185 lines
5.6 KiB
Markdown
185 lines
5.6 KiB
Markdown
# WMT2024++
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## 概述
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WMT2024++ 是一个基于 WMT 2024 新闻翻译任务的综合性机器翻译基准测试。它支持以英语为源语言的 54 个语言对，可用于评估模型在多种目标语言上的翻译质量。
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## 任务描述
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- **任务类型**：机器翻译
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- **输入**：带有翻译提示的英文源文本
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- **输出**：目标语言的翻译文本
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- **语言对**：54 个（英语到 54 种目标语言）
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## 主要特性
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- 广泛的多语言覆盖（54 种目标语言）
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- 新闻领域文本，贴近实际应用场景
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- 多种评估指标（BLEU、BERTScore、COMET）
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- 标准化的提示模板，确保评估一致性
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- 支持批量评分以提升效率
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## 评估说明
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- 默认配置使用 **0-shot** 评估
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- 评估指标：**BLEU**、**BERTScore**（XLM-RoBERTa）、**COMET**（wmt22-comet-da）
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- 在 **test** 划分上进行评估
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- 应用语言特定的归一化处理
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- COMET 指标需要安装 `unbabel-comet` 包
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- 子集代表单个语言对（例如 `en-zh_cn`、`en-de_de`）
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `wmt24pp` |
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| **数据集ID** | [extraordinarylab/wmt24pp](https://modelscope.cn/datasets/extraordinarylab/wmt24pp/summary) |
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| **论文** | N/A |
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| **标签** | `MachineTranslation`, `MultiLingual` |
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| **指标** | `bleu`, `bert_score`, `comet` |
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| **默认示例数** | 0-shot |
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| **评估划分** | `test` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 52,800 |
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| 提示词长度（平均） | 265.45 字符 |
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| 提示词长度（最小/最大） | 71 / 1047 字符 |
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**各子集统计数据：**
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| 子集 | 样本数 | 提示平均长度 | 提示最小长度 | 提示最大长度 |
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|--------|---------|-------------|------------|------------|
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| `en-ar_eg` | 960 | 263.26 | 75 | 1039 |
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| `en-ar_sa` | 960 | 263.26 | 75 | 1039 |
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| `en-bg_bg` | 960 | 269.26 | 81 | 1045 |
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| `en-bn_in` | 960 | 265.26 | 77 | 1041 |
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| `en-ca_es` | 960 | 265.26 | 77 | 1041 |
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| `en-cs_cz` | 960 | 261.26 | 73 | 1037 |
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| `en-da_dk` | 960 | 263.26 | 75 | 1039 |
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| `en-de_de` | 960 | 263.26 | 75 | 1039 |
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| `en-el_gr` | 960 | 261.26 | 73 | 1037 |
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| `en-es_mx` | 960 | 265.26 | 77 | 1041 |
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| `en-et_ee` | 960 | 267.26 | 79 | 1043 |
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| `en-fa_ir` | 960 | 261.26 | 73 | 1037 |
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| `en-fi_fi` | 960 | 265.26 | 77 | 1041 |
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| `en-fil_ph` | 960 | 267.26 | 79 | 1043 |
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| `en-fr_ca` | 960 | 263.26 | 75 | 1039 |
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| `en-fr_fr` | 960 | 263.26 | 75 | 1039 |
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| `en-gu_in` | 960 | 267.26 | 79 | 1043 |
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| `en-he_il` | 960 | 263.26 | 75 | 1039 |
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| `en-hi_in` | 960 | 261.26 | 73 | 1037 |
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| `en-hr_hr` | 960 | 267.26 | 79 | 1043 |
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| `en-hu_hu` | 960 | 269.26 | 81 | 1045 |
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| `en-id_id` | 960 | 271.26 | 83 | 1047 |
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| `en-is_is` | 960 | 269.26 | 81 | 1045 |
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| `en-it_it` | 960 | 265.26 | 77 | 1041 |
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| `en-ja_jp` | 960 | 267.26 | 79 | 1043 |
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| `en-kn_in` | 960 | 265.26 | 77 | 1041 |
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| `en-ko_kr` | 960 | 263.26 | 75 | 1039 |
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| `en-lt_lt` | 960 | 271.26 | 83 | 1047 |
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| `en-lv_lv` | 960 | 265.26 | 77 | 1041 |
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| `en-ml_in` | 960 | 269.26 | 81 | 1045 |
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| `en-mr_in` | 960 | 265.26 | 77 | 1041 |
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| `en-nl_nl` | 960 | 261.26 | 73 | 1037 |
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| `en-no_no` | 960 | 269.26 | 81 | 1045 |
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| `en-pa_in` | 960 | 265.26 | 77 | 1041 |
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| `en-pl_pl` | 960 | 263.26 | 75 | 1039 |
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| `en-pt_br` | 960 | 271.26 | 83 | 1047 |
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| `en-pt_pt` | 960 | 271.26 | 83 | 1047 |
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| `en-ro_ro` | 960 | 267.26 | 79 | 1043 |
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| `en-ru_ru` | 960 | 265.26 | 77 | 1041 |
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| `en-sk_sk` | 960 | 263.26 | 75 | 1039 |
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| `en-sl_si` | 960 | 269.26 | 81 | 1045 |
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| `en-sr_rs` | 960 | 265.26 | 77 | 1041 |
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| `en-sv_se` | 960 | 265.26 | 77 | 1041 |
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| `en-sw_ke` | 960 | 265.26 | 77 | 1041 |
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| `en-sw_tz` | 960 | 265.26 | 77 | 1041 |
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| `en-ta_in` | 960 | 261.26 | 73 | 1037 |
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| `en-te_in` | 960 | 263.26 | 75 | 1039 |
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| `en-th_th` | 960 | 259.26 | 71 | 1035 |
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| `en-tr_tr` | 960 | 265.26 | 77 | 1041 |
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| `en-uk_ua` | 960 | 269.26 | 81 | 1045 |
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| `en-ur_pk` | 960 | 259.26 | 71 | 1035 |
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| `en-vi_vn` | 960 | 271.26 | 83 | 1047 |
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| `en-zh_cn` | 960 | 267.26 | 79 | 1043 |
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| `en-zh_tw` | 960 | 267.26 | 79 | 1043 |
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| `en-zu_za` | 960 | 259.26 | 71 | 1035 |
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## 样例示例
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**子集**: `en-ar_eg`
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```json
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{
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"input": [
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{
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"id": "557f3aa1",
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"content": [
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{
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"text": "Translate the following english sentence into arabic:\n\nenglish: Siso's depictions of land, water center new gallery exhibition\narabic:"
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}
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]
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}
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],
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"target": "رسومات سيسو عن الأرض والمية في معرضه الجديد",
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"id": 0,
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"group_id": 0,
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"subset_key": "en-ar_eg",
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"metadata": {
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"source_text": "Siso's depictions of land, water center new gallery exhibition",
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"target_text": "رسومات سيسو عن الأرض والمية في معرضه الجديد",
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"source_language": "en",
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"target_language": "ar_eg"
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}
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}
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```
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## 提示模板
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**提示模板：**
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```text
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Translate the following {source_language} sentence into {target_language}:
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{source_language}: {source_text}
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{target_language}:
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```
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## 使用方法
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### 使用 CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets wmt24pp \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['wmt24pp'],
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dataset_args={
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'wmt24pp': {
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# subset_list: ['en-ar_eg', 'en-ar_sa', 'en-bg_bg'] # 可选，用于评估特定子集
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}
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},
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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``` |