README.md

---
language:
- ru
license: mit
library_name: transformers
tags:
- spellchecking
- pytorch
- natural language generation
metrics:
- precision
- recall
- f1
model-index:
- name: sage-fredt5-large
  results:
  - task:
      type: text-generation
    dataset:
      name: RUSpellRU (spell&punct)
      type: spellcheck_benchmark
    metrics:
    - type: f1_spell
      value: 88.2
      name: F1 (spell)
      verified: false
    - type: f1_punct
      value: 88.4
      name: F1 (punct)
      verified: false
    - type: f1_case
      value: 95.6
      name: F1 (case)
      verified: false
    - type: f1_spell
      value: 79.6
      name: F1 (spell)
      verified: false
    - type: f1_punct
      value: 68.8
      name: F1 (punct)
      verified: false
    - type: f1_case
      value: 80.5
      name: F1 (case)
      verified: false
    - type: f1_spell
      value: 72.4
      name: F1 (spell)
      verified: false
    - type: f1_punct
      value: 72.0
      name: F1 (punct)
      verified: false
    - type: f1_case
      value: 76.6
      name: F1 (case)
      verified: false
    - type: f1_spell
      value: 62.7
      name: F1 (spell)
      verified: false
    - type: f1_punct
      value: 41.4
      name: F1 (punct)
      verified: false
    - type: f1_case
      value: 38.1
      name: F1 (case)
      verified: false
---

# sage-v1.1.0

![banner](images/sage_banner.jpg)

## 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](https://huggingface.co/ai-forever/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](https://github.com/ai-forever/sage).

## Public references
- [SAGE library announcement](https://youtu.be/yFfkV0Qjuu0), DataFest 2023
- [Paper about synthetic error generation methods](https://www.dialog-21.ru/media/5914/martynovnplusetal056.pdf), Dialogue 2023
- [SAGE EACL 2024 paper](https://aclanthology.org/2024.findings-eacl.10/)


## 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](https://www.livejournal.com/media)), 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](https://github.com);

**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
```python
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](https://github.com/ai-forever/sage), GitHub
- [sage-fredt5-large](https://huggingface.co/ai-forever/sage-fredt5-large), HuggingFace
- [sage-fredt5-distilled-95m](https://huggingface.co/ai-forever/sage-fredt5-distilled-95m), HuggingFace
- [sage-m2m100-1.2B](https://huggingface.co/ai-forever/sage-m2m100-1.2B), HuggingFace
- [sage-mt5-large](https://huggingface.co/ai-forever/sage-mt5-large), HuggingFace


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

## Contacts
[email protected]