Remove Python builder and requirements; keep only Parquet data and README.

requirements.txt

@@ -1,2 +0,0 @@
-pandas
-pyarrow

sp500_earnings_transcripts.py

@@ -1,87 +0,0 @@
-"""
-DatasetBuilder for S&P 500 Earnings Call Transcripts (2005-2025) dataset.
-"""
-import os
-from pathlib import Path
-
-import datasets
-import pyarrow.parquet as pq
-
-_CITATION = """
-@dataset{kurry2025sp500earnings,
-  author    = {Kurry},
-  title     = {S&P 500 Earnings Transcripts Dataset},
-  year      = {2025},
-  publisher = {Hugging Face},
-  url       = {https://huggingface.co/datasets/kurry/sp500_earnings_transcripts}
-}
-"""
-
-_DESCRIPTION = """
-Full earnings call transcripts for S&P 500 companies and US large-cap companies
-from 2005 to 2025, including metadata and structured speaker-by-speaker dialogue.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/kurry/sp500_earnings_transcripts"
-_LICENSE = "mit"
-
-
-class Sp500EarningsTranscripts(datasets.GeneratorBasedBuilder):
-    """S&P 500 Earnings Call Transcripts Dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="default", version=VERSION,
-                              description="S&P 500 earnings call transcripts dataset")
-    ]
-    DEFAULT_CONFIG_NAME = "default"
-
-    def _info(self):
-        features = datasets.Features({
-            "symbol": datasets.Value("string"),
-            "company_name": datasets.Value("string"),
-            "company_id": datasets.Value("float64"),
-            "year": datasets.Value("int32"),
-            "quarter": datasets.Value("int32"),
-            "date": datasets.Value("string"),
-            "content": datasets.Value("string"),
-            "structured_content": datasets.Sequence(
-                datasets.Features({
-                    "speaker": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                })
-            ),
-        })
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # data is provided locally in parquet_files directory
-        data_dir = Path(__file__).resolve().parent / "parquet_files"
-        # collect all parquet files
-        filepaths = sorted(data_dir.glob("*.parquet"))
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": filepaths},
-            )
-        ]
-
-    def _generate_examples(self, filepaths):
-        """
-        Generate examples from parquet files.
-        Args:
-            filepaths: list of pathlib.Path to parquet files
-        """
-        idx = 0
-        for path in filepaths:
-            # read parquet file with nested structured_content
-            table = pq.read_table(path)
-            for record in table.to_pylist():
-                yield idx, record
-                idx += 1