Omkar2118/datasets huggingface dataset:
RuCoS
Full name: Russian Reading Comprehension with Commonsense Reasoning

Russian reading comprehension with Commonsense reasoning (RuCoS) is a large-scale reading comprehension dataset that requires commonsense reasoning. RuCoS consists of queries automatically generated from CNN/Daily Mail news articles; the answer to each query is a text span from a summarizing passage of the corresponding news. The goal of RuCoS is to evaluate a machine`s ability of commonsense reasoning in reading comprehension.


Example
```
  {'source': 'Lenta',
   'passage': {
          'text':
          'Мать двух мальчиков, брошенных отцом в московском аэропорту Шереметьево, забрала их. Об этом сообщили ТАСС в пресс-службе министерства образования и науки Хабаровского края. Сейчас младший ребенок посещает детский сад, а старший ходит в школу. В учебных заведениях с ними по необходимости работают штатные психологи. Также министерство социальной защиты населения рассматривает вопрос о бесплатном оздоровлении детей в летнее время. Через несколько дней после того, как Виктор Гаврилов бросил своих детей в аэропорту, он явился с повинной к следователям в городе Батайске Ростовской области.\n@context\nБросившего детей в Шереметьево отца задержали за насилие над женой\n@context\nРоссиянина заподозрили в истязании брошенных в Шереметьево детей\n@context\nОставивший двоих детей в Шереметьево россиянин сам пришел к следователям',
          'entities': [
              {'start': 60, 'end': 71, 'text': 'Шереметьево'},
              {'start': 102, 'end': 106, 'text': 'ТАСС'},
              {'start': 155, 'end': 172, 'text': 'Хабаровского края'},
              {'start': 470, 'end': 485, 'text': 'Виктор Гаврилов'},
              {'start': 563, 'end': 571, 'text': 'Батайске'},
              {'start': 572, 'end': 590, 'text': 'Ростовской области'},
              {'start': 620, 'end': 631, 'text': 'Шереметьево'},
              {'start': 725, 'end': 736, 'text': 'Шереметьево'},
              {'start': 778, 'end': 789, 'text': 'Шереметьево'}
          ]
      },
      'qas': [
          {
              'query': '26 января @placeholder бросил сыновей в возрасте пяти и семи лет в Шереметьево.',
              'answers': [
                  {'start': 470, 'end': 485, 'text': 'Виктор Гаврилов'}
              ],
              'idx': 0
          }
      ],
      'idx': 0
  }
```

### How did we collect data? 
All text examples were collected from open news sources, then automatically filtered with QA systems to prevent obvious questions to infiltrate the dataset. The texts were then filtered by IPM frequency of the contained words and, finally, manually reviewed.

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
DaNetQA
Full name: Yes/no Question Answering Dataset for the Russian

DaNetQA is a question answering dataset for yes/no questions. These questions are naturally occurring ---they are generated in unprompted and unconstrained settings.

Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context. The text-pair classification setup is similar to existing natural language inference tasks.

By sampling questions from a distribution of information-seeking queries (rather than prompting annotators for text pairs), we observe significantly more challenging examples compared to existing NLI datasets.


### Example
```
{
  "text": "В период с 1969 по 1972 год по программе «Аполлон» было выполнено 6 полётов с посадкой на Луне. Всего на Луне высаживались 12 астронавтов США. Список космонавтов Список космонавтов — участников орбитальных космических полётов Список астронавтов США — участников орбитальных космических полётов Список космонавтов СССР и России — участников космических полётов Список женщин-космонавтов Список космонавтов, посещавших МКС Энциклопедия астронавтики.",
  "question": "Был ли человек на луне?",
  "label": true,
  "idx": 5
}
```

### How did we collect data? 
All text examples were collected in accordance with the methodology for collecting the original dataset. Answers to the questions were received with the help of assessors, and texts were also received automatically using ODQA systems on Wikipedia. Human assessment was carried out on Yandex.Toloka.

*Additionally, to increase number of samples and the distribution of yes/no answers, we added extra data in the same format (data were collected from Yandex.Toloka while generating MuSeRC dataset).

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
RWSD
Full name: The Winograd Schema Challenge (Russian)

A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from a well-known example by Terry Winograd.

The set would then be presented as a challenge for AI programs, along the lines of the Turing test. The strengths of the challenge are that it is clear-cut, in that the answer to each schema is a binary choice; vivid, in that it is obvious to non-experts that a program that fails to get the right answers clearly has serious gaps in its understanding; and difficult, in that it is far beyond the current state of the art.

### Task Type
Logic and Reasoning, World knowledge. Binary Classification: true/false

### Example
```
{
  "text": "Кубок не помещается в коричневый чемодан, потому что он слишком большой."
  "label": false,
  "idx": 5,
  "target": {
    "span1_text": "чемодан",
    "span2_text": "он слишком большой",
    "span1_index": 5,
    "span2_index": 8
  },
}
```
### How did we collect data? 
All text examples were collected manually translating and adapting original Winograd dataset for Russian. Human assessment was carried out on Yandex.Toloka.

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
RUSSE
Full name: Russian Words in Context (based on RUSSE)

WiC: The Word-in-Context Dataset A reliable benchmark for the evaluation of context-sensitive word embeddings.

Depending on its context, an ambiguous word can refer to multiple, potentially unrelated, meanings. Mainstream static word embeddings, such as Word2vec and GloVe, are unable to reflect this dynamic semantic nature. Contextualised word embeddings are an attempt at addressing this limitation by computing dynamic representations for words which can adapt based on context.

Russian SuperGLUE task borrows original data from the Russe project, Word Sense Induction and Disambiguation shared task (2018)

### Task Type
Reading Comprehension. Binary Classification: true/false

### Example
```
{
  "idx" : 8,
  "word" : "дорожка",
  "sentence1" : "Бурые ковровые дорожки заглушали шаги",
  "sentence2" : "Приятели решили выпить на дорожку в местном баре",
  "start1" : 15,
  "end1" : 23,
  "start2" : 26,
  "end2" : 34,
  "label" : false,
  "gold_sense1" : 1,
  "gold_sense2" : 2
}
```
                      
### How did we collect data? 
All text examples were collected from Russe original dataset, already collected by Russian Semantic Evaluation at ACL SIGSLAV. Human assessment was carried out on Yandex.Toloka.

In version 2, we have manually collected in the same format testset.

Introducing paper: RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
TERRa
Full name: Textual Entailment Recognition for Russian

Textual Entailment Recognition has been proposed recently as a generic task that captures major semantic inference needs across many NLP applications, such as Question Answering, Information Retrieval, Information Extraction, and Text Summarization. This task requires to recognize, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text.

### Task Type
RTE (Recognizing Textual Entailment) Sentence Pair Classification - Entailment - Not Entailment

### Example
```
{
  "premise": "Автор поста написал в комментарии, что прорвалась канализация.",
  "hypothesis": "Автор поста написал про канализацию.",
  "label": "entailment",
  "idx": "6062"
}
```

### How did we collect data? 
All text examples were collected from open news sources and literary magazines, then manually reviewed and supplemented by a human assessment on Yandex.Toloka

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
PARus
Full name: Choice of Plausible Alternatives for Russian language

Choice of Plausible Alternatives for Russian language (PARus) evaluation provides researchers with a tool for assessing progress in open-domain commonsense causal reasoning. Each question in PARus is composed of a premise and two alternatives, where the task is to select the alternative that more plausibly has a causal relation with the premise. The correct alternative is randomized so that the expected performance of randomly guessing is 50%.

### Task Type
Evaluation of commonsense causal reasoning

Sentence Pair Classification: suitable - not suitable

### Example
```
{
  "premise": "Гости вечеринки прятались за диваном.",
  "choice1": "Это была вечеринка-сюрприз.",
  "choice2":"Это был день рождения.",
  "question": "cause",
  "label": 0,
  "idx": 4
}
```
### How did we collect data? 
All text examples were collected from open news sources and literary magazines, then manually reviewed and supplemented by a human assessment on Yandex.Toloka

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
RCB
Full name: Russian Commitment Bank

The Russian Commitment Bank is a corpus of naturally occurring discourses whose final sentence contains a clause-embedding predicate under an entailment cancelling operator (question, modal, negation, antecedent of conditional).

### Task Type
RTE (Recognizing Textual Entailment) Sentence Pair Classification - Entailment - Contradiction - Neutral

### Example
```
{
          "premise": "Сумма ущерба составила одну тысячу рублей. Уточняется, что на место происшествия выехала следственная группа, которая установила личность злоумышленника. Им оказался местный житель, ранее судимый за подобное правонарушение.",
          "hypothesis": "Ранее местный житель совершал подобное правонарушение.",
          "verb": "судить",
          "negation": "no_negation",
          "label": "entailment",
          "idx": 269
          }
```
### How did we collect data? 
All text examples were collected from open news sources and literary magazines, then manually reviewed and supplemented by a human assessment on Yandex.Toloka.

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
LiDiRus
Full name: Linguistic Diagnostic for Russian

LiDiRus is a diagnostic dataset that covers a large volume of linguistic phenomena, while allowing you to evaluate information systems on a simple test of textual entailment recognition. See more details diagnostics.

### Task Type
RTE (Recognizing Textual Entailment) Sentence Pair Classification - Entailment - Not Entailment

### Example
```
{
     'sentence1': "Кошка сидела на коврике.",
     'sentence2': "Кошка не сидела на коврике.",
     'label': 'not_entailment',
     'knowledge': '',
     'lexical-semantics': '',
     'logic': 'Negation',
     'predicate-argument-structure': ''
    }
```
### How did we collect data? 
All text examples manually translated and adapted from English [SuperGLUE Diagnostics](https://super.gluebenchmark.com/diagnostics)

Introducing paper: RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark