Use compressed CSV files and Git LFS for large files

.gitattributes

@@ -57,3 +57,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.csv.gz filter=lfs diff=lfs merge=lfs -text
+data/flat/train/flat-training.csv.gz filter=lfs diff=lfs merge=lfs -text
+data/sequential/train/sequential-training.csv.gz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -35,15 +35,15 @@ Train a generative model that generalizes well, using any open-source tools (Syn
 
 ## Dataset Description
 
-This dataset consists of two CSV files used in the MOSTLY AI Prize competition:
+This dataset consists of two compressed CSV files used in the MOSTLY AI Prize competition:
 
 ### Flat Data
-- File: `flat-training.csv.gz` (7.4MB)
+- File: `data/flat/train/flat-training.csv.gz` (7.4MB)
 - 100,000 records
 - 80 data columns: 60 numeric, 20 categorical
 
 ### Sequential Data
-- File: `sequential-training.csv.gz` (1.3MB)
+- File: `data/sequential/train/sequential-training.csv.gz` (1.3MB)
 - 20,000 groups
 - Each group contains 5-10 records
 - 11 data columns: 7 numeric, 3 categorical + 1 group ID
@@ -56,12 +56,68 @@ The files are compressed using gzip. You can load them directly using pandas:
 import pandas as pd
 
 # Load flat data
-flat_df = pd.read_csv('data/flat-training.csv.gz', compression='gzip')
+flat_df = pd.read_csv('data/flat/train/flat-training.csv.gz', compression='gzip')
 
 # Load sequential data
-sequential_df = pd.read_csv('data/sequential-training.csv.gz', compression='gzip')
+sequential_df = pd.read_csv('data/sequential/train/sequential-training.csv.gz', compression='gzip')
 ```
 
+### Dataset Visualizations
+
+#### Flat Dataset Visualizations
+
+Here's a preview of some data distributions in the flat dataset:
+
+<div class="flex flex-col space-y-4">
+  <div class="flex flex-row space-x-4">
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/flat_dog_hist.png" alt="Distribution of 'dog' values" />
+      <p class="text-center">Distribution of 'dog' values</p>
+    </div>
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/flat_deer_hist.png" alt="Distribution of 'deer' values" />
+      <p class="text-center">Distribution of 'deer' values</p>
+    </div>
+  </div>
+  <div class="flex flex-row space-x-4">
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/flat_cat_bar.png" alt="Count of 'cat' categories" />
+      <p class="text-center">Count of 'cat' categories</p>
+    </div>
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/flat_correlation.png" alt="Correlation heatmap" />
+      <p class="text-center">Correlation heatmap</p>
+    </div>
+  </div>
+</div>
+
+#### Sequential Dataset Visualizations
+
+Here's a preview of some data distributions in the sequential dataset:
+
+<div class="flex flex-col space-y-4">
+  <div class="flex flex-row space-x-4">
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/seq_mike_hist.png" alt="Distribution of 'mike' values" />
+      <p class="text-center">Distribution of 'mike' values</p>
+    </div>
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/seq_david_hist.png" alt="Distribution of 'david' values" />
+      <p class="text-center">Distribution of 'david' values</p>
+    </div>
+  </div>
+  <div class="flex flex-row space-x-4">
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/seq_alice_bar.png" alt="Count of 'alice' categories" />
+      <p class="text-center">Count of 'alice' categories</p>
+    </div>
+    <div class="w-1/2">
+      <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/seq_correlation.png" alt="Correlation heatmap" />
+      <p class="text-center">Correlation heatmap</p>
+    </div>
+  </div>
+</div>
+
 ### Column Description
 
 Note: Detailed column descriptions are intentionally not provided as part of the competition challenge. The task is to generate synthetic data that preserves the statistical properties of the original data without needing to understand the semantic meaning of each column.
@@ -84,14 +140,12 @@ Your synthetic data generation approach should generalize well to this unseen da
 
 ## Usage with Hugging Face Datasets
 
-The dataset can be loaded using the Hugging Face Datasets library:
+The dataset can be loaded using the Hugging Face Datasets library directly from the compressed CSV files:
 
 ```python
 from datasets import load_dataset
 
-# Load the flat dataset (default)
-flat_dataset = load_dataset("mostlyaiprize")
-# or explicitly specify the flat config
+# Load the flat dataset
 flat_dataset = load_dataset("mostlyaiprize", "flat")
 
 # Load the sequential dataset
@@ -100,6 +154,8 @@ sequential_dataset = load_dataset("mostlyaiprize", "sequential")
 # Access the data
 flat_data = flat_dataset["train"]
 sequential_data = sequential_dataset["train"]
+
+# Note: Hugging Face Datasets will automatically handle the gzip compression
 ```
 
 ## Dataset Schema
@@ -109,103 +165,103 @@ The schema for each dataset is as follows:
 ### Flat Dataset Schema (80 columns)
 ```python
 {
-    "dog": Value("int64"),
-    "cat": Value("string"),
-    "rabbit": Value("string"),
-    "deer": Value("float32"),
-    "panda": Value("int64"),
-    "koala": Value("string"),
-    "otter": Value("string"),
-    "hedgehog": Value("float32"),
-    "squirrel": Value("int64"),
-    "dolphin": Value("int64"),
-    "penguin": Value("float32"),
-    "turtle": Value("float32"),
-    "elephant": Value("string"),
-    "giraffe": Value("int64"),
-    "lamb": Value("string"),
-    "goat": Value("string"),
-    "cow": Value("string"),
-    "horse": Value("string"),
-    "donkey": Value("string"),
-    "pony": Value("int64"),
-    "llama": Value("string"),
-    "mouse": Value("string"),
-    "hamster": Value("string"),
-    "guinea": Value("int64"),
-    "duck": Value("string"),
-    "chicken": Value("float32"),
-    "sparrow": Value("int64"),
-    "parrot": Value("int64"),
-    "finch": Value("int64"),
-    "canary": Value("int64"),
-    "bee": Value("float32"),
-    "butterfly": Value("string"),
-    "ladybug": Value("int64"),
-    "snail": Value("float32"),
-    "frog": Value("int64"),
-    "cricket": Value("int64"),
-    "tamarin": Value("string"),
-    "wallaby": Value("string"),
-    "wombat": Value("int64"),
-    "zebra": Value("int64"),
-    "flamingo": Value("float32"),
-    "peacock": Value("int64"),
-    "bat": Value("int64"),
-    "fox": Value("int64"),
-    "beaver": Value("int64"),
-    "monkey": Value("int64"),
-    "seal": Value("int64"),
-    "robin": Value("int64"),
-    "loon": Value("string"),
-    "swan": Value("int64"),
-    "goldfish": Value("int64"),
-    "minnow": Value("string"),
-    "mole": Value("float32"),
-    "shrew": Value("int64"),
-    "puffin": Value("float32"),
-    "owl": Value("int64"),
-    "bunny": Value("int64"),
-    "bear": Value("int64"),
-    "chipmunk": Value("int64"),
-    "cub": Value("string"),
-    "acorn": Value("float32"),
-    "leaf": Value("string"),
-    "cloud": Value("float32"),
-    "rainbow": Value("int64"),
-    "puddle": Value("string"),
-    "berry": Value("float32"),
-    "apple": Value("int64"),
-    "honey": Value("int64"),
-    "pumpkin": Value("string"),
-    "teddy": Value("string"),
-    "blanket": Value("string"),
-    "button": Value("string"),
-    "whistle": Value("float32"),
-    "marble": Value("int64"),
-    "wagon": Value("string"),
-    "storybook": Value("string"),
-    "candle": Value("float32"),
-    "clover": Value("float32"),
-    "bubble": Value("int64"),
-    "cookie": Value("string")
+    "dog": "int64",
+    "cat": "string",
+    "rabbit": "string",
+    "deer": "float32",
+    "panda": "int64",
+    "koala": "string",
+    "otter": "string",
+    "hedgehog": "float32",
+    "squirrel": "int64",
+    "dolphin": "int64",
+    "penguin": "float32",
+    "turtle": "float32",
+    "elephant": "string",
+    "giraffe": "int64",
+    "lamb": "string",
+    "goat": "string",
+    "cow": "string",
+    "horse": "string",
+    "donkey": "string",
+    "pony": "int64",
+    "llama": "string",
+    "mouse": "string",
+    "hamster": "string",
+    "guinea": "int64",
+    "duck": "string",
+    "chicken": "float32",
+    "sparrow": "int64",
+    "parrot": "int64",
+    "finch": "int64",
+    "canary": "int64",
+    "bee": "float32",
+    "butterfly": "string",
+    "ladybug": "int64",
+    "snail": "float32",
+    "frog": "int64",
+    "cricket": "int64",
+    "tamarin": "string",
+    "wallaby": "string",
+    "wombat": "int64",
+    "zebra": "int64",
+    "flamingo": "float32",
+    "peacock": "int64",
+    "bat": "int64",
+    "fox": "int64",
+    "beaver": "int64",
+    "monkey": "int64",
+    "seal": "int64",
+    "robin": "int64",
+    "loon": "string",
+    "swan": "int64",
+    "goldfish": "int64",
+    "minnow": "string",
+    "mole": "float32",
+    "shrew": "int64",
+    "puffin": "float32",
+    "owl": "int64",
+    "bunny": "int64",
+    "bear": "int64",
+    "chipmunk": "int64",
+    "cub": "string",
+    "acorn": "float32",
+    "leaf": "string",
+    "cloud": "float32",
+    "rainbow": "int64",
+    "puddle": "string",
+    "berry": "float32",
+    "apple": "int64",
+    "honey": "int64",
+    "pumpkin": "string",
+    "teddy": "string",
+    "blanket": "string",
+    "button": "string",
+    "whistle": "float32",
+    "marble": "int64",
+    "wagon": "string",
+    "storybook": "string",
+    "candle": "float32",
+    "clover": "float32",
+    "bubble": "int64",
+    "cookie": "string"
 }
 ```
 
 ### Sequential Dataset Schema (11 columns)
 ```python
 {
-    "group_id": Value("string"),
-    "alice": Value("string"),
-    "david": Value("float32"),
-    "emily": Value("string"),
-    "jacob": Value("string"),
-    "james": Value("float32"),
-    "john": Value("string"),
-    "mike": Value("int64"),
-    "lucas": Value("float32"),
-    "mary": Value("float32"),
-    "sarah": Value("float32")
+    "group_id": "string",
+    "alice": "string",
+    "david": "float32",
+    "emily": "string",
+    "jacob": "string",
+    "james": "float32",
+    "john": "string",
+    "mike": "int64",
+    "lucas": "float32",
+    "mary": "float32",
+    "sarah": "float32"
 }
 ```
 

data/flat/train/flat-training.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d1a8f9b8b4e7d211269f37a95283e96c77145165a09bc892a9b178c9f1f8060
+size 7737713

data/sequential/train/sequential-training.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e6c61106dd5a706ed88f646c52bcd010abd105bf617ba46e7de3579cc1bb28e
+size 1374441

dataset_infos.json

@@ -1,24 +1,91 @@
 {
   "flat": {
-    "description": "This dataset contains the data used in the MOSTLY AI Prize competition.\nThe competition focuses on synthetic data generation and evaluation.\nIt contains two datasets:\n- flat-training.csv.gz: A flat (non-sequential) dataset\n- sequential-training.csv.gz: A sequential dataset",
-    "citation": "@dataset{mostlyaiprize,\n  author = {MOSTLY AI},\n  title = {MOSTLY AI Prize Dataset},\n  year = {2023},\n  url = {https://www.mostlyaiprize.com/},\n}\n",
+    "description": "This dataset contains the data used in the MOSTLY AI Prize competition.\nThe competition focuses on synthetic data generation and evaluation.\nIt contains two datasets:\n- flat-training.csv.gz: A flat (non-sequential) dataset with 100,000 records and 80 columns\n- sequential-training.csv.gz: A sequential dataset with 20,000 groups and 11 columns",
+    "citation": "@dataset{mostlyaiprize,\n  author = {MOSTLY AI},\n  title = {MOSTLY AI Prize Dataset},\n  year = {2025},\n  url = {https://www.mostlyaiprize.com/},\n}\n",
     "homepage": "https://www.mostlyaiprize.com/",
     "license": "Apache License 2.0",
     "features": {
-      "dog": {"_type": "Value", "dtype": "int64"},
-      "cat": {"_type": "Value", "dtype": "string"},
-      "rabbit": {"_type": "Value", "dtype": "string"},
-      "deer": {"_type": "Value", "dtype": "float32"},
-      "panda": {"_type": "Value", "dtype": "int64"},
-      "koala": {"_type": "Value", "dtype": "string"},
-      "otter": {"_type": "Value", "dtype": "string"},
-      "hedgehog": {"_type": "Value", "dtype": "float32"},
-      "squirrel": {"_type": "Value", "dtype": "int64"},
-      "dolphin": {"_type": "Value", "dtype": "int64"},
-      "penguin": {"_type": "Value", "dtype": "int64"},
-      "turtle": {"_type": "Value", "dtype": "float32"}
+      "dog": {"dtype": "int64"},
+      "cat": {"dtype": "string"},
+      "rabbit": {"dtype": "string"},
+      "deer": {"dtype": "float32"},
+      "panda": {"dtype": "int64"},
+      "koala": {"dtype": "string"},
+      "otter": {"dtype": "string"},
+      "hedgehog": {"dtype": "float32"},
+      "squirrel": {"dtype": "int64"},
+      "dolphin": {"dtype": "int64"},
+      "penguin": {"dtype": "float32"},
+      "turtle": {"dtype": "float32"},
+      "elephant": {"dtype": "string"},
+      "giraffe": {"dtype": "int64"},
+      "lamb": {"dtype": "string"},
+      "goat": {"dtype": "string"},
+      "cow": {"dtype": "string"},
+      "horse": {"dtype": "string"},
+      "donkey": {"dtype": "string"},
+      "pony": {"dtype": "int64"},
+      "llama": {"dtype": "string"},
+      "mouse": {"dtype": "string"},
+      "hamster": {"dtype": "string"},
+      "guinea": {"dtype": "int64"},
+      "duck": {"dtype": "string"},
+      "chicken": {"dtype": "float32"},
+      "sparrow": {"dtype": "int64"},
+      "parrot": {"dtype": "int64"},
+      "finch": {"dtype": "int64"},
+      "canary": {"dtype": "int64"},
+      "bee": {"dtype": "float32"},
+      "butterfly": {"dtype": "string"},
+      "ladybug": {"dtype": "int64"},
+      "snail": {"dtype": "float32"},
+      "frog": {"dtype": "int64"},
+      "cricket": {"dtype": "int64"},
+      "tamarin": {"dtype": "string"},
+      "wallaby": {"dtype": "string"},
+      "wombat": {"dtype": "int64"},
+      "zebra": {"dtype": "int64"},
+      "flamingo": {"dtype": "float32"},
+      "peacock": {"dtype": "int64"},
+      "bat": {"dtype": "int64"},
+      "fox": {"dtype": "int64"},
+      "beaver": {"dtype": "int64"},
+      "monkey": {"dtype": "int64"},
+      "seal": {"dtype": "int64"},
+      "robin": {"dtype": "int64"},
+      "loon": {"dtype": "string"},
+      "swan": {"dtype": "int64"},
+      "goldfish": {"dtype": "int64"},
+      "minnow": {"dtype": "string"},
+      "mole": {"dtype": "float32"},
+      "shrew": {"dtype": "int64"},
+      "puffin": {"dtype": "float32"},
+      "owl": {"dtype": "int64"},
+      "bunny": {"dtype": "int64"},
+      "bear": {"dtype": "int64"},
+      "chipmunk": {"dtype": "int64"},
+      "cub": {"dtype": "string"},
+      "acorn": {"dtype": "float32"},
+      "leaf": {"dtype": "string"},
+      "cloud": {"dtype": "float32"},
+      "rainbow": {"dtype": "int64"},
+      "puddle": {"dtype": "string"},
+      "berry": {"dtype": "float32"},
+      "apple": {"dtype": "int64"},
+      "honey": {"dtype": "int64"},
+      "pumpkin": {"dtype": "string"},
+      "teddy": {"dtype": "string"},
+      "blanket": {"dtype": "string"},
+      "button": {"dtype": "string"},
+      "whistle": {"dtype": "float32"},
+      "marble": {"dtype": "int64"},
+      "wagon": {"dtype": "string"},
+      "storybook": {"dtype": "string"},
+      "candle": {"dtype": "float32"},
+      "clover": {"dtype": "float32"},
+      "bubble": {"dtype": "int64"},
+      "cookie": {"dtype": "string"}
     },
-    "builder_name": "mostlyaiprize",
     "config_name": "flat",
     "version": {
       "version_str": "1.0.0",
@@ -31,30 +98,30 @@
       "train": {
         "name": "train",
         "num_bytes": 7864320,
-        "num_examples": null,
-        "dataset_name": "mostlyaiprize"
+        "num_examples": 100000,
+        "dataset_name": "mostlyaiprize",
+        "file_format": "gz"
       }
     }
   },
   "sequential": {
-    "description": "This dataset contains the data used in the MOSTLY AI Prize competition.\nThe competition focuses on synthetic data generation and evaluation.\nIt contains two datasets:\n- flat-training.csv.gz: A flat (non-sequential) dataset\n- sequential-training.csv.gz: A sequential dataset",
-    "citation": "@dataset{mostlyaiprize,\n  author = {MOSTLY AI},\n  title = {MOSTLY AI Prize Dataset},\n  year = {2023},\n  url = {https://www.mostlyaiprize.com/},\n}\n",
+    "description": "This dataset contains the data used in the MOSTLY AI Prize competition.\nThe competition focuses on synthetic data generation and evaluation.\nIt contains two datasets:\n- flat-training.csv.gz: A flat (non-sequential) dataset with 100,000 records and 80 columns\n- sequential-training.csv.gz: A sequential dataset with 20,000 groups and 11 columns",
+    "citation": "@dataset{mostlyaiprize,\n  author = {MOSTLY AI},\n  title = {MOSTLY AI Prize Dataset},\n  year = {2025},\n  url = {https://www.mostlyaiprize.com/},\n}\n",
     "homepage": "https://www.mostlyaiprize.com/",
     "license": "Apache License 2.0",
     "features": {
-      "group_id": {"_type": "Value", "dtype": "string"},
-      "alice": {"_type": "Value", "dtype": "string"},
-      "david": {"_type": "Value", "dtype": "float32"},
-      "emily": {"_type": "Value", "dtype": "string"},
-      "jacob": {"_type": "Value", "dtype": "string"},
-      "james": {"_type": "Value", "dtype": "float32"},
-      "john": {"_type": "Value", "dtype": "string"},
-      "mike": {"_type": "Value", "dtype": "int64"},
-      "lucas": {"_type": "Value", "dtype": "float32"},
-      "mary": {"_type": "Value", "dtype": "float32"},
-      "sarah": {"_type": "Value", "dtype": "float32"}
+      "group_id": {"dtype": "string"},
+      "alice": {"dtype": "string"},
+      "david": {"dtype": "float32"},
+      "emily": {"dtype": "string"},
+      "jacob": {"dtype": "string"},
+      "james": {"dtype": "float32"},
+      "john": {"dtype": "string"},
+      "mike": {"dtype": "int64"},
+      "lucas": {"dtype": "float32"},
+      "mary": {"dtype": "float32"},
+      "sarah": {"dtype": "float32"}
     },
-    "builder_name": "mostlyaiprize",
     "config_name": "sequential",
     "version": {
       "version_str": "1.0.0",
@@ -67,8 +134,9 @@
       "train": {
         "name": "train",
         "num_bytes": 1363149,
-        "num_examples": null,
-        "dataset_name": "mostlyaiprize"
+        "num_examples": 140000,
+        "dataset_name": "mostlyaiprize",
+        "file_format": "gz"
       }
     }
   }

mostlyaiprize.py

@@ -1,259 +0,0 @@
-import os
-import pandas as pd
-import datasets
-from datasets import Dataset, DatasetDict, Features, Value
-
-_CITATION = """
-@dataset{mostlyaiprize,
-  author = {MOSTLY AI},
-  title = {MOSTLY AI Prize Dataset},
-  year = {2025},
-  url = {https://www.mostlyaiprize.com/},
-}
-"""
-
-_DESCRIPTION = """
-This dataset contains the data used in the MOSTLY AI Prize competition.
-The competition focuses on synthetic data generation and evaluation.
-It contains two datasets:
-- flat-training.csv.gz: A flat (non-sequential) dataset with 100,000 records and 80 columns (60 numeric, 20 categorical)
-- sequential-training.csv.gz: A sequential dataset with 20,000 groups and 11 columns
-"""
-
-_HOMEPAGE = "https://www.mostlyaiprize.com/"
-_LICENSE = "Apache License 2.0"
-
-# Define the features for each dataset
-_FLAT_FEATURES = {
-    "dog": Value("int64"),
-    "cat": Value("string"),
-    "rabbit": Value("string"),
-    "deer": Value("float32"),
-    "panda": Value("int64"),
-    "koala": Value("string"),
-    "otter": Value("string"),
-    "hedgehog": Value("float32"),
-    "squirrel": Value("int64"),
-    "dolphin": Value("int64"),
-    "penguin": Value("float32"),
-    "turtle": Value("float32"),
-    "elephant": Value("string"),
-    "giraffe": Value("int64"),
-    "lamb": Value("string"),
-    "goat": Value("string"),
-    "cow": Value("string"),
-    "horse": Value("string"),
-    "donkey": Value("string"),
-    "pony": Value("int64"),
-    "llama": Value("string"),
-    "mouse": Value("string"),
-    "hamster": Value("string"),
-    "guinea": Value("int64"),
-    "duck": Value("string"),
-    "chicken": Value("float32"),
-    "sparrow": Value("int64"),
-    "parrot": Value("int64"),
-    "finch": Value("int64"),
-    "canary": Value("int64"),
-    "bee": Value("float32"),
-    "butterfly": Value("string"),
-    "ladybug": Value("int64"),
-    "snail": Value("float32"),
-    "frog": Value("int64"),
-    "cricket": Value("int64"),
-    "tamarin": Value("string"),
-    "wallaby": Value("string"),
-    "wombat": Value("int64"),
-    "zebra": Value("int64"),
-    "flamingo": Value("float32"),
-    "peacock": Value("int64"),
-    "bat": Value("int64"),
-    "fox": Value("int64"),
-    "beaver": Value("int64"),
-    "monkey": Value("int64"),
-    "seal": Value("int64"),
-    "robin": Value("int64"),
-    "loon": Value("string"),
-    "swan": Value("int64"),
-    "goldfish": Value("int64"),
-    "minnow": Value("string"),
-    "mole": Value("float32"),
-    "shrew": Value("int64"),
-    "puffin": Value("float32"),
-    "owl": Value("int64"),
-    "bunny": Value("int64"),
-    "bear": Value("int64"),
-    "chipmunk": Value("int64"),
-    "cub": Value("string"),
-    "acorn": Value("float32"),
-    "leaf": Value("string"),
-    "cloud": Value("float32"),
-    "rainbow": Value("int64"),
-    "puddle": Value("string"),
-    "berry": Value("float32"),
-    "apple": Value("int64"),
-    "honey": Value("int64"),
-    "pumpkin": Value("string"),
-    "teddy": Value("string"),
-    "blanket": Value("string"),
-    "button": Value("string"),
-    "whistle": Value("float32"),
-    "marble": Value("int64"),
-    "wagon": Value("string"),
-    "storybook": Value("string"),
-    "candle": Value("float32"),
-    "clover": Value("float32"),
-    "bubble": Value("int64"),
-    "cookie": Value("string")
-}
-
-_SEQUENTIAL_FEATURES = {
-    "group_id": Value("string"),
-    "alice": Value("string"),
-    "david": Value("float32"),
-    "emily": Value("string"),
-    "jacob": Value("string"),
-    "james": Value("float32"),
-    "john": Value("string"),
-    "mike": Value("int64"),
-    "lucas": Value("float32"),
-    "mary": Value("float32"),
-    "sarah": Value("float32")
-}
-
-class MostlyAIPrizeConfig(datasets.BuilderConfig):
-    """BuilderConfig for MOSTLY AI Prize dataset."""
-
-    def __init__(self, features, data_file, **kwargs):
-        """BuilderConfig for MOSTLY AI Prize.
-        Args:
-          features: Features of the dataset
-          data_file: The data file to load
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MostlyAIPrizeConfig, self).__init__(**kwargs)
-        self.features = features
-        self.data_file = data_file
-
-class MostlyAIPrize(datasets.GeneratorBasedBuilder):
-    """MOSTLY AI Prize dataset for synthetic data generation competition."""
-
-    VERSION = datasets.Version("1.0.0")
-    
-    BUILDER_CONFIGS = [
-        MostlyAIPrizeConfig(
-            name="flat",
-            description="Flat dataset with 100,000 records and 80 columns (60 numeric, 20 categorical)",
-            features=_FLAT_FEATURES,
-            data_file="flat-training.csv.gz",
-        ),
-        MostlyAIPrizeConfig(
-            name="sequential",
-            description="Sequential dataset with 20,000 groups and 11 columns",
-            features=_SEQUENTIAL_FEATURES,
-            data_file="sequential-training.csv.gz",
-        ),
-    ]
-    
-    DEFAULT_CONFIG_NAME = "flat"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=Features(self.config.features),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
-        data_file = os.path.join(data_dir, self.config.data_file)
-        
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_file,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate examples from the dataset file."""
-        df = pd.read_csv(filepath, compression="gzip")
-        
-        for idx, row in df.iterrows():
-            yield idx, {col: row[col] for col in self.config.features}
-            
-    # Add a method to provide dataset visualization information
-    @classmethod
-    def get_visualization_config(cls, config_name="flat"):
-        """Return configuration for dataset visualization on Hugging Face.
-        
-        This helps enhance the dataset preview with more than just a flat table.
-        """
-        if config_name == "flat":
-            return {
-                "type": "table-and-charts",
-                "charts": [
-                    {
-                        "type": "histogram",
-                        "column": "dog",
-                        "title": "Distribution of 'dog' values"
-                    },
-                    {
-                        "type": "histogram", 
-                        "column": "deer",
-                        "title": "Distribution of 'deer' values"
-                    },
-                    {
-                        "type": "histogram", 
-                        "column": "chicken",
-                        "title": "Distribution of 'chicken' values"
-                    },
-                    {
-                        "type": "bar",
-                        "column": "cat",
-                        "title": "Count of 'cat' categories"
-                    },
-                    {
-                        "type": "bar",
-                        "column": "koala",
-                        "title": "Count of 'koala' categories"
-                    }
-                ]
-            }
-        elif config_name == "sequential":
-            return {
-                "type": "table-and-charts",
-                "charts": [
-                    {
-                        "type": "histogram",
-                        "column": "mike",
-                        "title": "Distribution of 'mike' values"
-                    },
-                    {
-                        "type": "histogram",
-                        "column": "david",
-                        "title": "Distribution of 'david' values"
-                    },
-                    {
-                        "type": "histogram",
-                        "column": "james",
-                        "title": "Distribution of 'james' values"
-                    },
-                    {
-                        "type": "bar",
-                        "column": "alice",
-                        "title": "Count of 'alice' categories"
-                    },
-                    {
-                        "type": "bar",
-                        "column": "john",
-                        "title": "Count of 'john' categories"
-                    }
-                ]
-            }
-        return {"type": "table"}