Panisson_Text_Representation_Learning/4.2.Transformer-Test.ipynb

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   "source": [
    "---\n",
    "# SUMMER SCHOOL 2022:\n",
    "## Text mining and Natural Language Processing for Computational Social Sciences\n",
    "\n",
    "### SocialComQuant Project - Online Teaching Module: Text Representation Learning\n",
    "\n",
    "### André Panisson\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Text Classification with Transformers (part 2)\n",
    "\n",
    "In this notebook, we're going to test a model that was trained previously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell will run only if you are executing this notebook on Google Colab:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "p1nY7tCKf22f"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "RunningInCOLAB = 'COLAB_GPU' in os.environ\n",
    "\n",
    "if RunningInCOLAB:\n",
    "  !pip install datasets transformers sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import transformers\n",
    "transformers.logging.set_verbosity_error()\n",
    "import datasets\n",
    "datasets.logging.set_verbosity_error()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=1\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_VISIBLE_DEVICES 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load the Dataset"
   ]
  },
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   "source": [
    "imdb = datasets.load_dataset(\"imdb\", split=\"train\")\n",
    "imdb = imdb.train_test_split(train_size=5000, test_size=5000, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, pipeline\n",
    "\n",
    "tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")\n",
    "model = DistilBertForSequenceClassification.from_pretrained('distilbert-trained/', num_labels=2)\n",
    "pipe = pipeline(model=model, tokenizer=tokenizer, task=\"text-classification\", device=0,\n",
    "                max_length=512, truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"\n",
    "This film is about a struggling actor trying to find satisfaction in life, especially love which he has not\n",
    "had a taste of for 5 years.\n",
    "It basically is a film featuring a man with very poor social skills, \n",
    "and he says wrong things all the time. The plot is hollow and contrived.\n",
    "The main character, James, is lonely, but this theme of loneliness is not adequately explored.\n",
    "It is more like an empty statement which other subplots stem from.\n",
    "Sadness and disappointment after being dumped are superficial. There is a serious lack of emotions in the film.\n",
    "It is not funny as a comedy either. There are some funny one liners but that is it.\n",
    "It lacks the happy and uplifting atmosphere to infect people with happy mood.\n",
    "I don't find \"I Want Someone to Eat Cheese With\" funny.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'LABEL_0', 'score': 0.9838571548461914}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pipe(imdb['test']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_labels = [(0 if p['label']=='LABEL_0' else 1) for p in pred]\n",
    "true_labels = imdb['test']['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2319,  175],\n",
       "       [ 356, 2150]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(true_labels, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0.8938"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(true_labels, pred_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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