---
language:
- multilingual
tags:
- translation
- quality-estimation
- claude-haiku
license: mit
---

# natgillin/translations — Claude-Haiku-filtered bitext

Rows from `natgillin/translations-raw` that scored `> 0.8` on Claude Haiku 4.5 translation-quality evaluation. Globally deduplicated by xxh3-64 of `source\ntarget`.

## Schema

Each parquet file has 7 columns:

| column | type | description |
|---|---|---|
| `source` | string | source-language sentence |
| `target` | string | target-language sentence |
| `source_lang` | string | ISO-639-3 source language code |
| `target_lang` | string | ISO-639-3 target language code |
| `origin` | string | upstream OPUS corpus tag (e.g. `opus-nllb`) |
| `xxhash_intdigest` | uint64 | hash of the pair (see below) |
| `claude_haiku_score` | float32 | quality score in `[0.0, 1.0]` from Claude Haiku 4.5 |

The split is `claude_haiku_score > 0.8`: kept rows live in `natgillin/translations`, rejected rows in `natgillin/translations-rejected`.

## How `xxhash_intdigest` is computed

```python
import xxhash

def row_hash(source: str, target: str) -> int:
    return xxhash.xxh3_64(f"{source}\n{target}".encode("utf-8")).intdigest()
```

The hash is the **xxh3-64** intdigest of `f"{source}\n{target}"` encoded as UTF-8.
It is stable across runs and lets you deduplicate or join rows by content.

## How `claude_haiku_score` is computed

Rows are batched (default 20 / call) and sent to Claude Haiku 4.5 with the prompt below.
The model returns a JSON array of floats in `[0.0, 1.0]`; we attach each float to its
corresponding row as `claude_haiku_score`.

### Exact prompt template

```
You are a translation quality judge. Rate each TARGET translation's fluency and faithfulness to its SOURCE on a scale 0.00 to 1.00.

Rubric:
- 1.00: native-fluent target, accurately conveys source meaning, no errors
- 0.80: mostly fluent, minor errors that don't impede understanding (CUTOFF)
- 0.60: understandable but awkward; some meaning loss
- 0.40: broken grammar or significant meaning drift
- 0.20: mostly garbled / barely comprehensible
- 0.00: empty, wrong language, or not a translation at all

Source language: {src_lang}. Target language: {tgt_lang}.

Translations (return scores in the same order):
{pairs_block}

Output ONLY a JSON array of exactly {n} floats between 0.0 and 1.0. No prose. Example: [0.95, 0.4, 0.85, ...]
```

### JSON schema sent alongside the prompt

```json
{
  "type": "object",
  "properties": {
    "scores": {
      "type": "array",
      "items": {
        "type": "number",
        "minimum": 0.0,
        "maximum": 1.0
      }
    }
  },
  "required": [
    "scores"
  ],
  "additionalProperties": false
}
```

## Source

All rows come from `natgillin/translations-raw` (an OPUS / mtdata mirror).
Before any Haiku call, rows are deduplicated globally by `xxhash_intdigest`
so identical pairs are scored exactly once.