# Mldr Eval

> Evaluates retrieval over long multilingual documents (up to 8,192 tokens), testing a model's ability to capture information from extended contexts across multiple languages. Use when the user wants to benchmark on MLDR, or asks about evaluating this task. Reports nDCG@10.

- Skill: `qhjqhj00/mldr-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/mldr-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/mldr-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/mldr-eval

---


# mldr-eval

> M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation — Chen et al. (2024) (arXiv:2402.03216, 2024)

## What this evaluates

Evaluates retrieval over long multilingual documents (up to 8,192 tokens), testing a model's ability to capture information from extended contexts across multiple languages.

## Datasets

- **MLDR** — total ?; splits: test (-1)

## Metrics

- `nDCG@10` **(primary)** — range: [0, 1]
  - Normalized Discounted Cumulative Gain at rank 10, measuring the quality of ranked long-document retrieval results.

## Input / output format

**Input**: Long multilingual documents (from Wikipedia, Wudao, mC4) and corresponding queries.

**Output**: A ranked list of retrieved long documents.

## Scoring recipe

```python
def compute_ndcg_at_10(retrieved_ids, relevant_ids):
    dcg = 0.0
    for i, doc_id in enumerate(retrieved_ids[:10]):
        if doc_id in relevant_ids:
            dcg += 1.0 / math.log2(i + 2)
    idcg = sum(1.0 / math.log2(i + 2) for i in range(min(len(relevant_ids), 10)))
    return dcg / idcg if idcg > 0 else 0.0
```

## Common pitfalls

- Sparse retrieval surprisingly outperforms dense retrieval on this benchmark, contrary to typical dense-retrieval assumptions.
- The model's max length is fixed at 8192 tokens, which may truncate longer documents if not handled.

## Evidence (verbatim from paper)

> We evaluate the retrieval performance with longer sequences with two benchmarks: MLDR (Multilingual Long-Doc Retrieval), which is curated by the multilingual articles from Wikipedia, Wudao and mC4... measured by nDCG@10.

## Citation

```bibtex
@misc{chen2024m3embedding,
  title={M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
  author={Chen et al. (2024)},
  year={2024},
  note={arXiv:2402.03216}
}
```

- arXiv: 2402.03216