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13 changes: 13 additions & 0 deletions notebooks/kernel-metadata.json
Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
{
"id": "nvandessel/hippofloop",
"title": "hippofloop",
"code_file": "train-hippofloop.ipynb",
"language": "python",
"kernel_type": "notebook",
"is_private": true,
"enable_gpu": true,
"enable_internet": true,
"dataset_sources": ["nvandessel/floop-decisions"],
"competition_sources": [],
"kernel_sources": []
}
18 changes: 2 additions & 16 deletions notebooks/train-hippofloop.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -51,28 +51,14 @@
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Load and explore data\n",
"\n",
"Data is expected as a Kaggle Dataset mounted at `/kaggle/input/floop-decisions/`.\n",
"Upload your `decisions.jsonl` files there."
]
"source": "## 2. Load and explore data\n\nData is expected as a Kaggle Dataset. The path is auto-detected."
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DATA_DIR = Path(\"/kaggle/input/floop-decisions\")\n",
"\n",
"# Find all JSONL files in the dataset\n",
"jsonl_files = sorted(DATA_DIR.glob(\"*.jsonl\"))\n",
"print(f\"Found {len(jsonl_files)} JSONL files:\")\n",
"for f in jsonl_files:\n",
" size_mb = f.stat().st_size / (1024 * 1024)\n",
" print(f\" {f.name} ({size_mb:.1f} MB)\")"
]
"source": "# Auto-detect Kaggle dataset path\n_candidates = [\n Path(\"/kaggle/input/datasets/nvandessel/floop-decisions\"),\n Path(\"/kaggle/input/floop-decisions\"),\n]\nDATA_DIR = next((p for p in _candidates if p.exists()), None)\nif DATA_DIR is None:\n raise FileNotFoundError(\n f\"Kaggle dataset not found. Checked: {[str(p) for p in _candidates]}. \"\n \"Add the 'floop-decisions' dataset in the notebook sidebar.\"\n )\nprint(f\"Data dir: {DATA_DIR}\")\n\n# Find all JSONL files in the dataset\njsonl_files = sorted(DATA_DIR.glob(\"*.jsonl\"))\nif not jsonl_files:\n raise FileNotFoundError(f\"No .jsonl files found in {DATA_DIR}\")\nprint(f\"Found {len(jsonl_files)} JSONL files:\")\nfor f in jsonl_files:\n size_mb = f.stat().st_size / (1024 * 1024)\n print(f\" {f.name} ({size_mb:.1f} MB)\")"
},
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{
"cell_type": "code",
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