apply_bart_labels.ipynb

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     "name": "stdout",
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     "text": [
      "Mounted at /content/drive\n",
      "/content/drive/MyDrive/nyu-stuff/2023-fall/DS-UA 301 Advanced DS/final_project\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "\n",
    "drive.mount(\"/content/drive\")\n",
    "\n",
    "%cd /content/drive/MyDrive/nyu-stuff/2023-fall/DS-UA\\ 301\\ Advanced\\ DS/final_project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install -r requirements.txt"
   ]
  },
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     "text": [
      "[nltk_data] Error loading averaged_perceptron_tagger: <urlopen error\n",
      "[nltk_data]     [Errno 8] nodename nor servname provided, or not\n",
      "[nltk_data]     known>\n",
      "[nltk_data] Error loading universal_tagset: <urlopen error [Errno 8]\n",
      "[nltk_data]     nodename nor servname provided, or not known>\n",
      "[nltk_data] Error loading stopwords: <urlopen error [Errno 8] nodename\n",
      "[nltk_data]     nor servname provided, or not known>\n",
      "[nltk_data] Error loading punkt: <urlopen error [Errno 8] nodename nor\n",
      "[nltk_data]     servname provided, or not known>\n"
     ]
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   "source": [
    "import pandas\n",
    "import numpy\n",
    "import json5\n",
    "import functools\n",
    "import json\n",
    "from matplotlib import pyplot\n",
    "from typing import Optional, Any, Coroutine, Union, Dict, List\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "import contractions\n",
    "import torch\n",
    "\n",
    "nltk.download(\"averaged_perceptron_tagger\")\n",
    "nltk.download(\"universal_tagset\")\n",
    "nltk.download(\"stopwords\")\n",
    "nltk.download(\"punkt\")"
   ]
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      "text/plain": [
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   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "classifier = pipeline(\n",
    "    \"zero-shot-classification\", model=\"facebook/bart-large-mnli\", device_map=\"auto\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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      "displayName": "Patrick Liao (LBYPatrick)",
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    "id": "-8BvXAvt4Ike"
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
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    },
    "executionInfo": {
     "elapsed": 3065,
     "status": "ok",
     "timestamp": 1699328691436,
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      "displayName": "Patrick Liao (LBYPatrick)",
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    },
    "id": "WLbc3xCEgTGk",
    "outputId": "faad5188-9d4b-4b74-dfe7-8691aafada6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sequence': 'one fine day I will see the world',\n",
       " 'labels': ['travel', 'dancing', 'cooking'],\n",
       " 'scores': [0.9946518540382385, 0.003005197737365961, 0.0023429866414517164]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_to_classify = \"one fine day I will see the world\"\n",
    "candidate_labels = [\"travel\", \"cooking\", \"dancing\"]\n",
    "classifier(sequence_to_classify, candidate_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 809,
     "status": "ok",
     "timestamp": 1699328692241,
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      "displayName": "Patrick Liao (LBYPatrick)",
      "userId": "03016135564925096917"
     },
     "user_tz": 300
    },
    "id": "ysdhPVefhZgy"
   },
   "outputs": [],
   "source": [
    "df_comments = pandas.read_csv(\"data/balanced_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2028888,
     "status": "ok",
     "timestamp": 1699337080055,
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      "displayName": "Patrick Liao (LBYPatrick)",
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    },
    "id": "mHAyQHRBAkMT",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: 8 / 98"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1101: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: 98 / 98\n",
      "All Done.\n"
     ]
    }
   ],
   "source": [
    "classes = [\"good\", \"decent\", \"mediocre\", \"bad\", \"horrible\"]\n",
    "\n",
    "review_texts = df_comments[\"review_text\"].tolist()\n",
    "\n",
    "label_rows = []\n",
    "n_reviews = len(review_texts)\n",
    "\n",
    "from itertools import islice\n",
    "\n",
    "\n",
    "def chunk(it, size):\n",
    "    it = iter(it)\n",
    "    return iter(lambda: tuple(islice(it, size)), ())\n",
    "\n",
    "\n",
    "sz_batch = 512\n",
    "\n",
    "batches = list(chunk(review_texts, sz_batch))\n",
    "n_batches = len(batches)\n",
    "\n",
    "for idx, batch in enumerate(batches):\n",
    "    print(f\"\\rPROGRESS: {idx + 1} / {n_batches}\", end=\"\")\n",
    "\n",
    "    results = classifier(list(batch), classes)\n",
    "\n",
    "    for r in results:\n",
    "        row = [r[\"sequence\"]]\n",
    "        for c in classes:\n",
    "            row.append(r[\"scores\"][r[\"labels\"].index(c)])\n",
    "\n",
    "        label_rows.append(row)\n",
    "\n",
    "df_labels = pandas.DataFrame(label_rows, columns=[\"review_text\"] + classes)\n",
    "df_labels.to_csv(\"data/labels.csv\", index=False)\n",
    "\n",
    "print(\"\\nAll Done.\")"
   ]
  },
  {
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   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "aborted",
     "timestamp": 1699328797406,
     "user": {
      "displayName": "Patrick Liao (LBYPatrick)",
      "userId": "03016135564925096917"
     },
     "user_tz": 300
    },
    "id": "we4FaCP_QhJ0"
   },
   "outputs": [],
   "source": [
    "df_merged = pandas.concat(\n",
    "    [df_comments, df_labels.drop(columns=[\"review_text\"])], axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
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     "timestamp": 1699328797406,
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      "displayName": "Patrick Liao (LBYPatrick)",
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     },
     "user_tz": 300
    },
    "id": "aitcnLLQWcIN"
   },
   "outputs": [],
   "source": [
    "df_merged.to_csv(\"data/balanced_with_labels.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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