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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: record |
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dtype: string |
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- name: clean_para_index_set_pair |
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dtype: string |
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- name: src |
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dtype: string |
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- name: dst |
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dtype: string |
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- name: src_text |
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dtype: string |
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- name: dst_text |
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dtype: string |
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- name: src_rate |
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dtype: float64 |
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- name: dst_rate |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 8884444751 |
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num_examples: 15331650 |
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download_size: 2443622169 |
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dataset_size: 8884444751 |
|
--- |
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# 联合国数字图书馆的段落级中-英对齐平行语料 |
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用我口胡的方法弄出来的平行语料，统计数据和拿argostranslate直接又跑了一份bleu score的结果已经丢论文里了，论文在写了在写了。应该拿这份去练机翻模型没问题，数据源是人写的。 |
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bleu score 这里贴一份吧，懒得转格式了，我不太懂看，可能很差（ |
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Language & Paragraph Count & Avg Tokens & bleu1 & bleu2 & bleu3 & bleu4 \\ |
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\midrule |
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ar & 59754 & 52.71873 & 0.73799 & 0.58027 & 0.48118 & 0.40782 \\ |
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de & 187 & 69.58824 & 0.62058 & 0.38837 & 0.26155 & 0.18271 \\ |
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es & 66537 & 50.70776 & 0.74566 & 0.58545 & 0.48445 & 0.41073 \\ |
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fr & 68765 & 52.13133 & 0.67895 & 0.49830 & 0.39453 & 0.32332 \\ |
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ru & 65039 & 51.75020 & 0.71578 & 0.54827 & 0.44681 & 0.37495 \\ |
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zh & 56276 & 53.16430 & 0.64737 & 0.45399 & 0.34408 & 0.27072 \\ |
