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from pathlib import Path |
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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 datasets.download.download_manager import DownloadManager |
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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 Licenses, Tasks |
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_CITATION = "" |
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_LOCAL = False |
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_LANGUAGES = ["tha"] |
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_DATASETNAME = "thai_databricks_dolly" |
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_DESCRIPTION = """\ |
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This is a Thai-instructed dataset translated from databricks-dolly-15k using |
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Google Cloud Translation. databricks-dolly-15k is an open-source dataset of |
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instruction-following records generated by thousands of Databricks employees in |
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several behavioral categories outlined in the InstructGPT paper, including |
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brainstorming, classification, closed QA, generation, information extraction, |
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open QA, and summarization. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/Thaweewat/databricks-dolly-15k-th" |
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_LICENSE = Licenses.CC_BY_SA_3_0.value |
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_URL = "https://huggingface.co/datasets/Thaweewat/databricks-dolly-15k-th/resolve/main/databricks-dolly-15k-th.parquet" |
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_SUPPORTED_TASKS = [Tasks.INSTRUCTION_TUNING] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class ThaiDatabricksDollyDataset(datasets.GeneratorBasedBuilder): |
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"""Thai Databricks Dolly Dataset""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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SEACROWD_SCHEMA_NAME = "t2t" |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=_DATASETNAME, |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", |
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subset_id=_DATASETNAME, |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"instruction": datasets.Value("string"), |
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"context": datasets.Value("string"), |
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"response": datasets.Value("string"), |
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"category": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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features = schemas.text2text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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data_file = Path(dl_manager.download_and_extract(_URL)) |
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file})] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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"""Yield examples as (key, example) tuples""" |
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df = pd.read_parquet(filepath, engine="pyarrow") |
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for idx, row in df.iterrows(): |
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instruction = row.get("instruction").strip() |
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context = row.get("context").strip() |
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response = row.get("response").strip() |
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category = row.get("category").strip() |
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if self.config.schema == "source": |
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example = { |
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"instruction": instruction, |
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"context": context, |
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"response": response, |
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"category": category, |
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} |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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text_1 = f"Context: {context}\n\n{instruction}" if context else instruction |
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text_2 = response |
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example = { |
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"id": str(idx), |
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"text_1": text_1, |
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"text_2": text_2, |
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"text_1_name": "context_and_instruction", |
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"text_2_name": "response", |
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} |
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yield idx, example |
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