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LinCE Hindi-English LID Dataset
Data Science and Analytics
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About
This dataset provides Hindi-English language identification data, specifically designed for testing machine learning models [1]. It is an integral part of the broader LinCE (Linguistic Code-switching Evaluation) collection, which is an expansive compilation of language technologies and data [2]. This resource facilitates a multitude of purposes, including language identification (LID), part-of-speech (POS) tagging, Named-Entity Recognition (NER), and sentiment analysis (SA) [2]. It is highly valuable for training robust models efficiently with machine learning techniques, enabling the automatic detection and classification of various linguistic tasks [2]. The LinCE collection itself explores six distinct languages: Spanish, Hindi, Nepali, Spanish-English, Hindi-English, and Spanish Multi-Source-English (MSAEA), making this dataset a valuable tool for those looking to unlock the power of language through analysis within a diverse linguistic context [2].
Columns
The dataset contains the following key columns:
- words: The textual words within the dataset, represented as a string [1].
- idx: An index identifier for each record [1].
- lid: The language identification label assigned to the text [1].
Distribution
The data file is typically provided in a CSV format [3]. This specific test dataset contains 1,853 individual records or rows [1]. While a sample file will be updated separately to the platform, the structured nature of this data allows for straightforward integration into analytical workflows [3].
Usage
This dataset is ideal for a variety of applications and use cases, including:
- Testing machine learning models developed for language identification [1].
- Training ML models to automatically detect and classify tasks such as POS tagging or NER from different language variations [2].
- Building cross-linguistic models across multiple languages [2].
- Exploratory research within natural language processing (NLP) [2].
- Developing multilingual sentiment analysis systems [2].
- Training models to identify and classify named entities across multiple languages, regardless of the specific language or coding scheme [2].
- Developing AI-powered cross-lingual translators that accurately translate text between languages [2].
Coverage
The dataset specifically focuses on Hindi-English language identification [1]. As part of the wider LinCE project, it aligns with a collection that encompasses Spanish, Hindi, Nepali, Spanish-English, Hindi-English, and Spanish Multi-Source-English (MSAEA) [2]. While the listed region for the dataset's availability is GLOBAL, specific geographic or time range coverage for the data content itself is not detailed [4].
License
CC0
Who Can Use It
This dataset is particularly useful for:
- Data scientists and machine learning engineers focused on natural language processing.
- NLP researchers and linguists interested in language analysis, code-switching, and multilingual models [2].
- Developers and academics looking to build and test models for language identification, part-of-speech tagging, named-entity recognition, and sentiment analysis [2].
- Anyone aiming to uncover the insights from language data and develop advanced multilingual AI applications [2].
Dataset Name Suggestions
- Hindi-English Language ID Test Data
- LinCE Hindi-English LID Dataset
- Multilingual Code-switching Evaluation: Hindi-English
- NLP Hindi-English Language Identifier
- Cross-lingual Language Detection (Hindi-English)
Attributes
Original Data Source: LinCE (Linguistic Code-switching Evaluation)
QUALITY