README.md

metadata

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

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

{
  "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.