---
language:
- or
pretty_name: Odia Eval Benchmark
tags:
- odia
- oriya
- eval
- benchmark
- nlp
- multiple-choice
- question-answering
- generation
- translation
- ner
- classification
- math
- indic
task_categories:
- question-answering
- text-generation
- text-classification
- token-classification
- translation
- multiple-choice
language_creators:
- expert-generated
- machine-generated
- found
annotations_creators:
- expert-generated
- machine-generated
license:
- cc-by-4.0
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*.parquet
---

# Odia Eval Benchmark

## Dataset Summary

`odia-eval-benchmark` is a comprehensive, curated evaluation benchmark for **Odia (Oriya)** natural language understanding and generation. It consolidates **34 publicly available Odia datasets** into a single, normalized format covering **7 task families** and **121,947 evaluation rows**.

This benchmark was built from authoritative sources with three major improvements:

1. **Curated sources** - 8 datasets were re-pulled from their original/authoritative sources to fix broken links and corrupt data
2. **Machine-translated choices** - English multiple-choice options were translated to Odia with NLLB-200 (1.3B), enabling fully Odia-native MCQ evaluation
3. **Contamination control** - gold answers and references were screened against large Odia pretraining corpora; any row whose gold content a pretrained model may have memorized was excluded, so evaluation reflects true Odia capability rather than training-data recall

**Design decisions:**
- Fixed source URLs and re-pulled 8 datasets (SQuAD-v2, SQuAD-shifts, IndicQA, winogrande, truthfulqa, xsum, CNN-DailyMail, BBC XSum) from their canonical sources
- Dropped 3,865 SQuAD-v2 empty-gold rows and 401 IndicQA rows with corrupt answers
- Dropped 110 MMLU rows with corrupt answer mappings
- Translated 91,713 English MC options to Odia (NLLB-200, validated)
- Excluded 1,861 rows whose **gold content** overlaps pretraining data (contamination)
- Recovered 10,642 missing answer indexes (hellaswag + winogrande) by matching gold references to choices
- Regenerated globally unique row ids
- Added `script` column (Odia / Latin) for script-agnostic evaluation

## Languages

- **Language:** Odia (`or`, ISO 639-3: `ory`)
- **Script:** Odia (Odia-script); 9 rows use Latin-script math/code formulas (kept verbatim)

## Dataset Structure

### Data Fields

| Field | Type | Description |
|---|---|---|
| `dataset_name` | `string` | Source dataset identifier |
| `task` | `string` | Task family: `multiple_choice`, `qa`, `generation`, `math`, `classification`, `ner`, `translation` |
| `language` | `string` | `ory` |
| `split` | `string` | `test` or `validation` |
| `quality_tier` | `string` | `native`, `translated`, or `professional` |
| `source_url` | `string` | Canonical source URL |
| `license` | `string` | `cc-by-4.0` or `mit` |
| `prompt` | `string` | English prompt (if available) |
| `prompt_odia` | `string` | Odia prompt |
| `reference` / `reference_odia` | `string` | Reference / gold answer (EN / OD) |
| `context_en` / `context_odia` | `string` | Context passage (EN / OD) |
| `choices` / `choices_odia` | `list[string]` | MC options (EN / OD) |
| `answer` | `string` | Gold answer text |
| `answer_index` | `int` | Index of gold choice (MC) |
| `answer_letter` | `string` | Letter of gold choice (MC) |
| `answers` / `answer_start` | `list` | Extractive QA gold spans |
| `answer_type` | `string` | `index`, `text`, `span`, or `bool` |
| `is_bool` | `bool` | Yes/no question flag |
| `num_options` | `int` | Number of MC options |
| `tokens` / `ner_tags` / `ner_scheme` | `list` / `string` | NER tokens and BIO tags |
| `label` / `label_index` / `label_names` | `string` / `int` / `list` | Classification labels |
| `source_text` / `target_text` | `string` | Translation source / target |
| `domain` | `string` | Content domain |
| `subset` / `question_type` | `string` | Source subset / question type |
| `id` | `string` | Globally unique row id (`{dataset}/{index}`) |
| `script` | `string` | `Odia` or `Latin` |

### Task Distribution

| Task | Rows | Share |
|---|---|---|
| Multiple choice | 64,031 | 52.5% |
| QA (extractive / boolean / open) | 26,997 | 22.1% |
| Generation | 22,081 | 18.1% |
| Classification | 4,043 | 3.3% |
| Math | 2,638 | 2.2% |
| NER | 1,147 | 0.9% |
| Translation | 1,010 | 0.8% |

### Splits

| Split | Rows |
|---|---|
| test | 105,287 |
| validation | 16,660 |

### Quality Tiers

| Tier | Rows | Meaning |
|---|---|---|
| `professional` | 47,908 | Professionally translated by Sarvam AI |
| `native` | 47,075 | Natively Odia (collected in Odia) |
| `translated` | 26,964 | Community-translated (tripathysagar) |

## Included Datasets

| Dataset | Task | Rows | Split | License |
|---|---|---|---|---|
| bhram_il | QA | 2,087 | test | cc-by-4.0 |
| flores_plus | Translation | 1,010 | test | cc-by-4.0 |
| indic_copa | MC | 500 | test | cc-by-4.0 |
| indic_glue_csqa | QA | 1,975 | test | cc-by-4.0 |
| indic_glue_ner | NER | 153 | test | cc-by-4.0 |
| indic_glue_wstp | MC | 501 | test | cc-by-4.0 |
| indic_headline_gen | Generation | 6,485 | test | cc-by-4.0 |
| indic_qa | QA | 1,276 | test | cc-by-4.0 |
| indic_quest_odia | QA | 199 | test | cc-by-4.0 |
| indic_question_generation | Generation | 9,973 | test | cc-by-4.0 |
| indic_sentence_summarization | Generation | 5,623 | test | cc-by-4.0 |
| indic_squad_odia | QA | 7,736 | test | cc-by-4.0 |
| indic_xparaphrase | Classification | 1,995 | test | cc-by-4.0 |
| milu | MC | 4,520 | test | cc-by-4.0 |
| naamapadam_odia_ner | NER | 994 | test | cc-by-4.0 |
| odia_arc | MC | 1,170 | test | cc-by-4.0 |
| odia_gsm8k | Math | 1,319 | test | cc-by-4.0 |
| odia_hellaswag | MC | 19,825 | test | cc-by-4.0 |
| odia_news_classification | Classification | 2,048 | test | cc-by-4.0 |
| odia_truthfulqa | QA | 807 | validation | cc-by-4.0 |
| odia_truthfulqa_mc | MC | 809 | validation | cc-by-4.0 |
| odia_winogrande | MC | 3,034 | test | cc-by-4.0 |
| sarvam_arc_challenge | MC | 1,150 | test | mit |
| sarvam_arc_challenge_val | MC | 294 | validation | mit |
| sarvam_boolq | QA | 9,427 | test | mit |
| sarvam_boolq_val | QA | 3,270 | validation | mit |
| sarvam_gsm8k_indic | Math | 1,319 | test | mit |
| sarvam_indivibe_chat | QA | 100 | test | mit |
| sarvam_indivibe_code | QA | 40 | test | mit |
| sarvam_indivibe_math | QA | 40 | test | mit |
| sarvam_indivibe_stem | QA | 40 | test | mit |
| sarvam_mmlu_indic | MC | 14,003 | test | mit |
| sarvam_mmlu_indic_val | MC | 281 | validation | mit |
| sarvam_triviaqa | MC | 17,944 | test | mit |

## Dataset Creation

### Why this dataset?

Odia (ISO 639-3: `ory`) is spoken by ~38 million people, yet it is severely underrepresented in NLP evaluation. Existing benchmarks are scattered across repositories with inconsistent formats, broken links, and English-only prompts for multiple-choice tasks. This benchmark unifies them.

### Contamination Control

Gold content (answers, references, target translations, gold choices) was screened for **substring containment** against a large-scale Odia web-text corpus (12.4M+ rows). A row was excluded if:

- its gold text matched a corpus row exactly (>= 40 chars), **or**
- every 64-char window of its gold text appeared in a single corpus shard (i.e., the gold text is genuinely contained in the corpus)

Rows whose **input** (prompt/context) matched the corpus but whose gold did not were **kept** - input passages being in the pretraining corpus is expected and does not leak the answer. This distinction is why only 1,861 of 15,236 raw matches were excluded. Details:

- Excluded: 1,861 rows (gold matches), dominated by headline generation (652), question generation (580), and sentence summarization (425)
- Kept: 13,375 rows with benign input-only overlap

### Translation

English MC options (91,713 unique strings, 100,816 occurrences) were translated to Odia with **NLLB-200 (distilled 1.3B, int8)** using CTranslate2 with length-band batching. A validation gate checked for:

- empty output (253 -> fell back to source)
- untranslated output (1946 math/chem formulas -> kept verbatim)
- blown-up short strings (103 -> fell back to source)
- sibling choice collisions (19 -> fell back to source)

Math and chemistry formulas (`32°`, `H₂ + O₂`, `NaCl`) are kept in their original form - they are language-independent.

### Quality Gates

- 0 rows with out-of-bounds or missing answer index (10,642 recovered via gold-reference matching)
- 0 rows with no Odia content at all
- 100% of MCQ rows carry a valid `answer_index`
- Globally unique row ids

## Usage

### With Hugging Face `datasets`

```python
from datasets import load_dataset

ds = load_dataset("MaelisResearch/odia-eval-benchmark", split="train")

# MCQ rows with fully Odia options
mc = ds.filter(lambda x: x["task"] == "multiple_choice")
print(mc[0])
```

### Evaluation

The benchmark is designed to be used with an Odia-aware evaluation harness. A reference harness (`eval_harness.py`) is maintained in the associated benchmark repository and scores:

- **Multiple choice** - accuracy on `answer_index`
- **QA** - span F1 (SQuAD-style) for extractive; exact-match for boolean
- **Math** - exact match on the final number
- **Classification** - accuracy on `label_index`
- **NER** - token-level BIO F1
- **Translation** - chrF / BLEU
- **Generation** - reference-free (optional LLM judge)

## Considerations and Limitations

- **Contamination is controlled, not eliminated** - the screen covers a specific Odia web-text corpus; overlap with other corpora is possible
- **Translated MC options** may differ slightly from native phrasing - models that rely on English-style semantics may see a small distribution shift
- **Math/chem formulas** are intentionally untranslated (language-independent)
- **IndiVibe rows** (220) are judge-scored conversation/chat items with no gold answer - they are included for reference-style evaluation
- **`quality_tier`** lets you filter by provenance: use `professional` for the highest-quality subsets, `native` for natively collected data

## License

Each row carries its source license (`cc-by-4.0` or `mit`); consumers must respect the license of each constituent dataset. When redistributing, retain the `license` and `source_url` fields.

## Citation

```bibtex
@misc{maelis-odia-eval,
  title = {Odia Eval Benchmark},
  author = {Maelis Research},
  year = {2026},
  howpublished = {https://huggingface.co/datasets/MaelisResearch/odia-eval-benchmark}
}
```

## Contact

**Maelis Research** - publishing high-quality Indic language resources for open NLP research.

With this, we begin our journey towards revolutionising AI for the Odia language. Jai Jagannath!