'max_length': None,
            'padding': 'longest',
            'truncation': False,
            "return_tensors": "pt",
        }

def inference(sentence):
    text = "<LM>" + sentence
    with torch.inference_mode():
        encodings = tokenizer(text, **tokenizer_config)
        for k, v in encodings.items():
            encodings[k] = v.to('cuda:0')
        res = model.generate(
            **encodings,
            use_cache=True,
            max_length = encodings['input_ids'].size(1) * 1.5
        )
        res = res.cpu().tolist()
        res = tokenizer.batch_decode(res, skip_special_tokens=True)
    return res

text = 'Првет какдила'
text = re.sub(r'\n+', '\n', text)
print(inference(text))

# ['Привет, как дела?']

Resources

SAGE library, GitHub
sage-fredt5-large, HuggingFace
sage-fredt5-distilled-95m, HuggingFace
sage-m2m100-1.2B, HuggingFace
sage-mt5-large, HuggingFace

Specifications

File size: 7 Gb;
Framework: pytorch
Version: v1.1.0
Developer: SberDevices, AGI NLP

Contacts

[email protected]

Library: transformers
Type: t5
License: mit
Languages: ru
Created: Apr 18, 2024
Updated: Nov 19, 2024