metadata

language:
  - ru
tags:
  - spellchecking
  - pytorch
  - natural language generation
license: mit
metrics:
  - precision
  - recall
  - f1
library_name: transformers
model-index:
  - name: sage-fredt5-distilled-95m
    results:
      - task:
          type: text-generation
        dataset:
          type: spellcheck_benchmark
          name: RUSpellRU (spell&punct)
        metrics:
          - name: F1 (spell)
            type: f1_spell
            value: 78.9
            verified: false
          - name: F1 (punct)
            type: f1_punct
            value: 83.6
            verified: false
          - name: F1 (case)
            type: f1_case
            value: 93.5
            verified: false
      - task:
          type: text-generation
        dataset:
          type: spellcheck_benchmark
          name: MultidomainGold (spell&punct)
        metrics:
          - name: F1 (spell)
            type: f1_spell
            value: 73.4
            verified: false
          - name: F1 (punct)
            type: f1_punct
            value: 65
            verified: false
          - name: F1 (case)
            type: f1_case
            value: 77.9
            verified: false
      - task:
          type: text-generation
        dataset:
          type: spellcheck_benchmark
          name: MedSpellchecker (spell&punct)
        metrics:
          - name: F1 (spell)
            type: f1_spell
            value: 64.9
            verified: false
          - name: F1 (punct)
            type: f1_punct
            value: 70
            verified: false
          - name: F1 (case)
            type: f1_case
            value: 68.7
            verified: false
      - task:
          type: text-generation
        dataset:
          type: spellcheck_benchmark
          name: GitHubTypoCorpusRu (spell&punct)
        metrics:
          - name: F1 (spell)
            type: f1_spell
            value: 52.7
            verified: false
          - name: F1 (punct)
            type: f1_punct
            value: 42.1
            verified: false
          - name: F1 (case)
            type: f1_case
            value: 36.3
            verified: false
datasets:
  - ai-forever/spellcheck_punctuation_benchmark

sage-fredt5-distilled-95m

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 is a distilled version of the original model that had been trained based on the FRED-T5-1.7B architecture. 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 весёлый такой?
Каждй день воттак делой, и спена балеть нибудет. А вотак каждый день ниделай	Каждый день вот так делай, и спена болеть не будет. А вот так каждый день — ни 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-fredt5-distilled-95m	83.5	74.8	78.9	86.8	80.6	83.6	94.4	92.5	93.5
sage-ai-service	90.3	86.3	88.2	90.3	86.6	88.4	95.2	95.9	95.6
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-fredt5-distilled-95m	77.2	69.9	73.4	66.8	63.4	65.0	76.8	79.1	77.9
sage-ai-service	81.6	77.7	79.6	70.2	67.5	68.8	80.5	80.5	80.5
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-fredt5-distilled-95m	65.1	64.8	64.9	78.6	63.1	70.0	63.5	74.7	68.7
sage-ai-service	71.3	73.5	72.4	75.1	69.2	72.0	80.9	72.8	76.6
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-fredt5-distilled-95m	57.8	48.5	52.7	45.2	39.5	42.1	29.9	46.2	36.3
sage-ai-service	70.8	56.3	62.7	48.9	35.8	41.4	32.9	45.3	38.1
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

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ai-forever/sage-fredt5-distilled-95m")
model = AutoModelForSeq2SeqLM.from_pretrained("ai-forever/sage-fredt5-distilled-95m")
model.to("cuda:0")

sentence = "И не чсно прохожим в этот день непогожйи почему я веселый такйо"
text = "<LM>" + sentence
with torch.inference_mode():
    encodings = tokenizer(text, max_length=None, padding="longest", truncation=False, return_tensors="pt")
    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)

print(res)
# ["И не ясно прохожим в этот день непогожий, почему я весёлый такой?"]

Limitations

Complex formatting may cause some trouble in output generation.

Resources

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

License

Model FRED-T5-1.7B, on the basis of which our solution is made, and its source code are supplied under the MIT license. Our solution comes with MIT license also.

Specifications

File size: 0.383 Gb;
Framework: pytorch
Version: v1.0
Developer: SberDevices, AGI NLP

Contacts

nikita.martynov.98@list.ru