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Using GPT-2 Horror Stories Model
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using-gpt-2-horror-stories-model.ipynb
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"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
}
}
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/iam-abbas/f13433374e780d194f9dfaeb44162b1d/using-gpt-2-horror-stories-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eO1hXmwbt75y"
},
"source": [
"## Usage of `abbas/gpt-horror-stories` model on huggingface."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nry5KMAruDrI"
},
"source": [
"## Installing transformers"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ucic3GTwai7R",
"outputId": "12dc40c7-29b7-46be-da48-441c93308596"
},
"source": [
"!pip install transformers"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting transformers\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/50/0c/7d5950fcd80b029be0a8891727ba21e0cd27692c407c51261c3c921f6da3/transformers-4.1.1-py3-none-any.whl (1.5MB)\n",
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"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
"Collecting tokenizers==0.9.4\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
"\u001b[K |████████████████████████████████| 2.9MB 51.5MB/s \n",
"\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.8)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
"Collecting sacremoses\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
"\u001b[K |████████████████████████████████| 890kB 50.8MB/s \n",
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"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\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->transformers) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\n",
"Building wheels for collected packages: sacremoses\n",
" Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=7ddf65a991a0bf19b646d180e58a7d065a5ee8f35b3d53dae2d1d78c0bca4182\n",
" Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
"Successfully built sacremoses\n",
"Installing collected packages: tokenizers, sacremoses, transformers\n",
"Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.1.1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HhR7n_LeuGpV"
},
"source": [
"## Importing all the required libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bD1psnjxagWf"
},
"source": [
"from transformers import AutoTokenizer, AutoModelWithLMHead\r\n",
"import torch\r\n",
"import numpy as np"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "wBr8dCqbuL4q"
},
"source": [
"## Using cuda if available else cpu"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JMwzTzAJfLDm"
},
"source": [
"device = 'cpu'\r\n",
"if torch.cuda.is_available():\r\n",
" device = 'cuda'"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "jHq9HQ2-uQFt"
},
"source": [
"## Downloading the model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 383,
"referenced_widgets": [
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"7fff7bab70b2457d85346a54686799c3",
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"fe27ed397c3d4faaa0675d9087e5c39e",
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"0d8f2094f31440d090836fe3577bbae4",
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"834f6a0b023c4fb98c586d745fb022f5",
"11b2d400de7741af8e845f5f89b1096a",
"f49671b5ba554ce199d487b5d8d9b79e",
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},
"id": "lGnd3lJxcSfn",
"outputId": "245b9337-204d-403e-9907-2abe3ac4a63e"
},
"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"abbas/gpt2-horror-stories\")\r\n",
"model = AutoModelWithLMHead.from_pretrained(\"abbas/gpt2-horror-stories\")\r\n",
"model.to(device)\r\n",
"device"
],
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "37bee902ecd7447f9f0559588f2629ef",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=768.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d6187baa13d422f8c33dd0811ea54a3",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=798156.0, style=ProgressStyle(descripti…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ecab461591364384a8c1fb896c046561",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456356.0, style=ProgressStyle(descripti…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d64722a6c44b4f768b3069c5e0d5335e",
"version_minor": 0,
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"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=90.0, style=ProgressStyle(description_w…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2af4ab5ebfc420eb464a7001faae69f",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=167.0, style=ProgressStyle(description_…"
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"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/models/auto/modeling_auto.py:890: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
" FutureWarning,\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d2f8e393d43849b8999b8b0d6b52da16",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=510406559.0, style=ProgressStyle(descri…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'cuda'"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dfDtcBcPuTQs"
},
"source": [
"## Function to generate samples"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Jt8echeGaozd"
},
"source": [
"def choose_from_top(probs, n=10):\r\n",
" ind = np.argpartition(probs, -n)[-n:]\r\n",
" top_prob = probs[ind]\r\n",
" top_prob = top_prob / np.sum(top_prob)\r\n",
" choice = np.random.choice(n, 1, p = top_prob)\r\n",
" token_id = ind[choice][0]\r\n",
" return int(token_id)\r\n",
"\r\n",
"def generate_text(input_str, text_len = 100):\r\n",
" cur_ids = torch.tensor(tokenizer.encode(input_str)).unsqueeze(0).long().to(device)\r\n",
" model.eval()\r\n",
" with torch.no_grad():\r\n",
" for i in range(text_len):\r\n",
" outputs = model(cur_ids, labels=cur_ids)\r\n",
" loss, logits = outputs[:2]\r\n",
" softmax_logits = torch.softmax(logits[0,-1], dim=0) \r\n",
" next_token_id = choose_from_top(softmax_logits.to('cpu').numpy(), n=10)\r\n",
" cur_ids = torch.cat([cur_ids, torch.ones((1,1)).long().to(device) * next_token_id], dim = 1) # Add the last word\r\n",
" output_list = list(cur_ids.squeeze().to('cpu').numpy())\r\n",
" output_text = tokenizer.decode(output_list)\r\n",
" output_text = output_text.replace('<|endoftext|>', ' ')\r\n",
" print(output_text)"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "4RY5zvA8uWNv"
},
"source": [
"## Generating Samples"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "se-wcWi8b6y3",
"outputId": "d1c871df-584a-4d9a-a5e4-4a8ca33c201b"
},
"source": [
"generate_text(\"It was a scary\", 200)"
],
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": [
"It was a scary night. We heard a loud crash and the sound of a heavy metal door slamming behind us. We ran, I saw my brother standing right in the doorway of my room at the time. He looked like he was frozen in place, and his skin was pale. I saw my brother, standing there. “What’s wrong?” I asked him. My brother’s voice cracked open and he said something in a very low voice, “We’re going home. I’m going to get you some rest.” His words were almost a whisper, like the sound of a person being held prisoner, with no voice, no memory, and no reason to talk, no one to speak to. We were alone, and we were in the house, I could feel the door swing open and there we were. I saw my brothers face. I was in shock, I couldn’t believe what I’\n"
],
"name": "stdout"
}
]
}
]
}
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