Omkar2118/datasets huggingface dataset:
RuWorldTree
Full name: RuWorldTree

RuWorldTree is a QA dataset with multiple-choice elementary-level science questions, which evaluate the understanding of core science facts.

**Motivation**

The WorldTree dataset starts the triad of the Reasoning and Knowledge tasks. The data includes the corpus of factoid utterances of various kinds, complex factoid questions and a corresponding causal chain of facts from the corpus resulting in a correct answer.

The WorldTree design was originally proposed in (Jansen et al., 2018).

An example in English for illustration purposes:

```{
    'question': 'A bottle of water is placed in the freezer. What property of water will change when the water reaches the freezing point? (A) color (B) mass (C) state of matter (D) weight', 

    'answer': 'C', 

    'exam_name': 'MEA',

    'school_grade': 5,

    'knowledge_type': 'NO TYPE',

    'perturbation': 'ru_worldtree', 

    'episode': [18, 10, 11]
}```

**Data Fields**

 - text: a string containing the sentence text
 - answer: a string with a candidate for the coreference resolution
 - options: a list of all the possible candidates present in the text
 - reference: a string containing an anaphor (a word or phrase that refers back to an earlier word or phrase)
 - homonymia_type: a float corresponding to the type of the structure with syntactic homonymy
 - label: an integer, either 0 or 1, indicating whether the homonymy is resolved correctly or not
perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
 - episode: a list of episodes in which the instance is used. Only used for the train set

**Data Splits**

The dataset consists of a training set with labeled examples and a test set in two configurations:

 - raw data: includes the original data with no additional sampling
 - episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation

We use the same splits of data as in the original English version.

**Test Perturbations**

Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:

 - ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
 - Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
 - EDAdelete: randomly deletes tokens in the text
 - EDAswap: randomly swaps tokens in the text
 - BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
 - AddSent: replaces one or more choice options with a generated one

Introducing paper: TAPE: Assessing Few-shot Russian Language Understanding
RuOpenBookQA
Full name: RuOpenBookQA

RuOpenBookQA is a QA dataset with multiple-choice elementary-level science questions which probe the understanding of core science facts.

**Motivation**

RuOpenBookQA is mainly based on the work of (Mihaylov et al., 2018): it is a QA dataset with multiple-choice elementary-level science questions, which probe the understanding of 1k+ core science facts.

Very similar to the pipeline of the RuWorldTree, the dataset includes a corpus of factoids, factoid questions and correct answer. Only one fact is enough to find the correct answer, so this task can be considered easier.

```{
    'ID': '7-674', 

    'question': 'If a person walks in the direction opposite to the compass needle, they are going (A) west (B) north (C) east (D) south',

    'answer': 'D',

    'episode': [11],

    'perturbation': 'ru_openbook'
}```

**Data Fields**

 - ID: a string containing a unique question id
 - question: a string containing question text with answer options
 - answer: a string containing the correct answer key (A, B, C or D)
 - perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
 - episode: a list of episodes in which the instance is used. Only used for the train set

**Data Splits**

The dataset consists of a training set with labeled examples and a test set in two configurations:

 - raw data: includes the original data with no additional sampling
 - episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation

**Test Perturbations**

Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:

  - ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
 - Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
 -  EDAdelete: randomly deletes tokens in the text
 -  EDAswap: randomly swaps tokens in the text
 - BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
 - AddSent: replaces one or more choice options with a generated one

Introducing paper: TAPE: Assessing Few-shot Russian Language Understanding
CheGeKa
Full name: CheGeKa

CheGeKa is a Jeopardy!-like Russian QA dataset collected from the official Russian quiz database ChGK.

**Motivation**

The task can be considered the most challenging in terms of reasoning, knowledge and logic, as the task implies the QA pairs with a free response form (no answer choices); however, a long chain of causal relationships between facts and associations forms the correct answer.

The original corpus of the CheGeKa game was introduced in Mikhalkova (2021).

An example in English for illustration purposes:

```{
    'question_id': 3665,

    'question': 'THIS MAN replaced John Lennon when the Beatles got together for the last time.',

    'answer': 'Julian Lennon',

    'topic': 'The Liverpool Four',

    'author': 'Bayram Kuliyev',

    'tour_name': 'Jeopardy!. Ashgabat-1996',

    'tour_link': 'https://db.chgk.info/tour/ash96sv',

    'episode': [16],

    'perturbation': 'chegeka'
}```

**Data Fields**

 - question_id: an integer corresponding to the question id in the database
 - question: a string containing the question text
 - answer: a string containing the correct answer to the question
 - topic: a string containing the question category
 - author: a string with the full name of the author
 - tour_name: a string with the title of a tournament
 - tour link: a string containing the link to a tournament (None for the test set)
 - perturbation: a string containing the name of the perturbation applied to text. If no perturbation was applied, the dataset name is used
 - episode: a list of episodes in which the instance is used. Only used for the train set

**Data Splits**

The dataset consists of a training set with labeled examples and a test set in two configurations:

 - raw data: includes the original data with no additional sampling
 - episodes: data is split into evaluation episodes and includes several perturbations of test for robustness evaluation

**Test Perturbations**

Each training episode in the dataset corresponds to seven test variations, including the original test data and six adversarial test sets, acquired through the modification of the original test through the following text perturbations:

 - ButterFingers: randomly adds noise to data by mimicking spelling mistakes made by humans through character swaps based on their keyboard distance
 - Emojify: replaces the input words with the corresponding emojis, preserving their original meaning
 - EDAdelete: randomly deletes tokens in the text
 - EDAswap: randomly swaps tokens in the text
 - BackTranslation: generates variations of the context through back-translation (ru -> en -> ru)
 - AddSent: generates extra words or a sentence at the end of the question

Introducing paper: TAPE: Assessing Few-shot Russian Language Understanding