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66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
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from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets.PMMEval import PMMEvalFloresDataset, PMMEvalFloresEvaluator, pmmeval_flores_postprocess
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NATURAL_LANGUAGE_FULLNAMES_FLORES = ['Chinese', 'Arabic', 'Spanish', 'French', 'Japanese', 'Korean', 'Portuguese', 'Thai', 'Vietnamese']
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PROMPT = {
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"Chinese": "将这个句子从英语翻译成中文。\n\n{src}",
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"Arabic": "ترجم هذه الجملة من الإنجليزية إلى العربية.\n\n{src}",
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"Spanish": "Traduce esta oración del inglés al español.\n\n{src}",
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"Japanese": "この文を英語から日本語に翻訳してください。\n\n{src}",
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"Korean": "이 문장을 영어에서 한국어로 번역하세요.\n\n{src}",
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"Thai": "แปลประโยคนี้จากภาษาอังกฤษเป็นภาษาไทย.\n\n{src}",
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"French": "Traduisez cette phrase de l'anglais en français.\n\n{src}",
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"Portuguese": "Traduza esta frase do inglês para o português.\n\n{src}",
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"Vietnamese": "Dịch câu này từ tiếng Anh sang tiếng Việt.\n\n{src}"
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}
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PMMEval_flores_datasets = list()
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# Add flores_200
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PMMEval_flores_reader_cfg = dict(
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input_columns=['src'],
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output_column='tgt',
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test_split='test'
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)
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PMMEval_flores_datasets = list()
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for lang_fullname in NATURAL_LANGUAGE_FULLNAMES_FLORES:
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PMMEval_flores_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[
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dict(
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role='HUMAN',
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prompt=PROMPT[lang_fullname]
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)
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]
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)
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer),
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)
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PMMEval_flores_eval_cfg = dict(
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evaluator=dict(type=PMMEvalFloresEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=pmmeval_flores_postprocess, lang_fullname=lang_fullname)
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)
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PMMEval_flores_datasets.append(
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dict(
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abbr=f'flores-{lang_fullname}',
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type=PMMEvalFloresDataset,
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path='P-MMEval',
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lang_fullname=lang_fullname,
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reader_cfg=PMMEval_flores_reader_cfg,
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infer_cfg=PMMEval_flores_infer_cfg,
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eval_cfg=PMMEval_flores_eval_cfg)
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)
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