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‎fastai_py.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "fastai_py.ipynb",
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"version": "0.3.2",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"id": "hGILgIBehFOz",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import fastai"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "LuK4B1zThJze",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"from fastai.text import * "
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0sdYbjfvhXNW",
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"colab_type": "code",
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"outputId": "6046aa28-e752-4342-dd7e-9dc455b10bdf",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"fastai.text.annealing_no"
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],
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"execution_count": 7,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<function fastai.callback.annealing_no>"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 7
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bWdN1Y-uiBZd",
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"colab_type": "code",
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"outputId": "51b91942-46ee-41d3-a9e8-85f74719cb16",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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}
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},
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"source": [
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"path = untar_data(URLs.IMDB_SAMPLE)\n",
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"path"
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],
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"PosixPath('/root/.fastai/data/imdb_sample')"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 8
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "5NYLx5gziR54",
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"colab_type": "code",
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"outputId": "14096442-f8c1-40b5-df31-16ddf0396da6",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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}
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},
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"source": [
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"df = pd.read_csv(path/'texts.csv')\n",
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"df.head()"
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],
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>label</th>\n",
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" <th>text</th>\n",
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" <th>is_valid</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>negative</td>\n",
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" <td>Un-bleeping-believable! Meg Ryan doesn't even ...</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>positive</td>\n",
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" <td>This is a extremely well-made film. The acting...</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>negative</td>\n",
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" <td>Every once in a long while a movie will come a...</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>positive</td>\n",
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" <td>Name just says it all. I watched this movie wi...</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>negative</td>\n",
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" <td>This movie succeeds at being one of the most u...</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" label text is_valid\n",
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"0 negative Un-bleeping-believable! Meg Ryan doesn't even ... False\n",
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"1 positive This is a extremely well-made film. The acting... False\n",
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"2 negative Every once in a long while a movie will come a... False\n",
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"3 positive Name just says it all. I watched this movie wi... False\n",
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"4 negative This movie succeeds at being one of the most u... False"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 9
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "4KFlG9RLilnm",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Language model data\n",
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"data_lm = TextLMDataBunch.from_csv(path, 'texts.csv')\n",
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"# Classifier model data\n",
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"data_clas = TextClasDataBunch.from_csv(path, 'texts.csv', vocab=data_lm.train_ds.vocab, bs=32)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "1TPi_DfTio26",
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"outputId": "11b99a8e-ac0c-4b16-d7ac-4ce7bb8fb865",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 80
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}
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},
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"source": [
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"learn = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.5)\n",
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"learn.fit_one_cycle(1, 1e-2)"
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],
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: left;\">\n",
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" <th>epoch</th>\n",
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" <th>train_loss</th>\n",
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" <th>valid_loss</th>\n",
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" <th>accuracy</th>\n",
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" <th>time</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>4.302421</td>\n",
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" <td>3.922743</td>\n",
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" <td>0.285108</td>\n",
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" <td>00:10</td>\n",
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"text/plain": [
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{
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"cell_type": "code",
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"id": "45AIvHEDjBdT",
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},
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"outputId": "9526f459-7cfe-4441-d97d-c3c4d301d515"
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},
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"source": [
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"learn.predict(\"Life\", 50)"
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],
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"execution_count": 15,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'Life : The comic book of Michael Jackson u II ? ( 1949 ) , Dream Days in the Woods Hotel , a core movie sequence , bears their names , their names in their respective names : A. G.'"
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]
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},
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"metadata": {
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"execution_count": 15
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"source": [
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""
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],
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"execution_count": 0,
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