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Updating description of fields and statistics

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  1. README.md +62 -43
README.md CHANGED
@@ -14,7 +14,7 @@ task_categories:
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  ### Dataset Summary
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- This dataset is a set of samples for testing the spell checker, grammar error correction and ungrammatical text detection models.
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19
  The dataset contains two splits:
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@@ -23,16 +23,14 @@ test.json contains samples hand-selected to evaluate the quality of models.
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  train.json contains synthetic samples generated in various ways.
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  The purpose of creating the dataset was to test an internal spellchecker for [a generative poetry project](https://github.com/Koziev/verslibre), but it can also be useful in other projects, since it does not have an explicit specialization for poetry.
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- You can consider this dataset as an extension of [RuCOLA](https://huggingface.co/datasets/RussianNLP/rucola).
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- In addition, for some samples there is a corrected version of the text ("fixed_sentence" field), so it can be used as an extension of datasets in [ai-forever/spellcheck_benchmark](https://huggingface.co/datasets/ai-forever/spellcheck_benchmark).
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  ### Example
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  ```
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  {
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  "id": 1483,
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- "sentence": "Разучи стихов по больше",
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- "fixed_sentence": "Разучи стихов побольше",
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  "label": 0,
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  "error_type": "Tokenization",
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  "domain": "prose"
@@ -45,38 +43,25 @@ The test split contains only examples of mistakes made by people. There are no s
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  The examples of errors in the test split come from different people in terms of gender, age, education, context, and social context.
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- The input and output text can be not only one sentence, but also 1) part of a sentence, 2) several sentences - a paragraph, 3) a fragment of a poem, usually a quatrain or two quatrains.
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- The texts may include offensive texts, texts that offend religious or political feelings, texts that contradict moral standards, etc. Such samples are only needed to make the corpus as representative as possible for the tasks of processing messages in various media such as blogs, comments, etc.
 
 
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  One sample may contain several errors of different types.
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54
 
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-
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- ### Uncensoring samples
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-
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- A number of samples contain text with explicit obscenities:
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-
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- ```
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- {
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- "id": 1,
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- "sentence": "Но не простого - с лёгкой еб@нцой.",
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- "fixed_sentence": "Но не простого - с лёгкой ебанцой.",
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- "label": 0,
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- "error_type": "Misspelling",
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- "domain": "prose"
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- }
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- ```
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-
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  ### Poetry samples
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- A few poetry samples are included in this version:
 
74
 
75
  ```
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  {
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  "id": 24,
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- "sentence": "Чему научит забытьё?\nСмерть формы д'арует литьё.\nРезец мгновенье любит стружка...\nСмерть безобидная подружка!",
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- "fixed_sentence": null,
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  "label": 0,
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  "error_type": "Grammar",
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  "domain": "poetry"
@@ -88,9 +73,9 @@ A few poetry samples are included in this version:
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  ### Dataset fields
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  **id** (int64): the sentence's id, starting 1.
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- **sentence** (str): the original sentence.
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- **fixed_sentence** (str): the corrected version of original sentence.
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- **label** (str): the target class. "1" for "acceptable", "0" for "unacceptable".
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  **error_type** (str): the violation category: Spelling, Grammar, Tokenization, Punctuation, Mixture, Unknown.
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  **domain** (str): domain: "prose" or "poetry".
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@@ -101,8 +86,8 @@ A few poetry samples are included in this version:
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  ```
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  {
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  "id": 6,
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- "sentence": "Я подбираю по проще слова",
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- "fixed_sentence": "Я подбираю попроще слова",
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  "label": 0,
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  "error_type": "Tokenization",
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  "domain": "prose"
@@ -114,8 +99,8 @@ A few poetry samples are included in this version:
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  ```
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  {
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  "id": 5,
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- "sentence": "И швырнуть по-дальше",
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- "fixed_sentence": "И швырнуть подальше",
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  "label": 0,
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  "error_type": "Punctuation",
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  "domain": "prose"
@@ -127,8 +112,8 @@ A few poetry samples are included in this version:
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  ```
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  {
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  "id": 38,
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- "sentence": "И ведь что интересно, русские официально ни в одном крестовом позоде не участвовали.",
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- "fixed_sentence": "И ведь что интересно, русские официально ни в одном крестовом походе не участвовали.",
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  "label": 0,
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  "error_type": "Spelling",
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  "domain": "prose"
@@ -140,8 +125,8 @@ A few poetry samples are included in this version:
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  ```
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  {
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  "id": 61,
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- "sentence": "на него никто не польститься",
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- "fixed_sentence": "на него никто не польстится",
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  "label": 0,
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  "error_type": "Grammar",
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  "domain": "prose"
@@ -151,16 +136,50 @@ A few poetry samples are included in this version:
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  Please note that error categories are not always set accurately, so you should not use
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  the "error_type" field to train classifiers.
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155
  ### Statistics
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157
  Statistics for test split.
158
 
159
  ```
160
- +--------+---------+---------+-------------+--------------+----------+---------+-------+
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- | Domain | Grammar | Unknown | Punctuation | Tokenization | Spelling | Mixture | TOTAL |
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- +--------+---------+---------+-------------+--------------+----------+---------+-------+
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- | prose | 185 | 636 | 1407 | 1999 | 1802 | 150 | 6179 |
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- | poetry | 1 | 614 | 222 | 172 | 27 | 30 | 1066 |
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- +--------+---------+---------+-------------+--------------+----------+---------+-------+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
 
 
 
 
 
 
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15
  ### Dataset Summary
16
 
17
+ This dataset is a set of samples for training and testing the spell checking, grammar error correction and ungrammatical text detection models.
18
 
19
  The dataset contains two splits:
20
 
 
23
  train.json contains synthetic samples generated in various ways.
24
 
25
  The purpose of creating the dataset was to test an internal spellchecker for [a generative poetry project](https://github.com/Koziev/verslibre), but it can also be useful in other projects, since it does not have an explicit specialization for poetry.
 
 
26
 
27
  ### Example
28
 
29
  ```
30
  {
31
  "id": 1483,
32
+ "text": "Разучи стихов по больше",
33
+ "fixed_text": "Разучи стихов побольше",
34
  "label": 0,
35
  "error_type": "Tokenization",
36
  "domain": "prose"
 
43
 
44
  The examples of errors in the test split come from different people in terms of gender, age, education, context, and social context.
45
 
46
+ The input and output text can be not only one sentence, but also 1) a part of a sentence or incomplete dialog responses, 2) several sentences - a paragraph, 3) a fragment of a poem, usually a quatrain or two quatrains.
47
 
48
+ The texts may include offensive phrases, phrases that offend religious or political feelings, fragments that contradict moral standards, etc.
49
+ Such samples are only needed to make the corpus as representative as possible for the tasks of processing messages
50
+ in various media such as blogs, comments, etc.
51
 
52
  One sample may contain several errors of different types.
53
 
54
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55
  ### Poetry samples
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+ The texts of the poems are included in the test part of the dataset, which makes it unique among similar
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+ datasets for the Russian language:
59
 
60
  ```
61
  {
62
  "id": 24,
63
+ "text": "Чему научит забытьё?\nСмерть формы д'арует литьё.\nРезец мгновенье любит стружка...\nСмерть безобидная подружка!",
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+ "fixed_text": null,
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  "label": 0,
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  "error_type": "Grammar",
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  "domain": "poetry"
 
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  ### Dataset fields
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  **id** (int64): the sentence's id, starting 1.
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+ **text** (str): the original text (part of sentence, whole sentence or several sentences).
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+ **fixed_text** (str): the corrected version of original text.
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+ **label** (str): the target class. "1" for "not defects", "0" for "contains defects".
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  **error_type** (str): the violation category: Spelling, Grammar, Tokenization, Punctuation, Mixture, Unknown.
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  **domain** (str): domain: "prose" or "poetry".
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  ```
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  {
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  "id": 6,
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+ "text": "Я ��одбираю по проще слова",
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+ "fixed_text": "Я подбираю попроще слова",
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  "label": 0,
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  "error_type": "Tokenization",
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  "domain": "prose"
 
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  ```
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  {
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  "id": 5,
102
+ "text": "И швырнуть по-дальше",
103
+ "fixed_text": "И швырнуть подальше",
104
  "label": 0,
105
  "error_type": "Punctuation",
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  "domain": "prose"
 
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  ```
113
  {
114
  "id": 38,
115
+ "text": "И ведь что интересно, русские официально ни в одном крестовом позоде не участвовали.",
116
+ "fixed_text": "И ведь что интересно, русские официально ни в одном крестовом походе не участвовали.",
117
  "label": 0,
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  "error_type": "Spelling",
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  "domain": "prose"
 
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  ```
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  {
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  "id": 61,
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+ "text": "на него никто не польститься",
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+ "fixed_text": "на него никто не польстится",
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  "label": 0,
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  "error_type": "Grammar",
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  "domain": "prose"
 
136
  Please note that error categories are not always set accurately, so you should not use
137
  the "error_type" field to train classifiers.
138
 
139
+ ### Uncensoring samples
140
+
141
+ A number of samples contain text with explicit obscenities:
142
+
143
+ ```
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+ {
145
+ "id": 1,
146
+ "text": "Но не простого - с лёгкой еб@нцой.",
147
+ "fixed_text": "Но не простого - с лёгкой ебанцой.",
148
+ "label": 0,
149
+ "error_type": "Misspelling",
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+ "domain": "prose"
151
+ }
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+ ```
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154
  ### Statistics
155
 
156
  Statistics for test split.
157
 
158
  ```
159
+ +--------+----------+--------------+----------+-------------+---------+---------+---------+-----------+--------+-------+
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+ | Domain | Spelling | Tokenization | No error | Punctuation | Unknown | Mixture | Grammar | Semantics | Gender | TOTAL |
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+ +--------+----------+--------------+----------+-------------+---------+---------+---------+-----------+--------+-------+
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+ | prose | 2127 | 2111 | 18033 | 1562 | 658 | 178 | 203 | 20 | 1 | 24893 |
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+ | poetry | 209 | 339 | 693 | 475 | 629 | 153 | 2 | 0 | 0 | 2500 |
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+ +--------+----------+--------------+----------+-------------+---------+---------+---------+-----------+--------+-------+
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+ ```
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+
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+ Statistics on the number of edits required to obtain a corrected version of the text:
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+ ```
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+ +-----------------+-------------------+------------------+
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+ | Number of edits | Number of samples | Share of samples |
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+ +-----------------+-------------------+------------------+
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+ | 1 | 5824 | 0.75 |
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+ | 2 | 1137 | 0.15 |
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+ | 3 | 348 | 0.04 |
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+ | 4 | 184 | 0.02 |
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+ | 5 | 97 | 0.01 |
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+ | >5 | 183 | 0.02 |
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+ +-----------------+-------------------+------------------+
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  ```
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+
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+ ## See also
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+
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+ [RuCOLA](https://huggingface.co/datasets/RussianNLP/rucola)
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+ [ai-forever/spellcheck_benchmark](https://huggingface.co/datasets/ai-forever/spellcheck_benchmark)
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+