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--- |
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license: cc-by-4.0 |
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language: |
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- hi |
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- en |
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tags: |
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- code-mixing |
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- Hinglish |
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- expert-annotated |
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size_categories: |
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- 1M<n<10M |
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configs: |
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- config_name: LID |
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data_files: |
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- split: train |
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path: "LID_train.csv" |
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- split: test |
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path: "LID_test.csv" |
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- config_name: POS |
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data_files: |
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- split: train |
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path: "POS_train.csv" |
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- split: test |
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path: "POS_test.csv" |
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- config_name: NER |
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data_files: |
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- split: train |
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path: "NER_train.csv" |
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- split: test |
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path: "NER_test.csv" |
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- config_name: Translation |
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data_files: |
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- split: train |
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path: "Translation_train.csv" |
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- split: test |
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path: "Translation_test.csv" |
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--- |
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## Dataset Details |
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**COMI-LINGUA** (**CO**de-**MI**xing and **LING**uistic Insights on Natural Hinglish **U**sage and **A**nnotation) is a high-quality Hindi-English code-mixed dataset, manually annotated by three annotators. It serves as a benchmark for multilingual NLP models by covering multiple foundational tasks. |
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**COMI-LINGUA provides annotations for several key NLP tasks:** |
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* **Language Identification (LID):** Token-wise classification of Hindi, English, and other linguistic units.<br> |
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Initial predictions were generated using the [Microsoft LID tool](https://github.com/microsoft/LID-tool), which annotators then reviewed and corrected. |
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* **Matrix Language Identification (MLI):** Sentence-level annotation of the dominant language. |
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* **Part-of-Speech (POS) Tagging:** Syntactic categorization for linguistic analysis.<br> |
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Tags were pre-assigned using the [CodeSwitch NLP library](https://github.com/sagorbrur/codeswitch), which annotators then reviewed and corrected. |
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* **Named Entity Recognition (NER):** Identification of entities in Hinglish text.<br> |
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Each token is pre-assigned a language tag using the [CodeSwitch NLP library](https://github.com/sagorbrur/codeswitch), which annotators then reviewed and corrected. |
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* **Translation:** Parallel translation of sentences in Romanized Hindi and Devanagari Hindi and English languages.<br> |
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Initial translation predictions were generated using the [Llama 3.3 LLM](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) and refined by human annotators for accuracy. |
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### Dataset Description |
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- **Curated by:** [Lingo Research Group at IIT Gandhinagar, India](https://lingo.iitgn.ac.in/) |
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- **Funded by:** SERB |
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- **Language(s) (NLP):** Bilingual (Hindi [hi], English [en]) |
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- **License:** cc-by-4.0 |
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