Upload nusax_mt.py with huggingface_hub
Browse files- nusax_mt.py +16 -16
nusax_mt.py
CHANGED
@@ -4,14 +4,14 @@ from typing import Dict, List, Tuple
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import datasets
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import pandas as pd
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from
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from
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from
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DEFAULT_SOURCE_VIEW_NAME, Tasks)
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_DATASETNAME = "nusax_mt"
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_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
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_UNIFIED_VIEW_NAME =
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_LANGUAGES = ["ind", "ace", "ban", "bjn", "bbc", "bug", "jav", "mad", "min", "nij", "sun", "eng"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
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_LOCAL = False
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@@ -45,7 +45,7 @@ _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
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_SOURCE_VERSION = "1.0.0"
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-
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_URLS = {
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"train": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/train.csv",
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@@ -54,13 +54,13 @@ _URLS = {
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}
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def
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"""Construct
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if schema != "source" and schema != "
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raise ValueError(f"Invalid schema: {schema}")
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if lang_source == "" and lang_target == "":
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return
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name="nusax_mt_{schema}".format(schema=schema),
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version=datasets.Version(version),
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description="nusax_mt with {schema} schema for all 132 language pairs".format(schema=schema),
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@@ -68,7 +68,7 @@ def nusantara_config_constructor(lang_source, lang_target, schema, version):
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subset_id="nusax_mt",
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)
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else:
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return
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name="nusax_mt_{lang_source}_{lang_target}_{schema}".format(lang_source=lang_source, lang_target=lang_target, schema=schema),
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version=datasets.Version(version),
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description="nusax_mt with {schema} schema for {lang_source} source language and {lang_target} target language".format(lang_source=lang_source, lang_target=lang_target, schema=schema),
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@@ -97,15 +97,15 @@ class NusaXMT(datasets.GeneratorBasedBuilder):
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"""NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language."""
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BUILDER_CONFIGS = (
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[
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+ [
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+ [
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)
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DEFAULT_CONFIG_NAME = "nusax_senti_ind_eng_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source" or self.config.schema == "
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features = schemas.text2text_features
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else:
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raise ValueError(f"Invalid config schema: {self.config.schema}")
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@@ -140,11 +140,11 @@ class NusaXMT(datasets.GeneratorBasedBuilder):
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]
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
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if self.config.schema != "source" and self.config.schema != "
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raise ValueError(f"Invalid config schema: {self.config.schema}")
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df = pd.read_csv(filepath).reset_index()
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if self.config.name == "nusax_mt_source" or self.config.name == "
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# load all 132 language pairs
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id_count = -1
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for lang_source in LANGUAGES_MAP:
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import datasets
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import pandas as pd
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
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DEFAULT_SOURCE_VIEW_NAME, Tasks)
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_DATASETNAME = "nusax_mt"
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_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
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_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
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_LANGUAGES = ["ind", "ace", "ban", "bjn", "bbc", "bug", "jav", "mad", "min", "nij", "sun", "eng"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
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_LOCAL = False
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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_URLS = {
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"train": "https://raw.githubusercontent.com/IndoNLP/nusax/main/datasets/mt/train.csv",
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}
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def seacrowd_config_constructor(lang_source, lang_target, schema, version):
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"""Construct SEACrowdConfig with nusax_mt_{lang_source}_{lang_target}_{schema} as the name format"""
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if schema != "source" and schema != "seacrowd_t2t":
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raise ValueError(f"Invalid schema: {schema}")
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if lang_source == "" and lang_target == "":
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return SEACrowdConfig(
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name="nusax_mt_{schema}".format(schema=schema),
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version=datasets.Version(version),
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description="nusax_mt with {schema} schema for all 132 language pairs".format(schema=schema),
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subset_id="nusax_mt",
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)
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else:
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return SEACrowdConfig(
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name="nusax_mt_{lang_source}_{lang_target}_{schema}".format(lang_source=lang_source, lang_target=lang_target, schema=schema),
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version=datasets.Version(version),
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description="nusax_mt with {schema} schema for {lang_source} source language and {lang_target} target language".format(lang_source=lang_source, lang_target=lang_target, schema=schema),
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"""NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language."""
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BUILDER_CONFIGS = (
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[seacrowd_config_constructor(lang1, lang2, "source", _SOURCE_VERSION) for lang1 in LANGUAGES_MAP for lang2 in LANGUAGES_MAP if lang1 != lang2]
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+ [seacrowd_config_constructor(lang1, lang2, "seacrowd_t2t", _SEACROWD_VERSION) for lang1 in LANGUAGES_MAP for lang2 in LANGUAGES_MAP if lang1 != lang2]
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+ [seacrowd_config_constructor("", "", "source", _SOURCE_VERSION), seacrowd_config_constructor("", "", "seacrowd_t2t", _SEACROWD_VERSION)]
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)
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DEFAULT_CONFIG_NAME = "nusax_senti_ind_eng_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source" or self.config.schema == "seacrowd_t2t":
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features = schemas.text2text_features
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else:
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raise ValueError(f"Invalid config schema: {self.config.schema}")
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]
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
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if self.config.schema != "source" and self.config.schema != "seacrowd_t2t":
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raise ValueError(f"Invalid config schema: {self.config.schema}")
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df = pd.read_csv(filepath).reset_index()
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if self.config.name == "nusax_mt_source" or self.config.name == "nusax_mt_seacrowd_t2t":
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# load all 132 language pairs
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id_count = -1
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for lang_source in LANGUAGES_MAP:
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