{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai import *        # Quick access to most common functionality\n",
    "from fastai.text import *   # Quick access to NLP functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of creating a language model and then transfering to a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/ubuntu/.fastai/data/imdb_sample')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = untar_data(URLs.IMDB_SAMPLE)\n",
    "path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open and view the independent and dependent variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0  0  Un-bleeping-believable! Meg Ryan doesn't even ...\n",
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       "2  0  Every once in a long while a movie will come a...\n",
       "3  1  Name just says it all. I watched this movie wi...\n",
       "4  0  This movie succeeds at being one of the most u..."
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path/'train.csv', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('negative', 'positive')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = read_classes(path/'classes.txt')\n",
    "classes[0], classes[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a `DataBunch` for each of the language model and the classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_lm = TextLMDataBunch.from_csv(path)\n",
    "data_clas = TextClasDataBunch.from_csv(path, vocab=data_lm.train_ds.vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll fine-tune the language model. [fast.ai](http://www.fast.ai/) has a pre-trained English model available that we can download, we jsut have to specify it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      },
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:44\n",
      "epoch  train loss  valid loss  accuracy\n",
      "1      4.921718    4.155864    0.245985  (00:22)\n",
      "2      4.640784    4.090212    0.252899  (00:22)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn = RNNLearner.language_model(data_lm, pretrained_model=URLs.WT103)\n",
    "learn.unfreeze()\n",
    "learn.fit(2, slice(1e-4,1e-2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save our language model's encoder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save_encoder('enc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fine tune it to create a classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:46\n",
      "epoch  train loss  valid loss  accuracy\n",
      "1      0.686322    0.669499    0.635000  (00:15)\n",
      "2      0.651778    0.617119    0.715000  (00:15)\n",
      "3      0.654797    0.593843    0.695000  (00:15)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn = RNNLearner.classifier(data_clas)\n",
    "learn.load_encoder('enc')\n",
    "learn.fit(3, 1e-3)"
   ]
  }
 ],
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