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
.
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",
}
definference(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?']
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