multiclass_sentiment_RoBERTa_finetuned.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1755fe4-e97e-4a6b-aa64-8c02c835d79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install transformers transformers[torch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "91f45a9f-01eb-4843-84c6-58f8e2c302fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import torch\n",
    "from transformers import RobertaTokenizer, RobertaForSequenceClassification\n",
    "from transformers import DataCollatorWithPadding\n",
    "from transformers import Trainer, TrainingArguments\n",
    "from transformers import pipeline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "034a59b3-64b5-43ae-a909-d173c027ba1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(device(type='cuda'), 'NVIDIA GeForce RTX 3090')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device, torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc02517b-dac7-4769-93a3-0581913cd816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2287\n",
       "1    2544\n",
       "2    2816\n",
       "Name: classification, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json(\"../_data/multiclass_sentiment.json\")\n",
    "df[\"title+text\"] = df[\"title\"] + \" \" + df[\"text\"]\n",
    "\n",
    "texts = df[\"title+text\"].tolist()\n",
    "labels = df[\"classification\"].tolist()\n",
    "\n",
    "df[\"classification\"].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57666d3d-d277-406b-bb5d-39566de327a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6117, 1530)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_texts, test_texts, train_labels, test_labels = train_test_split(\n",
    "    texts, labels, stratify=labels, test_size=0.2\n",
    ")\n",
    "\n",
    "len(train_texts),len(test_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "390716dc-dd0d-45b1-ac35-78b08922567b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'roberta-large'\n",
    "\n",
    "tokenizer = RobertaTokenizer.from_pretrained(model_name)\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f270edef-5a28-40e9-bea2-9d80cdc60bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.dense.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": [
    "id2label = {0: \"NEGATIVE\", 1: \"NEUTRAL\", 2:\"POSITIVE\"}\n",
    "label2id = {\"NEGATIVE\": 0, \"NEUTRAL\": 1, \"POSITIVE\":2}\n",
    "\n",
    "model = RobertaForSequenceClassification.from_pretrained(\n",
    "    model_name, \n",
    "    num_labels=3,\n",
    "    id2label=id2label, \n",
    "    label2id=label2id\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "972148a4-6874-4d6a-85af-920c993a538d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_encodings = tokenizer(train_texts, truncation=True, max_length=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "753bcafe-21aa-4c00-ab73-a870a470672b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SentimentDataset(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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a59ee1ad-a68b-40e3-b611-ed063844dbaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = SentimentDataset(train_encodings, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09c093f9-2b26-4ceb-9545-7c54a0f83e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir='./results',\n",
    "    save_strategy=\"no\",\n",
    "    optim=\"adamw_torch\",\n",
    "    learning_rate=2e-5,\n",
    "    num_train_epochs=4, \n",
    "    group_by_length=True,\n",
    "    do_eval=False,\n",
    "    evaluation_strategy=\"no\",\n",
    "    per_device_train_batch_size=8, \n",
    "    per_device_eval_batch_size=8,  \n",
    "    warmup_steps=100,               \n",
    "    weight_decay=0.01,              \n",
    "    logging_strategy=\"no\",\n",
    "    load_best_model_at_end=True\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,                        \n",
    "    args=training_args,                 \n",
    "    train_dataset=train_dataset,        \n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e22094c-9d70-4383-bf7e-d92dba81f79c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='3060' max='3060' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3060/3060 23:32, Epoch 4/4]\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"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=3060, training_loss=0.4878290712443832, metrics={'train_runtime': 1413.2032, 'train_samples_per_second': 17.314, 'train_steps_per_second': 2.165, 'total_flos': 1.6551841324800624e+16, 'train_loss': 0.4878290712443832, 'epoch': 4.0})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec69cf26-8755-433e-b5ac-055a1a377434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7542483660130719"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_encodings = tokenizer(test_texts, truncation=True, padding=True, max_length=512)\n",
    "test_dataset = SentimentDataset(test_encodings, test_labels)\n",
    "\n",
    "raw_output = trainer.predict(test_dataset)\n",
    "y_preds = np.argmax(raw_output.predictions, axis=-1)\n",
    "accuracy_score(y_true=test_labels, y_pred=y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9001896b-ce4d-4c64-9041-997a3c49dbd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.80      0.79       458\n",
      "           1       0.67      0.66      0.67       509\n",
      "           2       0.82      0.80      0.81       563\n",
      "\n",
      "    accuracy                           0.75      1530\n",
      "   macro avg       0.75      0.75      0.75      1530\n",
      "weighted avg       0.75      0.75      0.75      1530\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(test_labels, y_preds)\n",
    "print(\"Classification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9254c3b1-ab6c-430e-8884-f1d47f27defe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_labels, y_preds)\n",
    "xlabels = ['Class 0', 'Class 1', \"Class 2\"]\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=xlabels, yticklabels=xlabels)\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22c5ee0f-6b0e-46eb-9496-dfce5c3b875f",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.save_model('./results/roberta_large_sentiment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c01e501-c0d0-4251-b268-770b75d67d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7542483660130719"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = './results/roberta_large_sentiment'\n",
    "model = RobertaForSequenceClassification.from_pretrained(model_path, num_labels=3)\n",
    "test_trainer = Trainer(model)\n",
    "raw_predictions = test_trainer.predict(test_dataset)\n",
    "y_preds = np.argmax(raw_predictions.predictions, axis=-1)\n",
    "accuracy_score(y_true=test_labels, y_pred=y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87b17764-62da-48c6-8ad6-99a32741bad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
      "pip install xformers.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9966276288032532},\n",
       " {'label': 'NEGATIVE', 'score': 0.9954739212989807}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = [\n",
    "        \"\"\"US economic growth was stronger than previously reported at the start of the year.\"\"\",\n",
    "        \"\"\"US economic growth was weaker than expected at the start of the year.\"\"\"\n",
    "        ]\n",
    "\n",
    "classifier = pipeline(\"text-classification\", model='./results/roberta_large_sentiment', device=0)\n",
    "classifier(texts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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