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brockmanmatt/getendword.ipynb

Last active July 25, 2020 00:14
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getendword.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "getEndWord.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyOwQ3+oWBfnNgTmElALCiFO",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/brockmanmatt/28d77f39ed4e115b07a78d36696d9c28/getendword.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "J7wnsgT2kPut",
"colab_type": "code",
"colab": {
"resources": {
"http://localhost:8080/nbextensions/google.colab/files.js": {
"data": 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"ok": true,
"headers": [
[
"content-type",
"application/javascript"
]
],
"status": 200,
"status_text": ""
}
},
"base_uri": "https://localhost:8080/",
"height": 89
},
"outputId": "844c3756-9ea6-4eb5-fe8a-6d59ba885004"
},
"source": [
"from google.colab import files\n",
"uploaded = files.upload()\n",
"print(\"done\")"
],
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <input type=\"file\" id=\"files-ef88ad22-2f41-4706-9989-9e696beb8b9b\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-ef88ad22-2f41-4706-9989-9e696beb8b9b\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script src=\"/nbextensions/google.colab/files.js\"></script> "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Saving key.json to key.json\n",
"done\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WHPHrUnhpKnI",
"colab_type": "text"
},
"source": [
"I'll install the API"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zq0ltp2xn4yt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
},
"outputId": "5d3155d7-b149-46fe-c83b-afea1aeccdad"
},
"source": [
"!pip install openai\n",
"import openai, json, pandas as pd"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting openai\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
"\r\u001b[K |██ | 10kB 16.7MB/s eta 0:00:01\r\u001b[K |████▏ | 20kB 1.8MB/s eta 0:00:01\r\u001b[K |██████▎ | 30kB 2.1MB/s eta 0:00:01\r\u001b[K |████████▍ | 40kB 2.4MB/s eta 0:00:01\r\u001b[K |██████████▍ | 51kB 2.1MB/s eta 0:00:01\r\u001b[K |████████████▌ | 61kB 2.3MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 71kB 2.5MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 81kB 2.4MB/s eta 0:00:01\r\u001b[K |██████████████████▊ | 92kB 2.6MB/s eta 0:00:01\r\u001b[K |████████████████████▉ | 102kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 112kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████ | 122kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 133kB 2.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▏ | 143kB 2.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▎| 153kB 2.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 163kB 2.8MB/s \n",
"\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
"Building wheels for collected packages: openai\n",
" Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170709 sha256=6a4b916b5fb7a2d71c81914a1d166bb4bc6c5bebfd08e00e5f688f6583e798e5\n",
" Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
"Successfully built openai\n",
"Installing collected packages: openai\n",
"Successfully installed openai-0.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q2yE0jcnpMEV",
"colab_type": "text"
},
"source": [
"Loading in key.json that I uploaded; I do this so I don't need to worry about accidently leaking creds if I share the colab (which I'm 99% sure is just a json file that won't expose them)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bwNXXwHen5x9",
"colab_type": "code",
"colab": {}
},
"source": [
"openai.api_key = json.load(open(\"key.json\", \"r\"))[\"key\"]"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "k67w5H0fpTkT",
"colab_type": "text"
},
"source": [
"Default keyword arguments to pass the aPI"
]
},
{
"cell_type": "code",
"metadata": {
"id": "e1EwpqqJkTYh",
"colab_type": "code",
"colab": {}
},
"source": [
"#arguments to send the API\n",
"kwargs = { \"engine\":\"davinci\", \"temperature\":0, \"max_tokens\":150, \"stop\":\"\\n\\n\", }\n"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zZubgPoOpWDH",
"colab_type": "text"
},
"source": [
"Quick wrapper to automatically save prompts and responses sent for later analysis if needed"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sXTDJx0An9Bl",
"colab_type": "code",
"colab": {}
},
"source": [
"import datetime\n",
"def query(prompt, myKwargs = kwargs):\n",
" \"\"\"\n",
" wrapper for the API to save the prompt and the result\n",
" \"\"\"\n",
"\n",
" r = openai.Completion.create(prompt=prompt, **myKwargs)[\"choices\"][0][\"text\"].strip()\n",
" with open(\"{}.json\".format(datetime.datetime.now().strftime(\"%Y%m%d%s\")), \"w\") as fh:\n",
" json.dump({\"prompt\":prompt, \"response\":r}, fh, indent=4)\n",
" return r"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EdFXafcJpZ3Q",
"colab_type": "text"
},
"source": [
"Test to make sure my query works"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4SlyKgjyopPn",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "36e476a5-7c91-4f83-af00-24662b254086"
},
"source": [
"newKwargs = kwargs.copy()\n",
"newKwargs[\"stop\"] = \"\\n\"\n",
"query(\"q: what is 1+1?\\na:\", newKwargs)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2'"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fMlOnoR2SMFd",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
},
"outputId": "f65138f7-e179-42fd-97b4-909750be7dfc"
},
"source": [
"prompt = \"\"\"\"Task: Rewrite the following statement so it ends with '{}'.\n",
"Statement: {}\n",
"Rewritten:\"\"\"\n",
"tests = [[\"I bought a black cat\", \"black\"], [\"I bought a black cat\", \"small\"], [\"I bought a black cat\", \"feline\"], [\"I bought a black cat\", \"dog\"], [\"I bought a black cat\", \"sold\"] ]\n",
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"\\n\"\n",
"for test in tests:\n",
" myPrompt = prompt.format(test[1], test[0])\n",
" r = openai.Completion.create(prompt=myPrompt, **myKwargs)[\"choices\"][0][\"text\"].strip()\n",
" print(test)\n",
" print(r)\n"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"['I bought a black cat', 'black']\n",
"I bought a black cat\n",
"['I bought a black cat', 'small']\n",
"I bought a black cat, which was small.\n",
"['I bought a black cat', 'feline']\n",
"I bought a black feline\n",
"['I bought a black cat', 'dog']\n",
"I bought a black dog\n",
"['I bought a black cat', 'sold']\n",
"I sold a black cat\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fiOcgnn2SaSf",
"colab_type": "text"
},
"source": [
"improved! increase temperature until word at end"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nwlVG4jPSZzz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 833
},
"outputId": "51e485a7-f2ca-49a4-ab90-51d54d4691eb"
},
"source": [
"import numpy as np, re\n",
"prompt = \"\"\"\"Task: Rewrite the following statement so it ends with '{}'.\n",
"Statement: {}\n",
"Rewritten:\"\"\"\n",
"tests = [[\"I bought a black cat\", \"black\"], [\"I bought a black cat\", \"small\"], [\"I bought a black cat\", \"feline\"], [\"I bought a black cat\", \"dog\"], [\"I bought a black cat\", \"sold\"] ]\n",
"myKwargs = kwargs.copy()\n",
"myKwargs[\"stop\"] = \"\\n\"\n",
"for test in tests:\n",
" myKwargs = kwargs.copy()\n",
" myKwargs[\"stop\"] = \"\\n\"\n",
" myKwargs[\"temperature\"] = 0\n",
" for i in range(10): #try 10 times, increasing temperature by .1 each time\n",
" myPrompt = prompt.format(test[1], test[0])\n",
" r = openai.Completion.create(prompt=myPrompt, **myKwargs)[\"choices\"][0][\"text\"].strip()\n",
" print(\"Temp {}: {}\".format(myKwargs[\"temperature\"], test))\n",
" print(r)\n",
" if re.sub('[^a-zA-Z]+', '', r).endswith(test[1]):\n",
" print(\"*****\\nMATCH\\n*****\")\n",
" break\n",
" myKwargs[\"temperature\"] = np.round(.1 * (i+1), 2)\n"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"Temp 0: ['I bought a black cat', 'black']\n",
"I bought a black cat\n",
"Temp 0.1: ['I bought a black cat', 'black']\n",
"I bought a black cat\n",
"Temp 0.2: ['I bought a black cat', 'black']\n",
"I bought a black cat\n",
"Temp 0.3: ['I bought a black cat', 'black']\n",
"I bought a black cat\n",
"Temp 0.4: ['I bought a black cat', 'black']\n",
"I bought a black cat because it was black\n",
"*****\n",
"MATCH\n",
"*****\n",
"Temp 0: ['I bought a black cat', 'small']\n",
"I bought a black cat, which was small.\n",
"*****\n",
"MATCH\n",
"*****\n",
"Temp 0: ['I bought a black cat', 'feline']\n",
"I bought a black feline\n",
"*****\n",
"MATCH\n",
"*****\n",
"Temp 0: ['I bought a black cat', 'dog']\n",
"I bought a black dog\n",
"*****\n",
"MATCH\n",
"*****\n",
"Temp 0: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.1: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.2: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.3: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.4: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.5: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.6: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.7: ['I bought a black cat', 'sold']\n",
"A black cat was sold to me by a stranger.\"\n",
"Temp 0.8: ['I bought a black cat', 'sold']\n",
"I sold a black cat\n",
"Temp 0.9: ['I bought a black cat', 'sold']\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iqLeyvtcSN11",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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