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
.
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")
sentence = "И не чсно прохожим в этот день непогожйи почему я веселый такйо"
inputs = tokenizer(sentence, max_length=None, padding="longest", truncation=False, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_length = inputs["input_ids"].size(1) * 1.5)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ["И не ясно прохожим в этот день непогожий, почему я весёлый такой?"]
Limitations
Complex formatting may cause some trouble in output generation.
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.
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