DistilBertModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3gR7L7AGgJip",
    "outputId": "19aeee51-4169-4807-aa46-bec1ce27cc50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "! pip -q install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "!pip -q install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "id": "8cfRllPJgSR7"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error\n",
    "from transformers import (\n",
    "    DistilBertTokenizerFast,\n",
    "    DistilBertForSequenceClassification,\n",
    "    Trainer,\n",
    "    TrainingArguments,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZXLbx-fWgTRm",
    "outputId": "76f813ec-3be9-41dd-d3a6-a44825e5344f"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "XzCPB0ETgWyB"
   },
   "outputs": [],
   "source": [
    "raw = pd.read_csv(\"c_final - Sheet1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "id": "7YKNWMi6gvkD",
    "outputId": "b5d16121-a172-4952-cda2-b633a808270d"
   },
   "outputs": [
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      "text/plain": [
       "   Label                                            Comment\n",
       "0    0.0  I had an accident with an Uber driver in Mexic...\n",
       "1    1.0  I have had my account completely hacked to whe...\n",
       "2    1.0  I requested an 8 mile ride in Boston on a Satu...\n",
       "3    1.0  Uber is overcharging for Toll fees. When In Fl...\n",
       "4    1.0  I had an airport flight today. Uber would not ..."
      ]
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "raw.head()"
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  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
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       "      <th>714</th>\n",
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       "      <th>715</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>716</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>717</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>895</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Let's be honest, who would mind paying couple ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A week ago I tried Uber (hotel near LAX). I wa...</td>\n",
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      "text/plain": [
       "     Label                                            Comment\n",
       "706    NaN                                                NaN\n",
       "707    NaN                                                NaN\n",
       "708    NaN                                                NaN\n",
       "709    NaN                                                NaN\n",
       "710    NaN                                                NaN\n",
       "711    NaN                                                NaN\n",
       "712    NaN                                                NaN\n",
       "713    NaN                                                NaN\n",
       "714    NaN                                                NaN\n",
       "715    NaN                                                NaN\n",
       "716    NaN                                                NaN\n",
       "717    NaN                                                NaN\n",
       "895    NaN  Let's be honest, who would mind paying couple ...\n",
       "934    NaN  A week ago I tried Uber (hotel near LAX). I wa..."
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "raw[raw[\"Label\"].isnull()]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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       "      <th>1702</th>\n",
       "      <td>1.0</td>\n",
       "      <td>They don't communicate and you can't contact t...</td>\n",
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       "<p>1689 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "      Label                                            Comment\n",
       "0       0.0  I had an accident with an Uber driver in Mexic...\n",
       "1       1.0  I have had my account completely hacked to whe...\n",
       "2       1.0  I requested an 8 mile ride in Boston on a Satu...\n",
       "3       1.0  Uber is overcharging for Toll fees. When In Fl...\n",
       "4       1.0  I had an airport flight today. Uber would not ...\n",
       "...     ...                                                ...\n",
       "1698    1.0  I tried to log in to Uber. I have only one ema...\n",
       "1699    1.0  When I saw an ad online for the soon to be Ube...\n",
       "1700    1.0  As a driver I was assaulted by a couple guys. ...\n",
       "1701    1.0  I attempted to sign up as a driver about a mon...\n",
       "1702    1.0  They don't communicate and you can't contact t...\n",
       "\n",
       "[1689 rows x 2 columns]"
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     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw=raw.dropna()\n",
    "raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw[\"Label\"]=raw[\"Label\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "Nt-3BJBjhCkQ"
   },
   "outputs": [],
   "source": [
    "X = list(raw[\"Comment\"])\n",
    "y = list(raw[\"Label\"])\n",
    "\n",
    "\n",
    "train_texts, test_texts, train_labels, test_labels = train_test_split(\n",
    "    X, y, random_state=42, test_size=0.2, stratify=y\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
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    },
    "id": "2vCdxxWVhdvu",
    "outputId": "58cccd62-121b-42c7-e8ba-24c5f9949141"
   },
   "outputs": [],
   "source": [
    "tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")\n",
    "\n",
    "train_encodings = tokenizer(train_texts, truncation=True, padding=True)\n",
    "test_encodings = tokenizer(test_texts, truncation=True, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "1rAe4MvNhr8I"
   },
   "outputs": [],
   "source": [
    "class uberdataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item[\"labels\"] = torch.tensor(self.labels[idx])\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "\n",
    "train_dataset = uberdataset(train_encodings, train_labels)\n",
    "test_dataset = uberdataset(test_encodings, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "kY1YSioZii7d"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(pred):\n",
    "    labels = pred.label_ids\n",
    "    preds = pred.predictions.argmax(-1)\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')\n",
    "    acc = accuracy_score(labels, preds)\n",
    "    return {\n",
    "        'accuracy': acc,\n",
    "        'f1': f1,\n",
    "        'precision': precision,\n",
    "        'recall': recall\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 158,
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    "id": "LzfBUybniqG6",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_projector.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# parameters below based on own my trials \n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"results\",  # output directory\n",
    "    overwrite_output_dir=True,\n",
    "    num_train_epochs=3,  # total number of training epochs\n",
    "    per_device_train_batch_size=4,  # batch size per device during training\n",
    "    per_device_eval_batch_size=4,  # batch size for evaluation\n",
    "    warmup_steps=100,  # number of warmup steps for learning rate scheduler\n",
    "    weight_decay=0.01,  # strength of weight decay\n",
    "    logging_dir=\"logs\",  # directory for storing logs\n",
    "    logging_steps=500,  # default: 500\n",
    "    save_steps=500,  # default: 500\n",
    "    learning_rate=1e-5,\n",
    "    do_train=True,\n",
    "    do_eval=True,\n",
    "    seed=16,\n",
    "    gradient_accumulation_steps=8,  # reduce memory usage while allowing bigger overall batch size.\n",
    ")\n",
    "\n",
    "model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
    "\n",
    "\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,  # the instantiated Transformers model to be trained\n",
    "    args=training_args,  # training arguments, defined above\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=train_dataset,  # training dataset\n",
    "    eval_dataset=test_dataset,  # test dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220
    },
    "id": "st9SrliHjPon",
    "outputId": "df072ca9-8f21-486a-b0c9-49cd909f3d19"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hgaddam/.local/lib/python3.9/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "/home/hgaddam/.local/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='63' max='63' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [63/63 00:47, Epoch 2/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 53.5 s, sys: 780 ms, total: 54.3 s\n",
      "Wall time: 48.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=63, training_loss=0.6884864928230406, metrics={'train_runtime': 48.8192, 'train_samples_per_second': 83.021, 'train_steps_per_second': 1.29, 'total_flos': 533843616583680.0, 'train_loss': 0.6884864928230406, 'epoch': 2.98})"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "JeKfkcuCuPCI",
    "outputId": "dd59fc67-e609-4a7b-8bc4-9405e67ad6b0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted=trainer.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 160
    },
    "id": "iLkcOIwhySTr",
    "outputId": "fb19fdcc-d075-4557-f81d-a137aa34b5a7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hgaddam/.local/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.6717835664749146,\n",
       " 'test_accuracy': 0.6479289940828402,\n",
       " 'test_f1': 0.6020066889632106,\n",
       " 'test_precision': 0.6521739130434783,\n",
       " 'test_recall': 0.5590062111801242,\n",
       " 'test_runtime': 1.7508,\n",
       " 'test_samples_per_second': 193.056,\n",
       " 'test_steps_per_second': 24.56}"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.predict(test_dataset).metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "Y_m40zQIGbao",
    "outputId": "925ec007-fadf-4383-f02d-9dcb856a6927"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hgaddam/.local/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error Rate: 0.3521\n"
     ]
    }
   ],
   "source": [
    "#error_rate = 1.0 - calculations['test_accuracy']\n",
    "error_rate = 1.0 - trainer.predict(test_dataset).metrics['test_accuracy']\n",
    "print(f\"Error Rate: {error_rate:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label1</th>\n",
       "      <th>Comments1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Our driver never showed up and Uber cancelled ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>My driver, Rohan was nice, but when I tried to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Uber is overcharging for Toll fees. When In Fl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>I called uber for going home two days ago and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Ordering a ride, then putting your 10, 12, and...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Label1                                          Comments1\n",
       "0       0  Our driver never showed up and Uber cancelled ...\n",
       "1       1  My driver, Rohan was nice, but when I tried to...\n",
       "2       1  Uber is overcharging for Toll fees. When In Fl...\n",
       "3       0  I called uber for going home two days ago and ...\n",
       "4       1  Ordering a ride, then putting your 10, 12, and..."
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predictions\n",
    "\n",
    "dft=pd.read_csv(\"project_test.csv\")\n",
    "dft\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Our driver never showed up and Uber cancelled our ride, not us. They charged us a cancellation fee for them cancelling. When I disputed it they would only credit it on future ride instead of refund back to our credit card. Why am I only getting a credit on account instead of a full refund to my credit card used?? This is not right what you have done when it was a Uber error.',\n",
       " \"My driver, Rohan was nice, but when I tried to add tip I noticed that he had switched my $9 9am fare in French Quarter to someone else's 2pm trip in Metairie for $28.00! UBER refused to acknowledge. 5 attempts to rectify problem and received nothing but irrelevant automated replies. If you get in the car with Rohan in NOLA and he says he can't get his system to work, it's a a SCAM. Terrible customer service from UBER with zero relevant assistance after 7 days of requesting corrected bill. BEWARE.\",\n",
       " \"Uber is overcharging for Toll fees. When In Florida the Tolls fees estimates between $ .75 to $ 2.00. Uber price shown will be Example $ 16.73 for the ride. After you take the ride you get charge 28.23 and when you check the account it's telling you that you got charge for Tolls. The driver never took the toll route and you try to contact Uber company and they take long to get back to you about the issue of being overcharge. I try to email them and let them know about the extra charges and why am I being charge for a service that I didn't receive.\",\n",
       " \"I called uber for going home two days ago and yesterday they charged me extra 40$ even no one told me reason. Then I contacted uber and they told me driver complained me about I was drunk or something else they sent me stupid pictures. I didn't understand exactly what happened but I just called uber and went home even I didn't talk to driver and went home. How come no one asked me anything and they just charged me 40$ directly. I will take my money back and I will delete uber.\",\n",
       " 'Ordering a ride, then putting your 10, 12, and 14-year-old kids in the car and expect me to take them without you.']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft_comments=list(dft[\"Comments1\"])\n",
    "dft_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sequences = tokenizer(dft_comments, truncation=True, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Label1, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_labels=dft.Label1\n",
    "new_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test = uberdataset(train_sequences, new_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hgaddam/.local/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PredictionOutput(predictions=array([[-0.00071123,  0.06836964],\n",
       "       [ 0.03975232,  0.01989567],\n",
       "       [-0.01391758,  0.10309046],\n",
       "       [-0.02537625,  0.13009623],\n",
       "       [ 0.09344252, -0.12840906]], dtype=float32), label_ids=array([0, 1, 1, 0, 1]), metrics={'test_loss': 0.7406556606292725, 'test_accuracy': 0.2, 'test_f1': 0.3333333333333333, 'test_precision': 0.3333333333333333, 'test_recall': 0.3333333333333333, 'test_runtime': 0.0348, 'test_samples_per_second': 143.491, 'test_steps_per_second': 28.698})"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.predict(new_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hgaddam/.local/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'test_loss': 0.7406556606292725,\n",
       " 'test_accuracy': 0.2,\n",
       " 'test_f1': 0.3333333333333333,\n",
       " 'test_precision': 0.3333333333333333,\n",
       " 'test_recall': 0.3333333333333333,\n",
       " 'test_runtime': 0.0346,\n",
       " 'test_samples_per_second': 144.6,\n",
       " 'test_steps_per_second': 28.92}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.predict(new_test).metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_projector.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DistilBertForSequenceClassification(\n",
      "  (distilbert): DistilBertModel(\n",
      "    (embeddings): Embeddings(\n",
      "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
      "      (position_embeddings): Embedding(512, 768)\n",
      "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer): Transformer(\n",
      "      (layer): ModuleList(\n",
      "        (0-5): 6 x TransformerBlock(\n",
      "          (attention): MultiHeadSelfAttention(\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
      "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
      "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
      "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "          (ffn): FFN(\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
      "            (activation): GELUActivation()\n",
      "          )\n",
      "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
      "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
      "  (dropout): Dropout(p=0.2, inplace=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import DistilBertForSequenceClassification\n",
    "\n",
    "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hgaddam/.local/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.3521\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "pred_labels = trainer.predict(test_dataset).predictions.argmax(axis=1)\n",
    "mse = mean_squared_error(test_labels, pred_labels)\n",
    "print(f\"MSE: {mse:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review: It's an economic and convenient service. I like the convenience of the app and being able to track just where my driver is. I've never had any situations that I felt were unsafe or threatening.\n",
      "The comment is fair\n",
      "\n",
      "Review: The Uber drivers that we used were OK. One was creepy and the other two ladies were nice. The one thing they all had in common, they were SLOW. They drive way below the speed limit and I felt like I was never going to get to my destination.\n",
      "The comment is fair\n",
      "\n",
      "Review: Uber Driver that ride me yesterday was been rude and his car is dirty\n",
      "The comment is fair\n",
      "\n"
     ]
    }
   ],
   "source": [
    "texts = [\n",
    "    \"It's an economic and convenient service. I like the convenience of the app and being able to track just where my driver is. I've never had any situations that I felt were unsafe or threatening.\",\n",
    "    \"The Uber drivers that we used were OK. One was creepy and the other two ladies were nice. The one thing they all had in common, they were SLOW. They drive way below the speed limit and I felt like I was never going to get to my destination.\",\n",
    "    \"Uber Driver that ride me yesterday was been rude and his car is dirty\"\n",
    "]\n",
    "\n",
    "\n",
    "for text in texts:\n",
    "    \n",
    "    encoding = tokenizer(text, truncation=True, padding=True, return_tensors='pt')\n",
    "\n",
    "    \n",
    "    output = model(encoding['input_ids'], encoding['attention_mask'])\n",
    "    predicted_label = torch.argmax(output[0]).item()\n",
    "\n",
    "    \n",
    "    print(f\"Review: {text}\")\n",
    "    if predicted_label == 1:\n",
    "        print(\"The comment is unfair\\n\")\n",
    "    else:\n",
    "        print(\"The comment is fair\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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