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Notebook showing how to fine-tune GPT3 models using OpenAI API. The task is predicting the title of a paper from the abstract.
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GPT3_finetuning_paper_title_from_abstract.ipynb
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},
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"## Running\n",
"You'll need to provide an [OpenAI API key](https://openai.com/blog/api-no-waitlist/). I stored that in the `.env` file and use `dotenv` to load it to the environment.\n",
"\n",
"IMPORTANT: Don't put quotes around your key. If you get your key wrong, you will need to go to `Runtime > Restart runtime` and run all your cells again."
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 305,
"outputs": [
{
"data": {
"text/plain": "True"
},
"execution_count": 305,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Install the CLI for openai"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 294,
"outputs": [],
"source": [
"!pip install --upgrade openai"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Metrics are provided in NLTK"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 133,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: nltk in /home/lorenzo/venv/OpenAI/lib/python3.8/site-packages (3.7)\r\n",
"Requirement already satisfied: joblib in /home/lorenzo/venv/OpenAI/lib/python3.8/site-packages (from nltk) (1.2.0)\r\n",
"Requirement already satisfied: regex>=2021.8.3 in /home/lorenzo/venv/OpenAI/lib/python3.8/site-packages (from nltk) (2022.10.31)\r\n",
"Requirement already satisfied: click in /home/lorenzo/venv/OpenAI/lib/python3.8/site-packages (from nltk) (8.1.3)\r\n",
"Requirement already satisfied: tqdm in /home/lorenzo/venv/OpenAI/lib/python3.8/site-packages (from nltk) (4.64.1)\r\n",
"\u001B[33mWARNING: You are using pip version 21.3.1; however, version 22.3.1 is available.\r\n",
"You should consider upgrading via the '/home/lorenzo/venv/OpenAI/bin/python -m pip install --upgrade pip' command.\u001B[0m\r\n"
]
}
],
"source": [
"!pip install nltk"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Get some things from arxiv; see [here](https://arxiv.org/help/api/user-manual) on how to use the API"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"The following is from [this website](https://python.plainenglish.io/analysis-of-the-arxiv-papers-for-a-topic-using-api-382111dfae2b)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"I extract from arxiv the abstract and title of all papers which have \"natural language\" and \"language models\" in their abstract and are in the category \"cs.LG\"."
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 295,
"outputs": [],
"source": [
"import requests\n",
"import xml.etree.ElementTree as ET\n",
"\n",
"query = 'abs:\"natural language\"+AND+abs:\"language models\"+AND+cat:\"cs.LG\"'\n",
"max_results = 1000"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 296,
"outputs": [],
"source": [
"url = f'http://export.arxiv.org/api/query?search_query={query}&max_results={max_results}'\n",
"resp = requests.get(url)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 297,
"outputs": [],
"source": [
"ns = {'r': 'http://www.w3.org/2005/Atom'}\n",
"root = ET.fromstring(resp.text)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 298,
"outputs": [],
"source": [
"all_papers = list()\n",
"entries = root.findall('r:entry', namespaces=ns)\n",
"for entry in entries:\n",
" all_papers.append({l.tag[l.tag.index('}') + 1:]: l.text for l in entry})"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 299,
"outputs": [
{
"data": {
"text/plain": " id updated \\\n0 http://arxiv.org/abs/1812.01216v1 2018-12-04T04:49:50Z \n1 http://arxiv.org/abs/1911.06415v1 2019-11-14T23:31:02Z \n2 http://arxiv.org/abs/1711.03953v4 2018-03-02T20:20:52Z \n3 http://arxiv.org/abs/2111.09791v1 2021-11-18T16:47:56Z \n4 http://arxiv.org/abs/1906.03591v2 2019-06-13T01:49:23Z \n\n published title \\\n0 2018-12-04T04:49:50Z Parameter Re-Initialization through Cyclical B... \n1 2019-11-14T23:31:02Z Sparse associative memory based on contextual ... \n2 2017-11-10T18:29:00Z Breaking the Softmax Bottleneck: A High-Rank R... \n3 2021-11-18T16:47:56Z Supporting Undotted Arabic with Pre-trained La... \n4 2019-06-09T08:15:53Z A Survey on Neural Network Language Models \n\n summary author \\\n0 Optimal parameter initialization remains a c... \\n \n1 In recent literature, contextual pretrained ... \\n \n2 We formulate language modeling as a matrix f... \\n \n3 We observe a recent behaviour on social medi... \\n \n4 As the core component of Natural Language Pr... \\n \n\n comment journal_ref \\\n0 Presented in Systems for Machine Learning Work... NeurIPS 2018 Workshop \n1 NaN NaN \n2 ICLR Oral 2018 NaN \n3 Paper accepted to 4th International Conference... NaN \n4 NaN NaN \n\n link primary_category category doi \n0 None None None NaN \n1 None None None NaN \n2 None None None NaN \n3 None None None NaN \n4 None None None NaN ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>updated</th>\n <th>published</th>\n <th>title</th>\n <th>summary</th>\n <th>author</th>\n <th>comment</th>\n <th>journal_ref</th>\n <th>link</th>\n <th>primary_category</th>\n <th>category</th>\n <th>doi</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>http://arxiv.org/abs/1812.01216v1</td>\n <td>2018-12-04T04:49:50Z</td>\n <td>2018-12-04T04:49:50Z</td>\n <td>Parameter Re-Initialization through Cyclical B...</td>\n <td>Optimal parameter initialization remains a c...</td>\n <td>\\n</td>\n <td>Presented in Systems for Machine Learning Work...</td>\n <td>NeurIPS 2018 Workshop</td>\n <td>None</td>\n <td>None</td>\n <td>None</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>http://arxiv.org/abs/1911.06415v1</td>\n <td>2019-11-14T23:31:02Z</td>\n <td>2019-11-14T23:31:02Z</td>\n <td>Sparse associative memory based on contextual ...</td>\n <td>In recent literature, contextual pretrained ...</td>\n <td>\\n</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>None</td>\n <td>None</td>\n <td>None</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>http://arxiv.org/abs/1711.03953v4</td>\n <td>2018-03-02T20:20:52Z</td>\n <td>2017-11-10T18:29:00Z</td>\n <td>Breaking the Softmax Bottleneck: A High-Rank R...</td>\n <td>We formulate language modeling as a matrix f...</td>\n <td>\\n</td>\n <td>ICLR Oral 2018</td>\n <td>NaN</td>\n <td>None</td>\n <td>None</td>\n <td>None</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>http://arxiv.org/abs/2111.09791v1</td>\n <td>2021-11-18T16:47:56Z</td>\n <td>2021-11-18T16:47:56Z</td>\n <td>Supporting Undotted Arabic with Pre-trained La...</td>\n <td>We observe a recent behaviour on social medi...</td>\n <td>\\n</td>\n <td>Paper accepted to 4th International Conference...</td>\n <td>NaN</td>\n <td>None</td>\n <td>None</td>\n <td>None</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>http://arxiv.org/abs/1906.03591v2</td>\n <td>2019-06-13T01:49:23Z</td>\n <td>2019-06-09T08:15:53Z</td>\n <td>A Survey on Neural Network Language Models</td>\n <td>As the core component of Natural Language Pr...</td>\n <td>\\n</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>None</td>\n <td>None</td>\n <td>None</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 299,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"all_papers_df = pd.DataFrame(all_papers)\n",
"all_papers_df.head()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 300,
"outputs": [
{
"data": {
"text/plain": "636"
},
"execution_count": 300,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(all_papers_df)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"The output is already shuffled. Now take the first 500 as train set and the remaining as test set and save to CSV file only the title and summary columns."
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 301,
"outputs": [],
"source": [
"all_papers_df = all_papers_df[['summary', 'title']]\n",
"\n",
"all_papers_df.rename(columns={'summary': 'prompt'}, inplace=True)\n",
"# rename 'title' to 'completion'\n",
"all_papers_df.rename(columns={'title': 'completion'}, inplace=True)\n",
"\n",
"# add the string '\\n\\n###\\n\\n' to the end of each prompt\n",
"all_papers_df['prompt'] = all_papers_df['prompt'].apply(lambda x: x + '\\n\\n###\\n\\n')\n",
"# add ' ' to the beginning of each completion\n",
"all_papers_df['completion'] = all_papers_df['completion'].apply(lambda x: ' ' + x)\n",
"# add '\\n' to the end of each completion\n",
"all_papers_df['completion'] = all_papers_df['completion'].apply(lambda x: x + '###')\n",
"\n",
"# keep only the first 500 elements\n",
"train_papers = all_papers_df[:500]\n",
"# remaining rows\n",
"test_papers = all_papers_df[500:]\n",
"\n",
"train_papers.to_csv('train.csv', index=False)\n",
"test_papers.to_csv('test.csv', index=False)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 302,
"outputs": [
{
"data": {
"text/plain": "136"
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(test_papers) #.head()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Now use CLI data preparation tool"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 113,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Analyzing...\r\n",
"\r\n",
"- Based on your file extension, your file is formatted as a CSV file\r\n",
"- Your file contains 500 prompt-completion pairs\r\n",
"- All prompts end with suffix `\\n\\n\\n###\\n\\n`\r\n",
"- All prompts start with prefix ` `\r\n",
"- All completions end with suffix `###`\r\n",
"\r\n",
"Based on the analysis we will perform the following actions:\r\n",
"- [Necessary] Your format `CSV` will be converted to `JSONL`\r\n",
"\r\n",
"\r\n",
"Your data will be written to a new JSONL file. Proceed [Y/n]: Y\r\n",
"\r\n",
"Wrote modified file to `train_prepared (1).jsonl`\r\n",
"Feel free to take a look!\r\n",
"\r\n",
"Now use that file when fine-tuning:\r\n",
"> openai api fine_tunes.create -t \"train_prepared (1).jsonl\"\r\n",
"\r\n",
"After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `\\n\\n\\n###\\n\\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=[\"###\"]` so that the generated texts ends at the expected place.\r\n",
"Once your model starts training, it'll approximately take 9.31 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\r\n"
]
}
],
"source": [
"!openai tools fine_tunes.prepare_data -f train.csv -q"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 114,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Analyzing...\r\n",
"\r\n",
"- Based on your file extension, your file is formatted as a CSV file\r\n",
"- Your file contains 136 prompt-completion pairs\r\n",
"- All prompts end with suffix `\\n\\n\\n###\\n\\n`\r\n",
"- All prompts start with prefix ` `\r\n",
"- All completions end with suffix `###`\r\n",
"\r\n",
"Based on the analysis we will perform the following actions:\r\n",
"- [Necessary] Your format `CSV` will be converted to `JSONL`\r\n",
"\r\n",
"\r\n",
"Your data will be written to a new JSONL file. Proceed [Y/n]: Y\r\n",
"\r\n",
"Wrote modified file to `test_prepared (1).jsonl`\r\n",
"Feel free to take a look!\r\n",
"\r\n",
"Now use that file when fine-tuning:\r\n",
"> openai api fine_tunes.create -t \"test_prepared (1).jsonl\"\r\n",
"\r\n",
"After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `\\n\\n\\n###\\n\\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=[\"###\"]` so that the generated texts ends at the expected place.\r\n",
"Once your model starts training, it'll approximately take 4.31 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\r\n"
]
}
],
"source": [
"!openai tools fine_tunes.prepare_data -f test.csv -q"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Now start the fine-tuning; it may take a bit to finish\n",
"We start with ada for a cheaper and faster trial"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 115,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Upload progress: 100%|███████████████████████| 647k/647k [00:00<00:00, 598Mit/s]\r\n",
"Uploaded file from train_prepared.jsonl: file-dAdDY5v3pkbCCu7hXsghiB5P\r\n",
"Upload progress: 100%|███████████████████████| 216k/216k [00:00<00:00, 157Mit/s]\r\n",
"Uploaded file from test_prepared.jsonl: file-EJnZT9ePCYEhs6E0N1Jfdo6M\r\n",
"Created fine-tune: ft-j4oRyVINs1Ev2zreKLqHiPlg\r\n",
"Streaming events until fine-tuning is complete...\r\n",
"\r\n",
"(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\r\n",
"[2022-11-25 23:50:18] Created fine-tune: ft-j4oRyVINs1Ev2zreKLqHiPlg\r\n",
"[2022-11-25 23:50:29] Fine-tune costs $0.21\r\n",
"[2022-11-25 23:50:30] Fine-tune enqueued. Queue number: 0\r\n",
"[2022-11-25 23:50:33] Fine-tune started\r\n",
"[2022-11-25 23:52:01] Completed epoch 1/4\r\n",
"[2022-11-25 23:53:14] Completed epoch 2/4\r\n",
"[2022-11-25 23:54:27] Completed epoch 3/4\r\n",
"[2022-11-25 23:55:40] Completed epoch 4/4\r\n",
"[2022-11-25 23:55:57] Uploaded model: ada:ft-personal:arxiv-title-from-abstract-2022-11-25-22-55-56\r\n",
"[2022-11-25 23:55:57] Uploaded result file: file-cDqsxVRQ7mVySDxuKgf2U2IX\r\n",
"[2022-11-25 23:55:57] Fine-tune succeeded\r\n",
"\r\n",
"Job complete! Status: succeeded 🎉\r\n",
"Try out your fine-tuned model:\r\n",
"\r\n",
"openai api completions.create -m ada:ft-personal:arxiv-title-from-abstract-2022-11-25-22-55-56 -p <YOUR_PROMPT>\r\n"
]
}
],
"source": [
"!openai api fine_tunes.create -t train_prepared.jsonl -v test_prepared.jsonl -m ada --suffix \"arxiv_title_from_abstract\""
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"I spent $0.21 for training Ada on that. Davinci costs 300/4 times as much"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 130,
"outputs": [
{
"data": {
"text/plain": "15.75"
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.21 * 300 / 4"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Now on davinci (you may want to run this on a separate terminal)."
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 304,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usage: openai [-h] [-v] [-b API_BASE] [-k API_KEY] [-o ORGANIZATION]\r\n",
" {api,tools,wandb} ...\r\n",
"openai: error: unrecognized arguments: -q\r\n"
]
}
],
"source": [
"!openai api fine_tunes.create -t train_prepared.jsonl -v test_prepared.jsonl -m davinci --suffix \"arxiv_title_from_abstract\""
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# Retrieve the state of a fine-tune. The resulting object includes\n",
"# job status (which can be one of pending, running, succeeded, or failed)\n",
"# and other information\n",
"!openai api fine_tunes.get -i < YOUR_FINE_TUNE_JOB_ID >\n",
"\n",
"# Cancel a job\n",
"#!openai api fine_tunes.cancel -i <YOUR_FINE_TUNE_JOB_ID>"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"# Use the fine-tuned model"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 331,
"outputs": [],
"source": [
"fine_tuned_ada = 'ada:ft-personal:arxiv-title-from-abstract-2022-11-25-22-55-56'\n",
"fine_tuned_davinci = 'davinci:ft-personal:arxiv-title-from-abstract-2022-11-26-15-53-15'"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 330,
"outputs": [],
"source": [
"import openai\n",
"\n",
"res = openai.Completion.create(\n",
" model=fine_tuned_davinci,\n",
" prompt=test_papers['prompt'].iloc[1],\n",
" stop=\"###\",\n",
" max_tokens=50,\n",
" temperature=1, presence_penalty=1, top_p=0.2)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 332,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROPOSED TITLE\n",
" Surveying and Organizing Research on Prompt-based Learning for Natural\n",
" Language Processing\n",
"TRUE TITLE\n",
" Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods\n",
" in Natural Language Processing\n"
]
}
],
"source": [
"print(\"PROPOSED TITLE\")\n",
"print(res.choices[0].text)\n",
"print(\"TRUE TITLE\")\n",
"print(test_papers['completion'].iloc[1][:-3])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Try the metrics for evaluating performance\n",
"\n",
"Want now to assess the performance of the fine-tuned model with respect to the original one. Can use the BLEU score for instance, or similar ones."
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 144,
"outputs": [],
"source": [
"# some tools for strings\n",
"\n",
"# remove empty strings from a list of strings\n",
"def remove_empty_strings(list_of_strings):\n",
" return [string for string in list_of_strings if string != '']\n",
"\n",
"\n",
"# remove '\\n' from a string\n",
"def remove_newline(string):\n",
" return string.replace('\\n', '')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 208,
"outputs": [],
"source": [
"from datasets import load_metric\n",
"\n",
"bleu = load_metric(\"bleu\")\n",
"rouge = load_metric(\"rouge\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 209,
"outputs": [
{
"data": {
"text/plain": "[['Prompt-Based',\n 'Learning',\n 'in',\n 'Natural',\n 'Language',\n 'Processing:',\n 'A',\n 'Survey']]"
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_proposed_completion = [res.choices[0].text.split(' ')[1:]]\n",
"list_proposed_completion"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 210,
"outputs": [
{
"data": {
"text/plain": "[[['Pre-train,',\n 'Prompt,',\n 'and',\n 'Predict:',\n 'A',\n 'Systematic',\n 'Survey',\n 'of',\n 'Prompting',\n 'Methods',\n 'in',\n 'Natural',\n 'Language',\n 'Processing']]]"
},
"execution_count": 210,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_true_completion = [[remove_empty_strings(remove_newline(test_papers['completion'].iloc[1][0:-3]).split(' '))]]\n",
"list_true_completion"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Compute the metrics"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 211,
"outputs": [],
"source": [
"bleu_scores = bleu.compute(predictions=list_proposed_completion, references=list_true_completion)\n",
"rouge_scores = rouge.compute(predictions=list_proposed_completion, references=list_true_completion)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 214,
"outputs": [
{
"data": {
"text/plain": "AggregateScore(low=Score(precision=0.375, recall=0.21428571428571427, fmeasure=0.2727272727272727), mid=Score(precision=0.375, recall=0.21428571428571427, fmeasure=0.2727272727272727), high=Score(precision=0.375, recall=0.21428571428571427, fmeasure=0.2727272727272727))"
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rouge_scores['rouge2']"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Evaluate performance with respect to the original model\n",
"\n",
"Now obtain all completions from the fine-tuned and original ada model"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 368,
"outputs": [],
"source": [
"def prompt_model(model, inputs, max_length=50, stop_string=None, max_parallel=10, prefix=\"\", suffix=\"\", presence_penalty=0):\n",
" outputs = []\n",
"\n",
" # add prefix to all inputs\n",
" inputs = [prefix + string + suffix for string in inputs]\n",
"\n",
" n_batches = int(np.ceil(len(inputs) / max_parallel))\n",
" for batch_idx in range(n_batches):\n",
" batch_inputs = inputs[\n",
" batch_idx * max_parallel: (batch_idx + 1) * max_parallel\n",
" ]\n",
" batch_outputs = openai.Completion.create(\n",
" model=model,\n",
" prompt=batch_inputs,\n",
" max_tokens=max_length,\n",
" stop=stop_string,\n",
" temperature=0,\n",
" presence_penalty=presence_penalty\n",
" )\n",
" for completion in batch_outputs.choices:\n",
" outputs.append(completion.text)\n",
" return outputs"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 369,
"outputs": [],
"source": [
"inputs = list(test_papers['prompt'])\n",
"inputs_reduced = inputs"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Completions with original model (non-fine-tuned) with 1 shot example"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 370,
"outputs": [],
"source": [
"# prefix says \"Give the title of the paper that corresponds to the following abstract:\" and then adds an example of abstract with corresponding title,\n",
"# coming from the training set\n",
"prefix = 'Give the title of the paper that corresponds to the following abstract:\\n\\nAbstract:' + train_papers['prompt'][0] + '\\n\\nTitle:' + train_papers['completion'][0] + '\\n\\nAbstract:'\n",
"suffix = '\\n\\nTitle:'"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 371,
"outputs": [],
"source": [
"completions_original_ada = prompt_model(\"ada\", inputs_reduced, prefix=prefix, suffix=suffix, stop_string=\"###\", presence_penalty=1)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 372,
"outputs": [],
"source": [
"completions_original_davinci = prompt_model(\"davinci\", inputs_reduced, prefix=prefix, suffix=suffix, stop_string=\"###\", presence_penalty=1)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 373,
"outputs": [
{
"data": {
"text/plain": "[' A Neural Language Model for Source Code Modeling that Fits the Real-World Constraints of Modern IDEs',\n ' Prompt-based Learning: A Survey']"
},
"execution_count": 373,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"completions_original_davinci[0:2]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Completions with fine-tuned models"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 374,
"outputs": [],
"source": [
"completions_fine_tuned_ada = prompt_model(fine_tuned_ada, inputs_reduced, stop_string=\"###\", presence_penalty=1)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 375,
"outputs": [],
"source": [
"completions_fine_tuned_davinci = prompt_model(fine_tuned_davinci, inputs_reduced, stop_string=\"###\", presence_penalty=1)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 376,
"outputs": [
{
"data": {
"text/plain": "[' Maybe Deep Neural Networks are the Best Choice for Modeling Source Code:\\n An Empirical Study of Models, Architectures, and Data Sets',\n ' Surveying and Organizing Research on Prompt-based Learning for Natural\\n Language Processing']"
},
"execution_count": 376,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"completions_fine_tuned_davinci[0:2]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Need to put all things in the right format for computing the score:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 377,
"outputs": [],
"source": [
"list_true_completions = [[remove_empty_strings(remove_newline(test_papers['completion'].iloc[i][0:-3]).split(' '))] for\n",
" i in range(len(test_papers))]\n",
"#for i in range(2)]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 378,
"outputs": [
{
"data": {
"text/plain": "[[['Sequence',\n 'Model',\n 'Design',\n 'for',\n 'Code',\n 'Completion',\n 'in',\n 'the',\n 'Modern',\n 'IDE']],\n [['Pre-train,',\n 'Prompt,',\n 'and',\n 'Predict:',\n 'A',\n 'Systematic',\n 'Survey',\n 'of',\n 'Prompting',\n 'Methods',\n 'in',\n 'Natural',\n 'Language',\n 'Processing']]]"
},
"execution_count": 378,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_true_completions[0:2]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 379,
"outputs": [],
"source": [
"def create_list_from_generated_completions(generated_completions):\n",
" return [generated_completions[i][1:].split(' ') for i in range(len(generated_completions))]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 380,
"outputs": [],
"source": [
"list_proposed_completions_finetuned_ada = create_list_from_generated_completions(completions_fine_tuned_ada)\n",
"list_proposed_completions_original_ada = create_list_from_generated_completions(completions_original_ada)\n",
"list_proposed_completions_finetuned_davinci = create_list_from_generated_completions(completions_fine_tuned_davinci)\n",
"list_proposed_completions_original_davinci = create_list_from_generated_completions(completions_original_davinci)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Compute scores"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 381,
"outputs": [],
"source": [
"def compute_bleu_rouge_scores(predictions, references):\n",
" bleu_scores = bleu.compute(predictions=predictions,\n",
" references=references)\n",
" rouge_scores = rouge.compute(predictions=predictions,\n",
" references=references)\n",
" return bleu_scores, rouge_scores"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 382,
"outputs": [],
"source": [
"bleu_scores_finetuned_ada, rouge_scores_finetuned_ada = compute_bleu_rouge_scores(predictions=list_proposed_completions_finetuned_ada, references=list_true_completions)\n",
"bleu_scores_original_ada, rouge_scores_original_ada = compute_bleu_rouge_scores(predictions=list_proposed_completions_original_ada, references=list_true_completions)\n",
"bleu_scores_finetuned_davinci, rouge_scores_finetuned_davinci = compute_bleu_rouge_scores(predictions=list_proposed_completions_finetuned_davinci, references=list_true_completions)\n",
"bleu_scores_original_davinci, rouge_scores_original_davinci = compute_bleu_rouge_scores(predictions=list_proposed_completions_original_davinci, references=list_true_completions)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Create table now"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 383,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------------+----------------------+----------------------+----------------------+---------------------+\n",
"| Model | BLEU | ROUGE_1 | ROUGE_2 | ROUGE_L |\n",
"+---------------------+----------------------+----------------------+----------------------+---------------------+\n",
"| Ada (few shots) | 0.009648180077564367 | 0.026550732973533036 | 0.006345504953899718 | 0.02587816010694753 |\n",
"| Ada finetuned | 0.06546804366476248 | 0.35135446679642834 | 0.17007819076705505 | 0.31552534001544624 |\n",
"| Davinci (few shots) | 0.08489227333015674 | 0.4153447628385855 | 0.20034426930573482 | 0.3727169077878785 |\n",
"| Davinci finetuned | 0.07878458584426118 | 0.41066028889523587 | 0.20154668762033787 | 0.36180679031422963 |\n",
"+---------------------+----------------------+----------------------+----------------------+---------------------+\n"
]
}
],
"source": [
"from prettytable import PrettyTable\n",
"\n",
"table = PrettyTable()\n",
"table.field_names = [\"Model\", \"BLEU\", \"ROUGE_1\", \"ROUGE_2\", \"ROUGE_L\"]\n",
"table.add_row([\"Ada (few shots)\", bleu_scores_original_ada[\"bleu\"], rouge_scores_original_ada[\"rouge1\"][1][2],\n",
" rouge_scores_original_ada[\"rouge2\"][1][2], rouge_scores_original_ada[\"rougeL\"][1][2]])\n",
"table.add_row([\"Ada finetuned\", bleu_scores_finetuned_ada[\"bleu\"], rouge_scores_finetuned_ada[\"rouge1\"][1][2],\n",
" rouge_scores_finetuned_ada[\"rouge2\"][1][2], rouge_scores_finetuned_ada[\"rougeL\"][1][2]])\n",
"table.add_row([\"Davinci (few shots)\", bleu_scores_original_davinci[\"bleu\"], rouge_scores_original_davinci[\"rouge1\"][1][2],\n",
" rouge_scores_original_davinci[\"rouge2\"][1][2], rouge_scores_original_davinci[\"rougeL\"][1][2]])\n",
"table.add_row([\"Davinci finetuned\", bleu_scores_finetuned_davinci[\"bleu\"], rouge_scores_finetuned_davinci[\"rouge1\"][1][2],\n",
" rouge_scores_finetuned_davinci[\"rouge2\"][1][2], rouge_scores_finetuned_davinci[\"rougeL\"][1][2]])\n",
"print(table)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Interestingly, fine-tuning Davinci leads to worse performance. That is quite surprising. According to those metrics, Ada finetuned is almost as good as Davinci.\n",
"\n",
"Print some examples:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 384,
"outputs": [],
"source": [
"import random\n",
"def print_example(example_number=None):\n",
" if example_number is None:\n",
" example_number = random.randint(0, len(list_proposed_completions_finetuned_ada))\n",
" print(\"example number\", example_number)\n",
" i = example_number\n",
" # print true completion\n",
" print('True completion: ' + test_papers['completion'].iloc[i][0:-3])\n",
" print('Proposed completions:')\n",
" # completion for ada original\n",
" print('Ada original: ' + completions_original_ada[i])\n",
" # completion for ada finetuned\n",
" print('Ada finetuned: ' + completions_fine_tuned_ada[i])\n",
" # completion for davinci original\n",
" print('Davinci original: ' + completions_original_davinci[i])\n",
" # completion for davinci finetuned\n",
" print('Davinci finetuned: ' + completions_fine_tuned_davinci[i])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 385,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"example number 2\n",
"True completion: Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango\n",
"Proposed completions:\n",
"Ada original: Parameter Re-Initialization through Cyclical Batch Size Schedules\n",
"Ada finetuned: Few-Shot Prompting with Counterfactual Prompting\n",
"Davinci original: Counterfactual Prompting: A Symbiotic Relationship between Text and Patterns in Few-Shot Language Modeling\n",
"Davinci finetuned: Counterfactual Prompting: Understanding the Mechanisms of Few-shot\n",
" Language Model Prompting\n"
]
}
],
"source": [
"print_example()"
],
"metadata": {
"collapsed": false
}
}
]
}
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