Omkar2118/datasets huggingface dataset:
TextBox 2.0

**TextBox 2.0** is a comprehensive and unified library for text generation, focusing on the use of pre-trained language models (PLMs). The library covers 13 common text generation tasks and their corresponding 83 datasets and further incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs.

Source: [TextBox 2.0: A Text Generation Library with Pre-trained Language Models](https://arxiv.org/pdf/2212.13005v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.13005v1.pdf](https://arxiv.org/pdf/2212.13005v1.pdf)

Introducing paper: TextBox 2.0: A Text Generation Library with Pre-trained Language Models
XAlign

It consists of an extensive collection of a high quality cross-lingual fact-to-text dataset in 11 languages: Assamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and monolingual dataset in English (en). This is the Wikipedia text <--> Wikidata KG aligned corpus used to train the data-to-text generation model. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.

## Data Format
Dataset is publicly available [here](https://github.com/tushar117/XAlign). Each directory contains language specific dataset (refered through language ISO code) and contains of three files:

- train.jsonl
- test.jsonl
- val.jsonl

Data stored in the above files are of JSON Line (jsonl) format.

### Record structure (JSON structure)
Each record consist of the following entries:

- sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.) 
- `facts` (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary.
- language (string) : Language identifier.

The `facts` key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries:

- subject (string) : central entity.
- object (string) : entity or a piece of information about the subject.
- predicate (string) : relationship that connects the subject and the object.
- qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of 
qualifier where each record is a dictionary. The dictionary contains two keys: `qualifier_predicate` to represent property of qualifer and `qualifier_object` to store value for the qualifier's predicate. 

### Examples
Example from English dataset
```
{
  "sentence": "Mark Paul Briers (born 21 April 1968) is a former English cricketer.",
  "facts": [
    {
      "subject": "Mark Briers",
      "predicate": "date of birth",
      "object": "21 April 1968",
      "qualifiers": []
    },
    {
      "subject": "Mark Briers",
      "predicate": "occupation",
      "object": "cricketer",
      "qualifiers": []
    },
    {
      "subject": "Mark Briers",
      "predicate": "country of citizenship",
      "object": "United Kingdom",
      "qualifiers": []
    }
  ],
  "language": "en"
}
```
Example from one of the low-resource languages (i.e. Hindi)
```
{
  "sentence": "बोरिस पास्तेरनाक १९५८ में साहित्य के क्षेत्र में नोबेल पुरस्कार विजेता रहे हैं।",
  "facts": [
    {
      "subject": "Boris Pasternak",
      "predicate": "nominated for",
      "object": "Nobel Prize in Literature",
      "qualifiers": [
        {
          "qualifier_predicate": "point in time",
          "qualifier_subject": "1958"
        }
      ]
    }
  ],
  "language": "hi"
}
```

Introducing paper: XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages