|
--- |
|
configs: |
|
- config_name: all |
|
data_files: |
|
- path: |
|
- all.txt.zst |
|
split: train |
|
default: true |
|
- config_name: ar |
|
data_files: |
|
- path: |
|
- ar.txt.zst |
|
split: train |
|
- config_name: az |
|
data_files: |
|
- path: |
|
- az.txt.zst |
|
split: train |
|
- config_name: bg |
|
data_files: |
|
- path: |
|
- bg.txt.zst |
|
split: train |
|
- config_name: bn |
|
data_files: |
|
- path: |
|
- bn.txt.zst |
|
split: train |
|
- config_name: ca |
|
data_files: |
|
- path: |
|
- ca.txt.zst |
|
split: train |
|
- config_name: cs |
|
data_files: |
|
- path: |
|
- cs.txt.zst |
|
split: train |
|
- config_name: da |
|
data_files: |
|
- path: |
|
- da.txt.zst |
|
split: train |
|
- config_name: de |
|
data_files: |
|
- path: |
|
- de.txt.zst |
|
split: train |
|
- config_name: el |
|
data_files: |
|
- path: |
|
- el.txt.zst |
|
split: train |
|
- config_name: en |
|
data_files: |
|
- path: |
|
- en.txt.zst |
|
split: train |
|
- config_name: es |
|
data_files: |
|
- path: |
|
- es.txt.zst |
|
split: train |
|
- config_name: et |
|
data_files: |
|
- path: |
|
- et.txt.zst |
|
split: train |
|
- config_name: fa |
|
data_files: |
|
- path: |
|
- fa.txt.zst |
|
split: train |
|
- config_name: fi |
|
data_files: |
|
- path: |
|
- fi.txt.zst |
|
split: train |
|
- config_name: fr |
|
data_files: |
|
- path: |
|
- fr.txt.zst |
|
split: train |
|
- config_name: he |
|
data_files: |
|
- path: |
|
- he.txt.zst |
|
split: train |
|
- config_name: hi |
|
data_files: |
|
- path: |
|
- hi.txt.zst |
|
split: train |
|
- config_name: hu |
|
data_files: |
|
- path: |
|
- hu.txt.zst |
|
split: train |
|
- config_name: hy |
|
data_files: |
|
- path: |
|
- hy.txt.zst |
|
split: train |
|
- config_name: id |
|
data_files: |
|
- path: |
|
- id.txt.zst |
|
split: train |
|
- config_name: is |
|
data_files: |
|
- path: |
|
- is.txt.zst |
|
split: train |
|
- config_name: it |
|
data_files: |
|
- path: |
|
- it.txt.zst |
|
split: train |
|
- config_name: ja |
|
data_files: |
|
- path: |
|
- ja.txt.zst |
|
split: train |
|
- config_name: ka |
|
data_files: |
|
- path: |
|
- ka.txt.zst |
|
split: train |
|
- config_name: kk |
|
data_files: |
|
- path: |
|
- kk.txt.zst |
|
split: train |
|
- config_name: ko |
|
data_files: |
|
- path: |
|
- ko.txt.zst |
|
split: train |
|
- config_name: lt |
|
data_files: |
|
- path: |
|
- lt.txt.zst |
|
split: train |
|
- config_name: lv |
|
data_files: |
|
- path: |
|
- lv.txt.zst |
|
split: train |
|
- config_name: mk |
|
data_files: |
|
- path: |
|
- mk.txt.zst |
|
split: train |
|
- config_name: ml |
|
data_files: |
|
- path: |
|
- ml.txt.zst |
|
split: train |
|
- config_name: mr |
|
data_files: |
|
- path: |
|
- mr.txt.zst |
|
split: train |
|
- config_name: ne |
|
data_files: |
|
- path: |
|
- ne.txt.zst |
|
split: train |
|
- config_name: nl |
|
data_files: |
|
- path: |
|
- nl.txt.zst |
|
split: train |
|
- config_name: 'no' |
|
data_files: |
|
- path: |
|
- no.txt.zst |
|
split: train |
|
- config_name: pl |
|
data_files: |
|
- path: |
|
- pl.txt.zst |
|
split: train |
|
- config_name: pt |
|
data_files: |
|
- path: |
|
- pt.txt.zst |
|
split: train |
|
- config_name: ro |
|
data_files: |
|
- path: |
|
- ro.txt.zst |
|
split: train |
|
- config_name: ru |
|
data_files: |
|
- path: |
|
- ru.txt.zst |
|
split: train |
|
- config_name: sk |
|
data_files: |
|
- path: |
|
- sk.txt.zst |
|
split: train |
|
- config_name: sl |
|
data_files: |
|
- path: |
|
- sl.txt.zst |
|
split: train |
|
- config_name: sq |
|
data_files: |
|
- path: |
|
- sq.txt.zst |
|
split: train |
|
- config_name: sr |
|
data_files: |
|
- path: |
|
- sr.txt.zst |
|
split: train |
|
- config_name: sv |
|
data_files: |
|
- path: |
|
- sv.txt.zst |
|
split: train |
|
- config_name: ta |
|
data_files: |
|
- path: |
|
- ta.txt.zst |
|
split: train |
|
- config_name: th |
|
data_files: |
|
- path: |
|
- th.txt.zst |
|
split: train |
|
- config_name: tr |
|
data_files: |
|
- path: |
|
- tr.txt.zst |
|
split: train |
|
- config_name: uk |
|
data_files: |
|
- path: |
|
- uk.txt.zst |
|
split: train |
|
- config_name: ur |
|
data_files: |
|
- path: |
|
- ur.txt.zst |
|
split: train |
|
- config_name: vi |
|
data_files: |
|
- path: |
|
- vi.txt.zst |
|
split: train |
|
- config_name: zh |
|
data_files: |
|
- path: |
|
- zh.txt.zst |
|
split: train |
|
language: |
|
- multilingual |
|
- ar |
|
- az |
|
- bg |
|
- bn |
|
- ca |
|
- cs |
|
- da |
|
- de |
|
- el |
|
- en |
|
- es |
|
- et |
|
- fa |
|
- fi |
|
- fr |
|
- he |
|
- hi |
|
- hu |
|
- hy |
|
- id |
|
- is |
|
- it |
|
- ja |
|
- ka |
|
- kk |
|
- ko |
|
- lt |
|
- lv |
|
- mk |
|
- ml |
|
- mr |
|
- ne |
|
- nl |
|
- 'no' |
|
- pl |
|
- pt |
|
- ro |
|
- ru |
|
- sk |
|
- sl |
|
- sq |
|
- sr |
|
- sv |
|
- ta |
|
- th |
|
- tr |
|
- uk |
|
- ur |
|
- vi |
|
- zh |
|
task_categories: |
|
- text-generation |
|
- text-classification |
|
- text-retrieval |
|
size_categories: |
|
- 1M<n<10M |
|
--- |
|
# Multilingual Sentences |
|
|
|
Dataset contains sentences from 50 languages, grouped by their two-letter ISO 639-1 codes. The "all" configuration includes sentences from all languages. |
|
|
|
## Dataset Overview |
|
|
|
Multilingual Sentence Dataset is a comprehensive collection of high-quality, linguistically diverse sentences. Dataset is designed to support a wide range of natural language processing tasks, including but not limited to language modeling, machine translation, and cross-lingual studies. |
|
|
|
## Methods |
|
|
|
Rigorous methodology consisted of three main stages: text preprocessing, language detection, and dataset processing. |
|
|
|
### Text Preprocessing |
|
|
|
Sophisticated text cleaning pipeline using the textacy library, which included: |
|
|
|
- Removal of HTML tags, email addresses, URLs, and emojis |
|
- Unicode and whitespace normalization |
|
- Standardization of punctuation and word formats |
|
|
|
### Language Detection |
|
|
|
Google CLD3 library utilized for accurate language identification: |
|
|
|
- Implemented NNetLanguageIdentifier |
|
- Configured for processing texts between 0-1000 bytes |
|
- Included reliability assessment for each language detection |
|
|
|
### Dataset Processing |
|
|
|
Workflow for dataset creation involved the following steps: |
|
|
|
1. Streamed loading of the LinguaNova multilingual dataset |
|
2. Application of the text preprocessing pipeline |
|
3. Sentence segmentation using PyICU for accurate boundary detection |
|
4. Quality filtering: |
|
- Length constraint (maximum 2048 characters per sentence) |
|
- High-reliability language verification |
|
5. Extraction of unique sentences |
|
6. Random shuffling for unbiased sampling |
|
7. Generation of language-specific files |
|
|
|
## Technical Details |
|
|
|
### Libraries and Tools |
|
|
|
- textacy: Advanced text preprocessing |
|
- Google CLD3: State-of-the-art language detection |
|
- Hugging Face datasets: Efficient data handling and processing |
|
- SentenceBreaker: Accurate sentence segmentation |
|
|
|
### Implementation Notes |
|
|
|
- Process was executed consistently across all 50 languages to ensure uniformity and high quality in the multilingual dataset preparation. |
|
- Special attention was given to maintaining the integrity of each language's unique characteristics throughout the processing pipeline. |
|
|
|
## Data Splits |
|
|
|
Dataset is organized into the following splits: |
|
|
|
- Individual language files: Contains sentences for each of the 50 languages |
|
- "all" configuration: Aggregates sentences from all languages into a single dataset |
|
|
|
## Limitations and Biases |
|
|
|
While extensive efforts were made to ensure dataset quality, users should be aware of potential limitations: |
|
|
|
- Language detection accuracy may vary for very short texts or closely related languages |
|
- Dataset may not fully represent all dialects or regional variations within each language |
|
- Potential biases in the original LinguaNova dataset could be carried over |
|
|
|
## Ethical Considerations |
|
|
|
Users of this dataset should be mindful of: |
|
|
|
- Potential biases in language representation |
|
- Need for responsible use in AI applications, especially in multilingual contexts |
|
- Privacy considerations, although personal identifiable information should have been removed |
|
|
