---
configs:
- config_name: default
  data_files:
  - split: train
    path: final_dataset.parquet
license: apache-2.0
task_categories:
- text-retrieval
language:
- ta
- en
size_categories:
- 1K<n<10K
---

# Tamil-English Code-Mixed Retrieval Evaluation Dataset

This dataset is a publication-quality multilingual and code-mixed retrieval evaluation benchmark generated from parallel English-Tamil document datasets. It is designed to evaluate information retrieval systems, dense retrievers, and cross-lingual models on natural queries in various language formats.

## Dataset Provenance & Source
- **Original Source Dataset:** [mozhi-ai/tamil-corpus](https://huggingface.co/datasets/mozhi-ai/tamil-corpus) (Mozhi Tamil Corpus).
- **English Translations:** Machine-translated into English using AI4Bharat's [IndicTrans2](https://github.com/AI4Bharat/IndicTrans2) translation model.
- **Intermediate Corpus:** [nlpctx/tamil-english-corpus](https://huggingface.co/datasets/nlpctx/tamil-english-corpus) (Tamil-English Retrieval Corpus).
- **Original Dataset License:** Apache 2.0.
- **Final Dataset License:** Apache 2.0.

## Dataset Statistics
- **Total documents in original dataset (`nlpctx/tamil-english-corpus`):** `4,591` documents.
- **Documents processed from original dataset:** `2,183` documents (representing the subset successfully completed by the pipeline).
- **Total generated retrieval queries (final dataset rows):** `9,299` query rows.
- **Average query variants per document:** `4.26` queries.

## Dataset Summary
For each processed document from the Mozhi AI parallel corpus, realistic retrieval search queries (representing search intents for search engines, forums, or AI assistants) were generated in four variants:
- **English (`query_en`)**: Standard English retrieval query.
- **Tamil (`query_ta`)**: Native Tamil script retrieval query.
- **Tamil Romanized (`query_ta_romanized`)**: Transliterated Latin-script query naturally typed.
- **Tamil-English Code-Mixed (`query_ta_codemix`)**: Tanglish code-mixed query naturally mixing Tamil syntactic structure with English words.
- **Answer Text (`answer_text`)**: Concise ground truth answer snippet extracted directly from the document.

## Data Schema
Each row in the dataset contains the following fields:

| Column | Type | Description |
| :--- | :--- | :--- |
| `doc_id` | string | Unique document identifier (corresponds to source corpus `id`) |
| `query_en` | string | English search query |
| `query_ta` | string | Native Tamil search query |
| `query_ta_romanized` | string | Transliterated Tamil search query (Tanglish) |
| `query_ta_codemix` | string | Code-mixed Tamil-English search query |
| `answer_text` | string | Snippet answering the query (ground-truth) |
| `text` | string | Full Tamil source document |
| `english_doc` | string | Full English parallel document |
| `source` | string | Source corpus identifier |
| `url` | string | URL metadata of source document |
| `license` | string | License of the source text |
| `date_collected` | string | Date when document was collected |
| `language_score` | float | Quality score of document language identification |
| `quality_score` | float | Document quality rating |
| `domain` | string | Domain classification of text |
| `register` | string | Document register style |
| `title` | string | Document title (if available) |
| `tamil_char_ratio` | float | Ratio of Tamil characters in native document |
| `unique_word_ratio` | float | Ratio of unique words in native document |
| `avg_word_length` | float | Average word length of native document |
| `word_count` | float | Word count of native document |
| `char_count` | float | Character count of native document |
| `sentence_count` | float | Sentence count of native document |
| `pii_count` | float | Detected PII count in document |
| `has_code_switching` | boolean | Whether source document contains code-switching |
| `generation_model` | string | LLM used to generate the retrieval queries |
| `generation_timestamp` | string | Timestamp of query generation |
## Generation & Quality Assurance
1. **Dynamic Key Rotation & Model Fallback**: Generation leveraged Gemma 4 models (`gemma-4-26b-a4b-it`, `gemma-4-31b-it`) and Gemini 3 models under rate-limit rotation.
2. **Quality-preserving Pydantic Validators**: Every generated row passed strict validations ensuring correct script blocks (Tamil Unicode vs. Latin script boundaries) and length checks.
3. **Stopword-filtered Jaccard Similarity Deduplication**: Rejects near-duplicate queries within and across document chunks using a Jaccard threshold of `0.85`.

## Citations
If you use this dataset, please cite the original datasets and models:

### Mozhi Tamil Corpus
```bibtex
@misc{mozhi_ai_tamil_corpus_2026,
  title={mozhi-ai Tamil Corpus},
  author={mozhi-ai contributors},
  year={2026},
  url={https://huggingface.co/datasets/mozhi-ai/tamil-corpus}
}
```

### IndicTrans2 Machine Translation Model
```bibtex
@article{gala2023indictrans,
  title={IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
  author={Jay Gala and Pranjal A. Chitale and A. K. Raghavan and Varun Gumma and Sumanth Doddapaneni and Aswanth Kumar M and Janki Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M. Khapra and Raj Dabre and Anoop Kunchukuttan},
  journal={Transactions on Machine Learning Research},
  year={2023}
}
```