First iteration of the MBPP dataset

README.md

@@ -1,3 +1,17 @@
 ---
+---
+pretty_name: MBPP
 license: mit
+description: A formatted version of MBPP. 
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: mbpp.csv
+
+---
 ---
+
+# Information
+
+This is a reformatted version of the [HumanEval dataset](https://github.com/openai/human-eval) 

convert.ipynb

@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "x = open(\"./mbpp.jsonl\")\n",
+    "entries = []\n",
+    "for line in x:\n",
+    "    contents = json.loads(line)\n",
+    "    entries.append(contents)\n",
+    "x.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'text': 'Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].', 'code': 'R = 3\\r\\nC = 3\\r\\ndef min_cost(cost, m, n): \\r\\n\\ttc = [[0 for x in range(C)] for x in range(R)] \\r\\n\\ttc[0][0] = cost[0][0] \\r\\n\\tfor i in range(1, m+1): \\r\\n\\t\\ttc[i][0] = tc[i-1][0] + cost[i][0] \\r\\n\\tfor j in range(1, n+1): \\r\\n\\t\\ttc[0][j] = tc[0][j-1] + cost[0][j] \\r\\n\\tfor i in range(1, m+1): \\r\\n\\t\\tfor j in range(1, n+1): \\r\\n\\t\\t\\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \\r\\n\\treturn tc[m][n]', 'task_id': 1, 'test_setup_code': '', 'test_list': ['assert min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8', 'assert min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12', 'assert min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16'], 'challenge_test_list': []}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(entries[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {\"source\": [], \"target\": [], \"program_id\": []}\n",
+    "\n",
+    "for i,entry in enumerate(entries):\n",
+    "    data[\"source\"].append(entry[\"text\"])\n",
+    "    data[\"target\"].append(entry[\"code\"])\n",
+    "    data[\"program_id\"].append(\"MBPP_\"+str(i))\n",
+    "\n",
+    "pd.DataFrame(data=data).to_csv(\"./mbpp.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}