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thai_databricks_dolly.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+# No paper citation found.
+_CITATION = ""
+
+_LOCAL = False
+_LANGUAGES = ["tha"]
+_DATASETNAME = "thai_databricks_dolly"
+_DESCRIPTION = """\
+This is a Thai-instructed dataset translated from databricks-dolly-15k using
+Google Cloud Translation. databricks-dolly-15k is an open-source dataset of
+instruction-following records generated by thousands of Databricks employees in
+several behavioral categories outlined in the InstructGPT paper, including
+brainstorming, classification, closed QA, generation, information extraction,
+open QA, and summarization.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Thaweewat/databricks-dolly-15k-th"
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+_URL = "https://huggingface.co/datasets/Thaweewat/databricks-dolly-15k-th/resolve/main/databricks-dolly-15k-th.parquet"
+_SUPPORTED_TASKS = [Tasks.INSTRUCTION_TUNING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ThaiDatabricksDollyDataset(datasets.GeneratorBasedBuilder):
+    """Thai Databricks Dolly Dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "instruction": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "response": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text2text_features
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        data_file = Path(dl_manager.download_and_extract(_URL))
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file})]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yield examples as (key, example) tuples"""
+        # pyarrow is an implicit dependency to load the parquet files
+        df = pd.read_parquet(filepath, engine="pyarrow")
+        for idx, row in df.iterrows():
+            instruction = row.get("instruction").strip()
+            context = row.get("context").strip()
+            response = row.get("response").strip()
+            category = row.get("category").strip()
+            if self.config.schema == "source":
+                example = {
+                    "instruction": instruction,
+                    "context": context,
+                    "response": response,
+                    "category": category,
+                }
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                text_1 = f"Context: {context}\n\n{instruction}" if context else instruction
+                text_2 = response
+                example = {
+                    "id": str(idx),
+                    "text_1": text_1,
+                    "text_2": text_2,
+                    "text_1_name": "context_and_instruction",
+                    "text_2_name": "response",
+                }
+
+            yield idx, example