added custom script for loading data

README.md

@@ -88,35 +88,65 @@ Each file is a JSON mapping of **token -> integer index** (0–14,999).
 | `data_with_embeddings/vocabs/wiki_vocab.json`  | Vocabulary for the Wiki corpus. Keys are tokens, values are integer indices. |
 
 ## Usage Example
+The dataset contains four Parquet files:
+
+- `bills_train`  
+- `bills_test`  
+- `wiki_train`  
+- `wiki_test`
+
+Because the Bills and Wiki splits use different schemas, you should load each split
+directly from its Parquet file using the generic `parquet` loader from 🤗 Datasets:
 
 ```python
 from datasets import load_dataset
 
-# Bills
+# ------------------------------
+# Bills Dataset
+# ------------------------------
 bills_train = load_dataset(
-    "lcalvobartolome/proxann_data",
-    name="bills",
+    "parquet",
+    data_files={
+        "train": "hf://datasets/lcalvobartolome/proxann_data@main/"
+                 "bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet"
+    },
     split="train",
-    trust_remote_code=True,
 )
-print(len(bills_train))  # 32661
+print("Bills train size:", len(bills_train))   # 32661
 
 bills_test = load_dataset(
-    "lcalvobartolome/proxann_data",
-    name="bills",
+    "parquet",
+    data_files={
+        "test": "hf://datasets/lcalvobartolome/proxann_data@main/"
+                "bills_test.metadata.parquet"
+    },
     split="test",
-    trust_remote_code=True,
 )
-print(bills_test.column_names) # 15242
+print("Bills test size:", len(bills_test))     # 15242
+
 
-# Wiki
+# ------------------------------
+# Wiki Dataset
+# ------------------------------
 wiki_train = load_dataset(
-    "lcalvobartolome/proxann_data",
-    name="wiki",
+    "parquet",
+    data_files={
+        "train": "hf://datasets/lcalvobartolome/proxann_data@main/"
+                 "wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet"
+    },
     split="train",
-    trust_remote_code=True,
 )
-print(len(wiki_train))  # 14290
+print("Wiki train size:", len(wiki_train))     # 14290
+
+wiki_test = load_dataset(
+    "parquet",
+    data_files={
+        "test": "hf://datasets/lcalvobartolome/proxann_data@main/"
+                "wiki_test.metadata.parquet"
+    },
+    split="test",
+)
+print("Wiki test size:", len(wiki_test))
 ```
 
 ## Related Resources

proxann_data.py

@@ -1,170 +0,0 @@
-# proxann_data.py
-#
-# Dataset script for `lcalvobartolome/proxann_data`.
-# Provides two configs:
-#   - "bills": US Congressional bills
-#   - "wiki":  Wikipedia articles
-#
-# Usage:
-#   from datasets import load_dataset
-#
-#   bills_train = load_dataset(
-#       "lcalvobartolome/proxann_data",
-#       name="bills",
-#       split="train",
-#       trust_remote_code=True,
-#   )
-#
-#   wiki_train = load_dataset(
-#       "lcalvobartolome/proxann_data",
-#       name="wiki",
-#       split="train",
-#       trust_remote_code=True,
-#   )
-
-import os
-from typing import Dict, Any, Iterator, Tuple
-
-import datasets
-import pyarrow.parquet as pq
-
-
-_CITATION = """\
-@inproceedings{soldaini2016proxann,
-  title = {ProxANN: ...},
-  author = {...},
-  year = {2016},
-}
-"""
-
-_DESCRIPTION = """\
-ProxANN dataset with two components:
-
-- bills: US Congressional bills with summaries and topics
-- wiki:  Wikipedia articles with category hierarchy
-
-Stored as Parquet files with pre-computed MiniLM embeddings.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/lcalvobartolome/proxann_data"
-_LICENSE = "mit"
-
-_BASE_URL = (
-    "https://huggingface.co/datasets/"
-    "lcalvobartolome/proxann_data/resolve/main"
-)
-
-_BILLS_FILES = {
-    "train": f"{_BASE_URL}/bills_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet",
-    "test": f"{_BASE_URL}/bills_test.metadata.parquet",
-}
-
-_WIKI_FILES = {
-    "train": f"{_BASE_URL}/wiki_train.metadata.embeddings.jsonl.all-MiniLM-L6-v2.parquet",
-    "test": f"{_BASE_URL}/wiki_test.metadata.parquet",
-}
-
-
-class ProxannConfig(datasets.BuilderConfig):
-    """BuilderConfig for ProxANN (bills / wiki)."""
-
-    def __init__(self, *, features: datasets.Features, **kwargs):
-        super().__init__(**kwargs)
-        self.features = features
-
-
-class ProxannData(datasets.GeneratorBasedBuilder):
-    """ProxANN dataset with two configs: bills and wiki."""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIG_CLASS = ProxannConfig
-    DEFAULT_CONFIG_NAME = "bills"
-
-    BUILDER_CONFIGS = [
-        ProxannConfig(
-            name="bills",
-            version=VERSION,
-            description="US Congressional bills with summaries and topics.",
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "summary": datasets.Value("string"),
-                    "topic": datasets.Value("string"),
-                    "subtopic": datasets.Value("string"),
-                    "subjects_top_term": datasets.Value("string"),
-                    "date": datasets.Value("timestamp[ns]"),
-                    "tokenized_text": datasets.Value("string"),
-                    "embeddings": datasets.Value("string"),
-                }
-            ),
-        ),
-        ProxannConfig(
-            name="wiki",
-            version=VERSION,
-            description="Wikipedia articles with category hierarchy.",
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "supercategory": datasets.Value("string"),
-                    "category": datasets.Value("string"),
-                    "subcategory": datasets.Value("string"),
-                    "page_name": datasets.Value("string"),
-                    "tokenized_text": datasets.Value("string"),
-                    "embeddings": datasets.Value("string"),
-                }
-            ),
-        ),
-    ]
-
-    def _info(self) -> datasets.DatasetInfo:
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=self.config.features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(
-        self, dl_manager: datasets.DownloadManager
-    ) -> list[datasets.SplitGenerator]:
-        """Download the appropriate files and define splits."""
-
-        if self.config.name == "bills":
-            data_files = dl_manager.download_and_extract(_BILLS_FILES)
-        elif self.config.name == "wiki":
-            data_files = dl_manager.download_and_extract(_WIKI_FILES)
-        else:
-            raise ValueError(f"Unknown config name: {self.config.name}")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": data_files["train"]},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": data_files["test"]},
-            ),
-        ]
-
-    def _generate_examples(
-        self, filepath: str
-    ) -> Iterator[Tuple[int, Dict[str, Any]]]:
-        """Yield (key, example) pairs from a Parquet file."""
-        
-        table = pq.read_table(filepath)
-
-        feature_names = list(self.config.features.keys())
-        table = table.select(feature_names)
-
-        for batch in table.to_batches():
-            batch_table = batch.to_pydict()
-            num_rows = len(next(iter(batch_table.values())))
-            for i in range(num_rows):
-                row = {
-                    col: batch_table[col][i]
-                    for col in feature_names
-                }
-                yield i, row