# BordIRLines Dataset

## Dataset Summary

The **BordIRLines Dataset** is an information retrieval (IR) dataset constructed from various language corpora. It contains queries and corresponding ranked docs along with their relevance scores. The dataset includes multiple languages, including English, Arabic, Spanish, and others, and is split across different sources like LLM-based outputs.
Each `doc` is a passage from a Wikipedia article.

### Languages

The dataset includes docs and queries in the following __languages__:

* `en`: English
* `zht`: Traditional Chinese
* `ar`: Arabic
* `zhs`: Simplified Chinese
* `es`: Spanish
* `fr`: French
* `ru`: Russian
* `hi`: Hindi
* `ms`: Malay
* `sw`: Swahili
* `az`: Azerbaijani
* `ko`: Korean
* `pt`: Portuguese
* `hy`: Armenian
* `th`: Thai
* `uk`: Ukrainian
* `ur`: Urdu
* `sr`: Serbian
* `iw`: Hebrew
* `ja`: Japanese
* `hr`: Croatian
* `tl`: Tagalog
* `ky`: Kyrgyz
* `vi`: Vietnamese
* `fa`: Persian
* `tg`: Tajik
* `mg`: Malagasy
* `nl`: Dutch
* `ne`: Nepali
* `uz`: Uzbek
* `my`: Burmese
* `da`: Danish
* `dz`: Dzongkha
* `id`: Indonesian
* `is`: Icelandic
* `tr`: Turkish
* `lo`: Lao
* `sl`: Slovenian
* `so`: Somali
* `mn`: Mongolian
* `bn`: Bengali
* `bs`: Bosnian
* `ht`: Haitian Creole
* `el`: Greek
* `it`: Italian
* `to`: Tonga
* `ka`: Georgian
* `sn`: Shona
* `sq`: Albanian
* `control`: see below

The **control** language is English, and contains the queries for all 251 territories. In contrast, **en** is only the 38 territories which have an English-speaking claimant.

## Systems
Currently supported IR systems:
* `openai`: embed using OpenAI's model `text-embedding-3-large`, cosine similarity

## Modes
Considering a user query in language `l` on a territory `t`, there are 4 modes for the IR.
* `qlang`: consider passages in `{l}`. This is monolingual IR (the default).
* `qlang_en`: consider passages in either `{l, en}`.
* `en`: consider passages in `{en}`.
* `rel_langs`:  consider passages in all relevant languages to `t` + `en`, so `{l1, l2, ..., en}`. This is a set, so `en` will not be duplicated if it already is relevant.

## Dataset Structure

### Data Fields

The dataset consists of the following fields:
* `query_id (string)`: The id of the query.
* `query (string)`: The query text as provided by the `queries.tsv` file.
* `territory (string)`: The territory of the query hit.
* `rank (int32)`: The rank of the document for the corresponding query.
* `score (float32)`: The relevance score of the document as provided by the search engine or model.
* `doc_id (string)`: The unique identifier of the article.
* `doc_text (string)`: The full text of the corresponding article or document.

### Download Structure

The dataset is structured as follows:

```
data/
  {lang}/
    {lang}_docs.json
    {system}/
      {mode}/
        {lang}_query_hits.tsv
...
  queries.tsv
```

* `queries.tsv`: Contains the list of query IDs and their associated query texts.
* `{lang}\_docs.json`: A JSON file with docs for a specific language.
* `{lang}\_query_hits.tsv`: A TSV file with relevance scores and hit ranks for queries.

Currently, there are 50 langs * 1 system * 4 modes = 200 query hit TSV files.

## Example Usage

```python
from datasets import load_dataset

# load DatasetDict with all 4 modes, for control language, 10 hits
dsd_control = load_dataset("borderlines/bordirlines", "control")

# load Dataset for English, with rel_langs mode, 50 hits
ds_en = load_dataset("borderlines/bordirlines", "en", split="openai.rel_langs", n_hits=50)
# load Dataset for Simplified Chinese, en mode
ds_zhs1 = load_dataset("borderlines/bordirlines", "zhs", split="openai.en")
# load Dataset for Simplified Chinese, qlang mode
ds_zhs2 = load_dataset("borderlines/bordirlines", "zhs", split="openai.qlang")
```

Note that for `dsd_control`, `en`