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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run pre-trained DeepSeek Coder 1.3B Model on Chat-GPT 4o generated dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First load dataset into pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total dataset examples: 1044\n",
"\n",
"\n",
"What is the highest number of assists recorded by the Indiana Pacers in a single home game?\n",
"SELECT MAX(ast_home) FROM game WHERE team_name_home = 'Indiana Pacers';\n",
"44.0\n"
]
}
],
"source": [
"import pandas as pd \n",
"\n",
"# Load dataset and check length\n",
"df = pd.read_csv(\"./train-data/sql_train.tsv\", sep='\\t')\n",
"print(\"Total dataset examples: \" + str(len(df)))\n",
"print(\"\\n\")\n",
"\n",
"# Test sampling\n",
"sample = df.sample(n=1)\n",
"print(sample[\"natural_query\"].values[0])\n",
"print(sample[\"sql_query\"].values[0])\n",
"print(sample[\"result\"].values[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load pre-trained DeepSeek model using transformers and pytorch packages"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"import torch\n",
"\n",
"# Set device to cuda if available, otherwise CPU\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Load model and tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"./deepseek-coder-1.3b-instruct\")\n",
"model = AutoModelForCausalLM.from_pretrained(\"./deepseek-coder-1.3b-instruct\", torch_dtype=torch.bfloat16, device_map=device) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create prompt to setup the model for better performance"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"input_text = \"\"\"You are an AI assistant that generates SQLite queries for an NBA database based on user questions. The database consists of two tables:\n",
"\n",
"1. `team` - Stores information about NBA teams.\n",
" - `id`: Unique team identifier.\n",
" - `full_name`: Full team name (e.g., \"Los Angeles Lakers\").\n",
" - `abbreviation`: 3-letter team code (e.g., \"LAL\").\n",
" - `city`, `state`: Location of the team.\n",
" - `year_founded`: The year the team was founded.\n",
"\n",
"2. `game` - Stores details of individual games.\n",
" - `game_date`: Date of the game.\n",
" - `team_id_home`, `team_id_away`: Unique IDs of home and away teams.\n",
" - `team_name_home`, `team_name_away`: Full names of the teams.\n",
" - `pts_home`, `pts_away`: Points scored by home and away teams.\n",
" - `wl_home`: \"W\" if the home team won, \"L\" if they lost.\n",
" - `reb_home`, `reb_away`: Total rebounds.\n",
" - `ast_home`, `ast_away`: Total assists.\n",
" - Other statistics include field goals (`fgm_home`, `fg_pct_home`), three-pointers (`fg3m_home`), free throws (`ftm_home`), and turnovers (`tov_home`).\n",
"\n",
"### Instructions:\n",
"- Generate a valid SQLite query to retrieve relevant data from the database.\n",
"- Use column names correctly based on the provided schema.\n",
"- Ensure the query is well-structured and avoids unnecessary joins.\n",
"- Format the query with proper indentation.\n",
"\n",
"### Example Queries:\n",
"User: \"What is the most points the Los Angeles Lakers have ever scored at home?\"\n",
"SQLite:\n",
"SELECT MAX(pts_home) \n",
"FROM game \n",
"WHERE team_name_home = 'Los Angeles Lakers';\n",
"\n",
"User: \"List all games where the Golden State Warriors scored more than 130 points.\" \n",
"SQLite:\n",
"SELECT game_date, team_name_home, pts_home, team_name_away, pts_away\n",
"FROM game\n",
"WHERE (team_name_home = 'Golden State Warriors' AND pts_home > 130)\n",
" OR (team_name_away = 'Golden State Warriors' AND pts_away > 130);\n",
" \n",
"Now, generate a SQL query based on the following user request: \"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test model performance on a single example"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Dean\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\transformers\\generation\\configuration_utils.py:634: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
" warnings.warn(\n",
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:32021 for open-end generation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SQLite:\n",
"SELECT MAX(ast_home) \n",
"FROM game \n",
"WHERE team_name_home = 'Indiana Pacers';\n",
"\n"
]
}
],
"source": [
"# Create message with sample query and run model\n",
"message=[{ 'role': 'user', 'content': input_text + sample[\"natural_query\"].values[0]}]\n",
"inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors=\"pt\").to(model.device)\n",
"outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)\n",
"\n",
"# Print output\n",
"query_output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)\n",
"print(query_output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test sample output on sqlite3 database"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cleaned\n",
"(44.0,)\n"
]
}
],
"source": [
"import sqlite3 as sql\n",
"\n",
"# Create connection to sqlite3 database\n",
"connection = sql.connect('./nba-data/nba.sqlite')\n",
"cursor = connection.cursor()\n",
"\n",
"# Execute query from model output and print result\n",
"if query_output[0:7] == \"SQLite:\":\n",
" print(\"cleaned\")\n",
" query = query_output[7:]\n",
"elif query_output[0:4] == \"SQL:\":\n",
" query = query_output[4:]\n",
"else:\n",
" query = query_output\n",
"cursor.execute(query)\n",
"rows = cursor.fetchall()\n",
"for row in rows:\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create function to compare output to ground truth result from examples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cleaned\n",
"[(44.0,)]\n",
"\n",
"SELECT MAX(ast_home) \n",
"FROM game \n",
"WHERE team_name_home = 'Indiana Pacers';\n",
"\n",
"SELECT MAX(ast_home) FROM game WHERE team_name_home = 'Indiana Pacers';\n",
"44.0\n",
"44.0\n",
"SQL matched? True\n",
"Result matched? True\n"
]
}
],
"source": [
"def compare_result(sample_query, sample_result, query_output):\n",
" # Clean model output to only have the query output\n",
" if query_output[0:7] == \"SQLite:\":\n",
" query = query_output[7:]\n",
" elif query_output[0:4] == \"SQL:\":\n",
" query = query_output[4:]\n",
" else:\n",
" query = query_output\n",
" \n",
" # Try to execute query, if it fails, then this is a failure of the model\n",
" try:\n",
" # Execute query and obtain result\n",
" cursor.execute(query)\n",
" rows = cursor.fetchall()\n",
"\n",
" # Check if this is a multi-line query\n",
" if \"|\" in sample_result:\n",
" return True, True\n",
" else:\n",
" # Strip all whitespace before comparing queries since there may be differences in spacing, newlines, tabs, etc.\n",
" query = query.replace(\" \", \"\").replace(\"\\n\", \"\").replace(\"\\t\", \"\")\n",
" sample_query = sample_query.replace(\" \", \"\").replace(\"\\n\", \"\").replace(\"\\t\", \"\")\n",
"\n",
" # Compare results and return\n",
" return (query == sample_query), (str(rows[0][0]) == str(sample_result))\n",
" except:\n",
" return False, False\n",
"\n",
"result = compare_result(sample[\"sql_query\"].values[0], sample[\"result\"].values[0], query_output)\n",
"print(\"SQL matched? \" + str(result[0]))\n",
"print(\"Result matched? \" + str(result[1]))"
]
}
],
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