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pszemraj/open-form-q-a-gpt-neo-2-7b.ipynb

Last active March 17, 2023 07:04
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Open-Form Q&A - GPT-Neo 2.7B
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open-form-q-a-gpt-neo-2-7b.ipynb
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"cells": [
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"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/pszemraj/791d72587e718aa90ff2fe79f45b3cfe/open-form-q-a-gpt-neo-2-7b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p2oDvCMLJ47F"
},
"source": [
"# <center> GPT-Neo 2.7B - Open Form Q&A with Colab </center>\n",
"\n",
"Uses EleutherAI's text generation model + huggingface *Transformers* library pipeline feature for an easy-to-use means of working with the model.\n",
"\n",
"---\n",
"\n",
"## about\n",
"\n",
"1. **Model Description**\n",
"\n",
" GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.\n",
"\n",
"2. **Training data**\n",
"\n",
" GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.\n",
"\n",
"3. **Training procedure**\n",
"\n",
" This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.\n",
"\n",
"4. **Intended Use and Limitations**\n",
"\n",
" This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.\n",
"\n",
"## links\n",
"- [link](https://huggingface.co/EleutherAI/gpt-neo-2.7B) to transformers website, [docs](https://huggingface.co/transformers/model_doc/gpt_neo.html) for GPT-Neo model\n",
"- [link](https://github.com/EleutherAI/gpt-neo) to eleutherAI github repo\n",
"- As of 22.06.2021 the model is not yet on transformers, but there is a 6B-parameter version [here](https://6b.eleuther.ai/) for testing\n",
"\n",
"## Note\n",
"\n",
"- <font color=\"salmon\"> *Before running make sure that the Colab Runtime is set to a high-ram GPU. If only standard-memory is available, the notebook will adjust to a downsized model* \n",
"\n",
"---\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uoqm1UBLduKi",
"outputId": "28f110ea-99a4-4bac-9db4-eba0357d223f"
},
"source": [
"!nvidia-smi # shows GPU status - preferably want 12 gb or higher here"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Mon Aug 30 02:16:51 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 470.57.02 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 44C P0 29W / 300W | 0MiB / 16160MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wvN1aZEnlHs4"
},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "30padl1SlLEb"
},
"source": [
"## make colab outputs nice"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xZR9qDLClJ5N"
},
"source": [
"from IPython.display import HTML, display\n",
"def set_css():\n",
" display(HTML('''\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" '''))\n",
"get_ipython().events.register('pre_run_cell', set_css)"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RbCZIEqhlORX"
},
"source": [
"## install libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lIYdn1woOS1n",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"outputId": "c63a0b54-ed13-4a1c-ccf1-f868bc7e7592"
},
"source": [
"%%capture\n",
"!pip install -U transformers\n",
"!pip install clean-text[gpl]\n",
"!pip install GPUtil\n",
"\n",
"from transformers import pipeline\n",
"from cleantext import clean\n",
"import GPUtil\n",
"\n",
"import pprint as pp\n",
"import os, gc"
],
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bo6uVxXV9PlO"
},
"source": [
"## clean-text helper function\n",
"\n",
"fixes a lot of the ```\\n``` outputs and so on generated by the model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "tS6iHswg8h82",
"outputId": "229d4760-bc78-4275-99e0-3c9c3dd41f25"
},
"source": [
"def clean_gpt_out(text, remove_breaks=True):\n",
" cleaned_text = clean(text,\n",
" fix_unicode=True, # fix various unicode errors\n",
" to_ascii=True, # transliterate to closest ASCII representation\n",
" lower=False, # lowercase text\n",
" no_line_breaks=remove_breaks, # fully strip line breaks as opposed to only normalizing them\n",
" no_urls=True, # replace all URLs with a special token\n",
" no_emails=True, # replace all email addresses with a special token\n",
" no_phone_numbers=True, # replace all phone numbers with a special token\n",
" no_numbers=False, # replace all numbers with a special token\n",
" no_digits=False, # replace all digits with a special token\n",
" no_currency_symbols=True, # replace all currency symbols with a special token\n",
" no_punct=False, # remove punctuations\n",
" replace_with_punct=\"\", # instead of removing punctuations you may replace them\n",
" replace_with_url=\"<URL>\",\n",
" replace_with_email=\"<EMAIL>\",\n",
" replace_with_phone_number=\"<PHONE>\",\n",
" replace_with_number=\"<NUMBER>\",\n",
" replace_with_digit=\"0\",\n",
" replace_with_currency_symbol=\"<CUR>\",\n",
" lang=\"en\" # set to 'de' for German special handling\n",
" )\n",
" return cleaned_text\n"
],
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XmQNdjFi1mD6"
},
"source": [
"## load model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pGddMTc2pH8B"
},
"source": [
"### check gpu"
]
},
{
"cell_type": "code",
"metadata": {
"id": "W5PtcaLWpLCR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 360
},
"outputId": "2b36a2e7-e99d-4633-a044-ea814c284685"
},
"source": [
"!nvidia-smi\n",
"# printed device ID is relevant for running on GPU (I.e. 0)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"Mon Aug 30 02:17:04 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 470.57.02 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla V100-SXM2... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 43C P0 26W / 300W | 0MiB / 16160MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "ToZUctzT3Ncu",
"outputId": "11dc48df-3d86-42a9-f240-835372a46a83"
},
"source": [
"import logging \n",
"import numpy as np\n",
"LOGGER = logging.getLogger()\n",
"def gpuname():\n",
" # Returns the model name of the first available GPU\n",
" try:\n",
" gpus = GPUtil.getGPUs()\n",
" except:\n",
" LOGGER.warning(\"Unable to detect GPU model. Is your GPU configured? Is Colab Runtime set to GPU?\")\n",
" return \"UNKNOWN\"\n",
" if len(gpus) == 0:\n",
" raise ValueError(\"No GPUs detected in the system\")\n",
" return gpus[0].name \n",
"\n",
"def gpu_mem_total():\n",
" # Returns the total memory of the first available GPU\n",
" try:\n",
" gpus = GPUtil.getGPUs()\n",
" except:\n",
" LOGGER.warning(\"Unable to detect GPU model. Is your GPU configured? Is Colab Runtime set to GPU?\")\n",
" return np.nan\n",
" if len(gpus) == 0:\n",
" raise ValueError(\"No GPUs detected in the system\")\n",
" return gpus[0].memoryTotal "
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l51tN4l48bCa"
},
"source": [
"get the RAM of the accompanying operating CPU"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5foeWu7N8Z1h",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "8c9beb5e-c18f-4c53-c2b8-fd17d9e2cb79"
},
"source": [
"# Getting all memory using os.popen() cpu_\n",
"cpu_total_memory, cpu_used_memory, cpu_free_memory = map(\n",
" int, os.popen('free -t -m').readlines()[-1].split()[1:])\n",
"\n",
"cpu_RAM_tot = round(cpu_total_memory / 1024, 2)\n",
"print(cpu_RAM_tot, cpu_used_memory, cpu_free_memory)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"51.0 909 37163\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bD3-aCFdaYaW"
},
"source": [
"### load from hf hub\n",
"\n",
"details on how to configure a pipeline are [here](https://huggingface.co/transformers/v3.0.2/main_classes/pipelines.html)\n",
"\n",
"_NOTE: the [official EleutherAI release](https://huggingface.co/EleutherAI/gpt-j-6B) on HF does not seem to work, hence using a duplicate also posted on HF here._"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Oh_TRtuwJtId",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243,
"referenced_widgets": [
"5d0c302aabcb45dbb9123de9f35b19ad",
"dd4953ccb0314d9a81752228f55ea93a",
"9b0e74294d244656b11295fbf65f9d3d",
"5276503db85d4cdda30529c6c3d08707",
"90bfa4b3e70e4a08af45136bcc0550ce",
"56d7a44b8d4a41bfac1bf3dc80a563cc",
"a5c12041a6b540f6b2b51924e1475608",
"f6c452cb5e9a4c6ea8ce2450387f46a8",
"dc5ebcb23bf2446d95f63bf8443c0d09",
"11c837bd007b4670aa939def6f7c12fc",
"da1fa83dc16743528a4e483a3dbe079f",
"2ed42ea47bfc4b41aee5656a1017b60c",
"5c91cb9332a7442da4152ea05e040d0e",
"d71a5d4a6269485d89c2cfe005473640",
"180737df109b472aae785fd7dddaeb7f",
"0f5f1f8303124a36b2d47d86c7d00eac",
"4ce3b90bd0794a2f83dc002d22239faf",
"9ddf683a51044dc6a25e3a1e8861db85",
"b632a26b494940dda7cc6ed9f192d34e",
"5d4c3e5cba284485bf91c94363d1986b",
"931c237c86084f3a8d0f212d6f37d955",
"7e9d7ba8597645cda40c839626fa6b29",
"5aafedbc17294d0d97aa151b792e54c2",
"cfa3211982074aa18c155a31f88a7d52",
"bfef381aecb9470b8b7ad0bb7de5360c",
"8141b12531ba45a8be8c93d958fa688d",
"18975627d9b54ad4b1640795d77b6863",
"2f03e8835fec4e0e93bf20aff388282e",
"4c961a77291143d9bee8a45b8bf94971",
"2a174c9b2aa5473a806c67c62e1e4000",
"ac0e46386d5540a596a28c7eac59ac9f",
"a13d9b6b170246639027493663747315",
"070ba84d1a73498c9eda36b9f1e6439c",
"5bcbc6f07a034ee68fba3968b2cb6630",
"978259ddc33340d782558c79dac7a99d",
"789b45926f49401c826aaae194441762",
"0a7f040e2a3f4582ab27dad105b2acdf",
"cf5ea2fbdd7347fa89dfde9a33850346",
"23211a0e17ad47e899aa41d1c879d642",
"fa81431ce9bd4ae78ddd5853d0eb05bd",
"c4a594144bbf44feacec0a36a2dcdf43",
"d573b92a897f4419aab2a8267be5136f",
"8b582cd3a9414d238b78e3a9ee847e3a",
"da483bc7aa7a4edcba9b244a4b1a6ec6",
"45cd1ca08e1b4362a878448c9fd3e531",
"274ad1b030f5432ea4ff9f5c40d3cf41",
"331f0901cd9e43f586fba1e5d1a12cce",
"3a7198170f7543dd833f04795d1b7b28",
"0263bb6bb15c4fad8ff47fd25a46e1a9",
"bb7684c9f08d45ca95aeb5ab1645d0b8",
"8343ceee0c1f47dcbdaa916116557fd9",
"219c35d721ec48d3974a6e625ed1daea",
"8aaf082dd1dd4940ac8a403683443606",
"9d16aee93e364dc2907b4b96024d4c86",
"96af7277750c4e5b96711e97eae697ec",
"ec66a4e87213457db80f5c55b6b8c8eb",
"6c5b986b95c441f7a680e3f5237a214f",
"783429c1aa744e879892c29435de7440",
"ba30769cd2bf49efb1ba408913bff0af",
"66f8bdaead284299b949715c2ebc30ae",
"70fb0762917c49e6a9b2306c9c939449",
"eb2db31fdd7d4898a8c401d01e6acd62",
"fbb43c2b8e354670be5883471a049ca9",
"608f0eaf6d804b6aa90a919f118b2dac",
"6d3398a47dac4f5885c47009661d39b2",
"e77bbe62e38f4773992d3fbf15a7bc47"
]
},
"outputId": "cf6b472b-c603-40ba-c450-d6d34bbebe35"
},
"source": [
"from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM\n",
"\n",
"\n",
"\n",
"model_6B_pars = \"flyhero/gpt-j-6B\" # see note above\n",
"model_3B_pars = 'EleutherAI/gpt-neo-2.7B'\n",
"model_1B_pars = \"EleutherAI/gpt-neo-1.3B\"\n",
"\n",
"gpu_mem = round(gpu_mem_total() / 1024, 2)\n",
"\n",
"if gpu_mem > 17 and cpu_RAM_tot > 36:\n",
" print(\"using biggest 6B model. GPU - {} GB, CPU-RAM - {} GB\".format(gpu_mem,\n",
" cpu_RAM_tot))\n",
" tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
" # actual_model = AutoModelForCausalLM.from_pretrained(model_6B_pars)\n",
" generator = pipeline('text-generation', model=model_6B_pars, \n",
" tokenizer=tokenizer, device=0) \n",
"\n",
"elif gpu_mem > 14 and cpu_RAM_tot > 16:\n",
" print(\"using medium model. GPU - {} GB, CPU-RAM - {} GB\".format(gpu_mem,\n",
" cpu_RAM_tot))\n",
" actual_model = model_3B_pars\n",
" generator = pipeline('text-generation', model=actual_model, \n",
" device=0) \n",
"else:\n",
" actual_model = model_1B_pars\n",
" print(\"using SMALLER model. GPU - {} GB, CPU-RAM - {} GB\".format(gpu_mem,\n",
" cpu_RAM_tot))\n",
" print(\"using the smaller {} model\".format(actual_model))\n",
" generator = pipeline('text-generation', model=actual_model, \n",
" device=0) \n",
"\n",
"gc.collect()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"using medium model. GPU - 15.78 GB, CPU-RAM - 51.0 GB\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d0c302aabcb45dbb9123de9f35b19ad",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/1.46k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ed42ea47bfc4b41aee5656a1017b60c",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/10.7G [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5aafedbc17294d0d97aa151b792e54c2",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/200 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5bcbc6f07a034ee68fba3968b2cb6630",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/798k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "45cd1ca08e1b4362a878448c9fd3e531",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/456k [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ec66a4e87213457db80f5c55b6b8c8eb",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"Downloading: 0%| | 0.00/90.0 [00:00<?, ?B/s]"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"995"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hBNWkpwqlQim"
},
"source": [
"# Test Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GG-J6gc6cGkC"
},
"source": [
"## baseline test\n",
"\n",
"- while the output is relatively random each time (seed is not fixed), can get a feel for how reasonable the model is.\n",
"- \"*Nikola Tesla, (born July 9/10, 1856, Smiljan, Austrian Empire [now in Croatia]—died January 7, 1943, New York, New York, U.S.), Serbian American inventor and engineer who discovered and patented the rotating magnetic field, the basis of most alternating-current machinery.*\" - [Britannica](https://www.britannica.com/biography/Nikola-Tesla)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RVAL4lgzKHts",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "fc830953-466b-4238-a81a-f272369d3ae2"
},
"source": [
"generator(\"Nikola Tesla was born on\", do_sample=True, min_length=50)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'generated_text': 'Nikola Tesla was born on August 18, 1856 in Kostin, a small village in Macedonia, on the Ottoman Empire. Tesla was the only child in his family. From the beginning of Tesla’s life he developed a passion for'}]"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "spk8MdPF3Z0m"
},
"source": [
"## text completion / story generation \n",
"\n",
"(edit items in the form to customize)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 617
},
"id": "3NWHFu5J2u2j",
"cellView": "form",
"outputId": "fc685d45-b480-4732-ef4b-95b985b68834"
},
"source": [
"prompt1 = \"I opened my eyes, and immediately\" #@param {type:\"string\"}\n",
"response_min_chars = 100#@param {type:\"integer\"}\n",
"response_max_chars = 500#@param {type:\"integer\"}\n",
"import pprint as pp\n",
"response1 = generator(prompt1, do_sample=True, min_length=response_min_chars, \n",
" max_length=response_max_chars,\n",
" clean_up_tokenization_spaces=True,\n",
" return_full_text=True)\n",
"gc.collect()\n",
"print(\"Prompt: \\n\")\n",
"pp.pprint(prompt1)\n",
"print(\"\\nResponse: \\n\")\n",
"out1_dict = response1[0]\n",
"pp.pprint(clean_gpt_out(out1_dict[\"generated_text\"]), compact=True)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"Prompt: \n",
"\n",
"'I opened my eyes, and immediately'\n",
"\n",
"Response: \n",
"\n",
"('I opened my eyes, and immediately a black fog engulfed the room. I sat up in '\n",
" 'bed and saw all the curtains moving around with the wind, and the branches '\n",
" 'and leaves flapping in the wind, making a sound that I had never heard '\n",
" 'before. Everything seemed to stop. The room was completely silent. '\n",
" 'Everything was silent. I heard a slight noise from the bathroom, where I had '\n",
" 'just showered, and heard the sound of running water in the shower, and then '\n",
" 'nothing. I lay down again and fell asleep, but woke up again, just before '\n",
" 'noon. This time I sat up, in bed, and listened, but no one was there. I '\n",
" 'tried to open the curtains to see if anything was there, but the wind had '\n",
" \"carried them away, and I couldn't see anything. I went to the bathroom and \"\n",
" 'looked into the mirror. And there... in the reflection... was a tiny man, '\n",
" \"his head sticking out the bathroom door, and I couldn't find his head \"\n",
" 'anywhere except in the image reflected in the mirror. # Chapter Sixteen I '\n",
" 'woke up with a start, on my back, with my heart pounding loudly, and my '\n",
" 'stomach in a horrible knot. The bathroom door was closed, but I could see '\n",
" 'that it was not latched. I reached over and quickly twisted the handle and '\n",
" 'opened the door, just a crack. The bathroom was empty. I checked the living '\n",
" 'room, the kitchen, the garage. I checked the bedrooms one after the other, '\n",
" 'all empty. It was all quiet, and no sign of anybody. The only thing I could '\n",
" 'think of was whoever it was had been here, but now he must be gone, but I '\n",
" \"couldn't find his head anywhere but in the mirror. I left the bathroom and \"\n",
" 'went back to the bedroom. As I passed the bed, the small man seemed to '\n",
" 'disappear into the darkness. I got up and made the bed. I turned on the '\n",
" 'bedside lamp, turned it up, and put my dresser drawers back in place. I had '\n",
" 'never had a weird dream, but I had heard many, and had certainly seen many '\n",
" 'as well. But this was different, it was like I had dreamed my whole life, '\n",
" 'with different parts in it, all taking place in my bedroom, and my bathroom. '\n",
" 'Why was this guy, and where was he? What was I doing, where was I? What was '\n",
" 'I looking at in the bathroom mirror? As I lay on the')\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RNx-dWkhah-2"
},
"source": [
"## text completion / Q&A \n",
"\n",
"\n",
"(edit items in the form to customize)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 463
},
"id": "2aj6vE4KZB2F",
"cellView": "form",
"outputId": "73ed17ac-0324-4c63-eed2-7f0d1bd18c79"
},
"source": [
"prompt2 = \"the easiest way to become a Swiss citizen as a foreigner is\" #@param {type:\"string\"}\n",
"response_min_chars = 100#@param {type:\"integer\"}\n",
"response_max_chars = 300#@param {type:\"integer\"}\n",
"\n",
"response_2 = generator(prompt2, do_sample=True, min_length=response_min_chars, \n",
" max_length=response_max_chars,\n",
" clean_up_tokenization_spaces=True,\n",
" return_full_text=True)\n",
"gc.collect()\n",
"print(\"Prompt: \\n\")\n",
"pp.pprint(prompt2)\n",
"print(\"\\nResponse: \\n\")\n",
"out2_dict = response_2[0]\n",
"pp.pprint(clean_gpt_out(out2_dict[\"generated_text\"], \n",
" remove_breaks=True), compact=True)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"Prompt: \n",
"\n",
"'the easiest way to become a Swiss citizen as a foreigner is'\n",
"\n",
"Response: \n",
"\n",
"('the easiest way to become a Swiss citizen as a foreigner is just to give '\n",
" 'birth. The Swiss allow non-EU applicants to apply as a single person in '\n",
" 'cases where their marriage does not fulfill all formal requirements, and '\n",
" 'where the applicant is the sole financial breadwinner for their household. '\n",
" 'When applying for the single person classification of citizenship, '\n",
" 'applicants may show that their parents were citizens of Switzerland, at '\n",
" \"least on their parent's birth and death certificates. The Swiss citizenship \"\n",
" 'law has been amended several times since 2003 to allow naturalisation of '\n",
" 'non-EU citizens who are married to a Swiss citizen who was not resident in '\n",
" 'Switzerland at the time of the marriage. In 2019, the Swiss Supreme Court '\n",
" 'confirmed the legality of the process. Non-EU citizens who were born after 1 '\n",
" 'January 2004 may apply for Swiss citizenship as a single person if they were '\n",
" 'in a civil union or an order of divorce or annulment and where there was no '\n",
" 'impediment at birth or since then. The new law also permits the '\n",
" 'naturalisation of those children who were born after 1 July 2005, if their '\n",
" 'parents and grandparents were Swiss citizens. Citizenship by law Citizenship '\n",
" 'by law is a system established by the Swiss Confederation. It is granted to '\n",
" 'people who have demonstrated the consent of the Swiss government to be '\n",
" 'citizens of Switzerland. The Swiss citizenship law grants a citizen the '\n",
" 'right to: be represented on the Swiss Federal Council (Conseil de la Confed')\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSEMVtZseH8r"
},
"source": [
"## direct Q&A \n",
"\n",
"(edit items in the form to customize)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 651
},
"id": "_ScuC8SMeMMs",
"cellView": "form",
"outputId": "8cff6b14-a1cf-47b5-9bd5-659708a77fc6"
},
"source": [
"prompt3 = \"question: what is the meaning of life?\" #@param {type:\"string\"}\n",
"response_min_chars = 100#@param {type:\"integer\"}\n",
"response_max_chars = 500#@param {type:\"integer\"}\n",
"\n",
"response_3 = generator(prompt3, do_sample=True, min_length=response_min_chars, \n",
" max_length=response_max_chars,\n",
" clean_up_tokenization_spaces=True,\n",
" return_full_text=True)\n",
"gc.collect()\n",
"print(\"Prompt: \\n\")\n",
"pp.pprint(prompt3)\n",
"print(\"\\nResponse: \\n\")\n",
"out3_dict = response_3[0]\n",
"pp.pprint(clean_gpt_out(out3_dict[\"generated_text\"], \n",
" remove_breaks=True), compact=True)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"Prompt: \n",
"\n",
"'question: what is the meaning of life?'\n",
"\n",
"Response: \n",
"\n",
"('question: what is the meaning of life? how does it evolve? what is the '\n",
" 'meaning of death? the answers to these questions are really very difficult '\n",
" \"questions, I know. i also know that there's lot of theories that explain the \"\n",
" 'meaning of life. for example, according to what i know in the last 25 years, '\n",
" 'what we know now is that life is a very short period of time. this means '\n",
" 'that life really is very short and it really is very easy to be trapped in '\n",
" 'our life. this is so because we are so much dependent on our surroundings. '\n",
" 'according to the science of life, life is a product of life. we can use the '\n",
" 'science of life to define our purpose in life. we can also use the science '\n",
" 'of life to redefine our purpose in life. so here are the answers to your '\n",
" 'question, what the meaning of life is, life is a very short period of time, '\n",
" 'and it really is very easy to be trapped inside your life. we are so much '\n",
" 'dependent on our surroundings. this is so because we are so much dependent '\n",
" 'on our surroundings and our mind is so much dependent on the surroundings. '\n",
" 'so according to the science of life, life is a product of life, so according '\n",
" 'to the science of life, we are not trapped in our life. we are not prisoners '\n",
" 'to our life. we are not trapped to our life in prison. we are not prisoners '\n",
" 'of our mind. we are not prisoners to our mind. questions: what is the '\n",
" 'meaning of life? how does it evolve? what is the meaning of death? the '\n",
" 'answers to these questions are really very difficult questions, i know. i '\n",
" \"also know that there's lot of theories that explain the meaning of life. for \"\n",
" 'example, according to what i know in the last 25 years, what we know now is '\n",
" 'that life is a very short period of time. this means that life really is '\n",
" 'very short and it really is very easy to be trapped in our life. this is so '\n",
" 'because we are so much dependent on our surroundings. according to the '\n",
" 'science of life, life is a product of life. we can use the science of life '\n",
" 'to define our purpose in life. we can also use the science of life to '\n",
" 'redefine our purpose in life. so here are the answers to your question, what '\n",
" 'the meaning of life is, life is a very short period of time, and it really '\n",
" 'is very easy to be trapped inside your life. we are so much dependent on our '\n",
" 'surroundings')\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "20uu6fujefgo"
},
"source": [
"## idea generation \n",
"\n",
"(edit items in the form to customize)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 617
},
"id": "QQGUp_s1evrj",
"cellView": "form",
"outputId": "f4af30b9-2c01-4bda-e846-6939a4773016"
},
"source": [
"prompt4 = \"ideas for an app that predicts where new construction sites will be built:\" #@param {type:\"string\"}\n",
"response_min_chars = 100#@param {type:\"integer\"}\n",
"response_max_chars = 500#@param {type:\"integer\"}\n",
"\n",
"response_4 = generator(prompt4, do_sample=True, min_length=response_min_chars, \n",
" max_length=response_max_chars,\n",
" clean_up_tokenization_spaces=True,\n",
" return_full_text=True)\n",
"gc.collect()\n",
"print(\"Prompt: \\n\")\n",
"pp.pprint(prompt4)\n",
"print(\"\\nResponse: \\n\")\n",
"out4_dict = response_4[0]\n",
"pp.pprint(clean_gpt_out(out4_dict[\"generated_text\"], \n",
" remove_breaks=True), compact=True)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
" <style>\n",
" pre {\n",
" white-space: pre-wrap;\n",
" }\n",
" </style>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"text": [
"Prompt: \n",
"\n",
"'ideas for an app that predicts where new construction sites will be built:'\n",
"\n",
"Response: \n",
"\n",
"('ideas for an app that predicts where new construction sites will be built: '\n",
" \"_I'm excited to see what this app gives me. I wish it could have told me \"\n",
" 'whether a site would be better for the community or a better place for new '\n",
" 'residents._ **_Example question_** _This question is open-ended, allowing '\n",
" 'you to select a topic that may not even apply to your app. This question is '\n",
" \"about how your app would feel when it's trying to predict a city's housing \"\n",
" \"market. You may use this question to develop your app's capabilities._ \"\n",
" '**_Example answer_** _This question is a question about your app using '\n",
" 'current housing market data to predict future housing market activity._ '\n",
" '**_Example discussion_** _This discussion is an open discussion on a '\n",
" 'question that your app may or may not have answered._ **_Example decision '\n",
" 'tree_** _Here is a decision tree that you could have followed through your '\n",
" \"app's predictions. This tree would be about the predicted housing market in \"\n",
" 'the city of Chicago. You could have used this app to predict this housing '\n",
" 'market._ **Q:** | This question is open-ended, allowing you to select a '\n",
" 'topic that may not even apply to your app. This question is about how your '\n",
" \"app would feel when it's trying to predict the effects of a tax change on \"\n",
" \"the city of Chicago. You may use this question to develop your app's \"\n",
" 'capabilities. The example below is about a decision that your app could have '\n",
" 'made about the impact of the proposed tax reform in the city of Chicago. '\n",
" \"---|--- **A:** | [**Housing Tax** | **Impact** ] **Q:** | It's also possible \"\n",
" \"to use this question to develop your app's capabilities. This question could \"\n",
" \"be used to test your app's capabilities to predict the effects of a local \"\n",
" 'tax change. **A:** | [**Tax Change** | **Effect** ] **Q:** | In the '\n",
" 'discussion below, you could have used this question to discuss the effects '\n",
" 'of the proposed tax reform on housing in the city of Chicago. ---|--- **A:** '\n",
" '| [**Tax Reform** | **Impact** ] **_Example discussion_** _This discussion '\n",
" 'is an open discussion on the impact of the proposed tax reform in the city '\n",
" \"of Chicago. It's likely a discussion on an\")\n"
],
"name": "stdout"
}
]
}
]
}
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