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@kernelmachine
kernelmachine committed Jul 8, 2019
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367 changes: 199 additions & 168 deletions colab/VAMPIRE_AGNews.ipynb
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@@ -1,169 +1,200 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "VAMPIRE_AGNews.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/allenai/vampire/blob/colab/colab/VAMPIRE_AGNews.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wEFivVCCpEUn",
"colab_type": "code",
"colab": {}
},
"source": [
"!git clone https://github.com/allenai/vampire\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rMDq7NeCrHB3",
"colab_type": "code",
"colab": {}
},
"source": [
"%cd vampire"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "f52p11vJpJW8",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip install -r requirements.txt"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JXC-bTeIptAF",
"colab_type": "code",
"colab": {}
},
"source": [
"!python -m spacy download en"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mTii7MynrbLn",
"colab_type": "code",
"colab": {}
},
"source": [
"!SEED=42 python -m pytest -v --color=yes vampire"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_pHcbntzqHuV",
"colab_type": "code",
"colab": {}
},
"source": [
"!sh scripts/download_ag.sh"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zSVpbMX1rQl7",
"colab_type": "code",
"colab": {}
},
"source": [
"!curl -Lo ag.tar https://s3-us-west-2.amazonaws.com/allennlp/datasets/ag-news/vampire_preprocessed_example.tar\n",
"!tar -xvf ag.tar -C examples/\n",
"!rm ag.tar"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "d_oLBgaYtKsL",
"colab_type": "code",
"colab": {}
},
"source": [
"!DATA_DIR=\"$(pwd)/examples/ag\" VOCAB_SIZE=30000 LAZY=1 python -m scripts.train \\\n",
" --config training_config/vampire.jsonnet \\\n",
" --serialization-dir model_logs/vampire \\\n",
" --environment VAMPIRE \\\n",
" --device 0 -o"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "D0T6rYRLtTU_",
"colab_type": "code",
"colab": {}
},
"source": [
"!DATA_DIR=\"$(pwd)/examples/ag\" VAMPIRE_DIR=\"$(pwd)/model_logs/vampire\" VAMPIRE_DIM=81 THROTTLE=200 EVALUATE_ON_TEST=0 python -m scripts.train \\\n",
" --config training_config/classifier.jsonnet \\\n",
" --serialization-dir model_logs/clf \\\n",
" --environment CLASSIFIER \\\n",
" --device 0"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5H6K6A_S8fZs",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/allenai/vampire/blob/colab/colab/VAMPIRE_AGNews.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VAMPIRE Example: AG News Corpus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we run through the example in the README. Since VAMPIRE is a low resource method, it can be run on the CPU or Colab GPU. Before starting, open this notebook in a Colab environment, and connect to a GPU instance."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clone the repository and cd into working directory:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "wEFivVCCpEUn"
},
"outputs": [],
"source": [
"!git clone https://github.com/allenai/vampire\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "rMDq7NeCrHB3"
},
"outputs": [],
"source": [
"%cd vampire"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Install requirements:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "f52p11vJpJW8"
},
"outputs": [],
"source": [
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "JXC-bTeIptAF"
},
"outputs": [],
"source": [
"!python -m spacy download en"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All tests should pass:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "mTii7MynrbLn"
},
"outputs": [],
"source": [
"!SEED=42 python -m pytest -v --color=yes vampire"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download preprocessed AG News corpus, ready to run with VAMPIRE:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "zSVpbMX1rQl7"
},
"outputs": [],
"source": [
"!curl -Lo ag.tar https://s3-us-west-2.amazonaws.com/allennlp/datasets/ag-news/vampire_preprocessed_example.tar\n",
"!tar -xvf ag.tar -C examples/\n",
"!rm ag.tar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run VAMPIRE:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "d_oLBgaYtKsL"
},
"outputs": [],
"source": [
"!DATA_DIR=\"$(pwd)/examples/ag\" VOCAB_SIZE=30000 LAZY=1 python -m scripts.train \\\n",
" --config training_config/vampire.jsonnet \\\n",
" --serialization-dir model_logs/vampire \\\n",
" --environment VAMPIRE \\\n",
" --device 0 -o"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After VAMPIRE has trained, we can run a downstream classifier on the AG News corpus with just 200 examples. We report an average of 83.9% accuracy in the paper over five seeds under this setting:"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "D0T6rYRLtTU_"
},
"outputs": [],
"source": [
"!DATA_DIR=\"$(pwd)/examples/ag\" VAMPIRE_DIR=\"$(pwd)/model_logs/vampire\" VAMPIRE_DIM=81 THROTTLE=200 EVALUATE_ON_TEST=0 python -m scripts.train \\\n",
" --config training_config/classifier.jsonnet \\\n",
" --serialization-dir model_logs/clf \\\n",
" --environment CLASSIFIER \\\n",
" --device 0"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"include_colab_link": true,
"name": "VAMPIRE_AGNews.ipynb",
"provenance": [],
"version": "0.3.2"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

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