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Merge fa4728d0d5 into d572761cef
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CHEN PENGAN
GitHub
commit
352d1a4457
122
configs/datasets/wmt19/wmt19_gen.py
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122
configs/datasets/wmt19/wmt19_gen.py
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from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever, BM25Retriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.openicl.icl_evaluator import BleuEvaluator
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from opencompass.datasets.wmt19 import WMT19TranslationDataset
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LANG_CODE_TO_NAME = {
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'cs': 'Czech',
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'de': 'German',
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'en': 'English',
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'fi': 'Finnish',
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'fr': 'French',
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'gu': 'Gujarati',
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'kk': 'Kazakh',
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'lt': 'Lithuanian',
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'ru': 'Russian',
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'zh': 'Chinese'
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}
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wmt19_reader_cfg = dict(
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input_columns=['input'],
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output_column='target',
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train_split='validation',
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test_split='validation')
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wmt19_infer_cfg_0shot = 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(role='HUMAN', prompt='Translate the following {src_lang_name} text to {tgt_lang_name}:\n{{input}}\n'),
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dict(role='BOT', prompt='Translation:\n')
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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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wmt19_infer_cfg_5shot = dict(
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ice_template=dict(
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type=PromptTemplate,
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template='Example:\n{src_lang_name}: {{input}}\n{tgt_lang_name}: {{target}}'
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),
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prompt_template=dict(
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type=PromptTemplate,
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template='</E>\nTranslate the following {src_lang_name} text to {tgt_lang_name}:\n{{input}}\nTranslation:\n',
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ice_token='</E>',
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),
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retriever=dict(type=BM25Retriever, ice_num=5),
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inferencer=dict(type=GenInferencer),
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)
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wmt19_eval_cfg = dict(
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evaluator=dict(
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type=BleuEvaluator
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),
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pred_role='BOT',
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)
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language_pairs = [
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('cs', 'en'), ('de', 'en'), ('fi', 'en'), ('fr', 'de'),
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('gu', 'en'), ('kk', 'en'), ('lt', 'en'), ('ru', 'en'), ('zh', 'en')
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]
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wmt19_datasets = []
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for src_lang, tgt_lang in language_pairs:
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src_lang_name = LANG_CODE_TO_NAME[src_lang]
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tgt_lang_name = LANG_CODE_TO_NAME[tgt_lang]
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wmt19_datasets.extend([
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dict(
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abbr=f'wmt19_{src_lang}-{tgt_lang}_0shot',
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type=WMT19TranslationDataset,
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path='/path/to/wmt19',
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src_lang=src_lang,
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tgt_lang=tgt_lang,
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reader_cfg=wmt19_reader_cfg,
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infer_cfg={
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**wmt19_infer_cfg_0shot,
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'prompt_template': {
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**wmt19_infer_cfg_0shot['prompt_template'],
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'template': {
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**wmt19_infer_cfg_0shot['prompt_template']['template'],
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'round': [
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{
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**wmt19_infer_cfg_0shot['prompt_template']['template']['round'][0],
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'prompt': wmt19_infer_cfg_0shot['prompt_template']['template']['round'][0]['prompt'].format(
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src_lang_name=src_lang_name, tgt_lang_name=tgt_lang_name
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)
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},
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wmt19_infer_cfg_0shot['prompt_template']['template']['round'][1]
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]
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}
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}
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},
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eval_cfg=wmt19_eval_cfg),
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dict(
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abbr=f'wmt19_{src_lang}-{tgt_lang}_5shot',
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type=WMT19TranslationDataset,
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path='/path/to/wmt19',
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src_lang=src_lang,
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tgt_lang=tgt_lang,
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reader_cfg=wmt19_reader_cfg,
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infer_cfg={
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**wmt19_infer_cfg_5shot,
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'ice_template': {
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**wmt19_infer_cfg_5shot['ice_template'],
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'template': wmt19_infer_cfg_5shot['ice_template']['template'].format(
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src_lang_name=src_lang_name, tgt_lang_name=tgt_lang_name
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)
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},
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'prompt_template': {
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**wmt19_infer_cfg_5shot['prompt_template'],
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'template': wmt19_infer_cfg_5shot['prompt_template']['template'].format(
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src_lang_name=src_lang_name, tgt_lang_name=tgt_lang_name
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)
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}
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},
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eval_cfg=wmt19_eval_cfg),
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])
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@ -164,6 +164,7 @@ from .wic import * # noqa: F401, F403
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from .wikibench import * # noqa: F401, F403
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from .winograd import * # noqa: F401, F403
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from .winogrande import * # noqa: F401, F403
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from .wmt19 import * # noqa: F401, F403
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from .wnli import wnliDataset # noqa: F401, F403
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from .wsc import * # noqa: F401, F403
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from .xcopa import * # noqa: F401, F403
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38
opencompass/datasets/wmt19.py
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38
opencompass/datasets/wmt19.py
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import os
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import pandas as pd
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from datasets import Dataset, DatasetDict
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from opencompass.registry import LOAD_DATASET
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from opencompass.datasets.base import BaseDataset
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@LOAD_DATASET.register_module()
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class WMT19TranslationDataset(BaseDataset):
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@staticmethod
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def load(path: str, src_lang: str, tgt_lang: str):
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print(f"Attempting to load data from path: {path}")
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print(f"Source language: {src_lang}, Target language: {tgt_lang}")
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lang_pair_dir = os.path.join(path, f"{src_lang}-{tgt_lang}")
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if not os.path.exists(lang_pair_dir):
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lang_pair_dir = os.path.join(path, f"{tgt_lang}-{src_lang}")
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if not os.path.exists(lang_pair_dir):
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raise ValueError(f"Cannot find directory for language pair {src_lang}-{tgt_lang} or {tgt_lang}-{src_lang}")
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print(f"Loading data from directory: {lang_pair_dir}")
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val_file = os.path.join(lang_pair_dir, "validation-00000-of-00001.parquet")
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val_df = pd.read_parquet(val_file)
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def process_split(df):
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return Dataset.from_dict({
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'input': df['translation'].apply(lambda x: x[src_lang]).tolist(),
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'target': df['translation'].apply(lambda x: x[tgt_lang]).tolist()
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})
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return DatasetDict({
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'validation': process_split(val_df)
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})
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@classmethod
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def get_dataset(cls, path, src_lang, tgt_lang):
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return cls.load(path, src_lang, tgt_lang)
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