04 nlp-tutorial-fine-tuning-bert-in-pytorch.ipynb

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   "source": [
    "# Effortless NLP using HuggingFace's Tranformers Ecosystem"
   ]
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   "source": [
    "![Image](https://raw.githubusercontent.com/RajkumarGalaxy/dataset/master/Images/z0002.jpg)\n",
    "\n",
    "> Image by [Author](https://raw.githubusercontent.com/RajkumarGalaxy/dataset/master/Images/z0002.jpg)\n",
    "### How to finetune a BERT model on a custom dataset using Pytorch?"
   ]
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   "source": [
    "#### ------------------------------------------------ \n",
    "#### *Articles So Far In This Series*\n",
    "#### -> [[NLP Tutorial] Finish Tasks in Two Lines of Code](https://www.kaggle.com/rajkumarl/nlp-tutorial-finish-tasks-in-two-lines-of-code)\n",
    "#### -> [[NLP Tutorial] Unwrapping Transformers Pipeline](https://www.kaggle.com/rajkumarl/nlp-unwrapping-transformers-pipeline)\n",
    "#### -> [[NLP Tutorial] Exploring Tokenizers](https://www.kaggle.com/rajkumarl/nlp-tutorial-exploring-tokenizers)\n",
    "#### -> [[NLP Tutorial] Fine-Tuning in TensorFlow](https://www.kaggle.com/rajkumarl/nlp-tutorial-fine-tuning-in-tensorflow) \n",
    "#### -> [[NLP Tutorail] Fine-Tuning in Pytorch](https://www.kaggle.com/rajkumarl/nlp-tutorial-fine-tuning-in-pytorch) \n",
    "#### -> [[NLP Tutorail] Fine-Tuning with Trainer API](https://www.kaggle.com/rajkumarl/nlp-tutorial-fine-tuning-with-trainer-api) \n",
    "#### ------------------------------------------------ "
   ]
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   "source": [
    "# Prepare Environment and Data\n",
    "\n",
    "In this article we discuss fine-tuning a BERT model on the famous WNLI dataset using Trainer API. This requires a GPU environment for faster training and inference, while it still works on a CPU device too."
   ]
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      "Installing collected packages: huggingface-hub, transformers\r\n",
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      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "datasets 1.14.0 requires huggingface-hub<0.1.0,>=0.0.19, but you have huggingface-hub 0.1.2 which is incompatible.\r\n",
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
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      "Installing collected packages: datasets\r\n",
      "  Attempting uninstall: datasets\r\n",
      "    Found existing installation: datasets 1.14.0\r\n",
      "    Uninstalling datasets-1.14.0:\r\n",
      "      Successfully uninstalled datasets-1.14.0\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "allennlp 2.7.0 requires transformers<4.10,>=4.1, but you have transformers 4.12.5 which is incompatible.\u001b[0m\r\n",
      "Successfully installed datasets-1.16.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "4.12.5\n",
      "1.16.1\n"
     ]
    }
   ],
   "source": [
    "# upgrade transformers and datasets to latest versions\n",
    "!pip install --upgrade transformers\n",
    "!pip install --upgrade datasets\n",
    "import transformers\n",
    "import datasets\n",
    "print(transformers.__version__)\n",
    "print(datasets.__version__)"
   ]
  },
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     "text": [
      "Device available: cuda:0\n"
     ]
    }
   ],
   "source": [
    "# Make necessary imports\n",
    "\n",
    "# for array operations \n",
    "import numpy as np \n",
    "# PyTorch framework\n",
    "import torch\n",
    "# plotting\n",
    "from matplotlib import pyplot as plt\n",
    "# reproducibility\n",
    "import random\n",
    "# to watch progress\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# HuggingFace ecosystem\n",
    "# tokenizer\n",
    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
    "# model\n",
    "from transformers import AutoModelForSequenceClassification\n",
    "# optimizer, lr-scheduler\n",
    "from transformers import AdamW, get_scheduler\n",
    "# dataset\n",
    "from datasets import load_dataset, load_metric\n",
    "\n",
    "# a seed for reproducibility\n",
    "SEED = 42\n",
    "# set seed\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "random.seed(SEED)\n",
    "\n",
    "# check for GPU device\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print('Device available:', device) "
   ]
  },
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    "tags": []
   },
   "source": [
    "Load the WNLI Dataset from GLUE benchmark"
   ]
  },
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   "outputs": [
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset glue/wnli (download: 28.32 KiB, generated: 154.03 KiB, post-processed: Unknown size, total: 182.35 KiB) to /root/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
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       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
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       "0 examples [00:00, ? examples/s]"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/wnli/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
     ]
    },
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   ],
   "source": [
    "raw_data = load_dataset(\"glue\", \"wnli\")"
   ]
  },
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     "iopub.status.idle": "2021-11-30T13:00:39.768850Z",
     "shell.execute_reply": "2021-11-30T13:00:39.769284Z",
     "shell.execute_reply.started": "2021-11-30T11:13:20.225787Z"
    },
    "papermill": {
     "duration": 0.045804,
     "end_time": "2021-11-30T13:00:39.769420",
     "exception": false,
     "start_time": "2021-11-30T13:00:39.723616",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 635\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 71\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 146\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how does it look like?\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "340a92f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:39.850857Z",
     "iopub.status.busy": "2021-11-30T13:00:39.850046Z",
     "iopub.status.idle": "2021-11-30T13:00:39.853095Z",
     "shell.execute_reply": "2021-11-30T13:00:39.853656Z",
     "shell.execute_reply.started": "2021-11-30T11:13:23.665747Z"
    },
    "papermill": {
     "duration": 0.047112,
     "end_time": "2021-11-30T13:00:39.853798",
     "exception": false,
     "start_time": "2021-11-30T13:00:39.806686",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sentence1': 'I stuck a pin through a carrot. When I pulled the pin out, it had a hole.',\n",
       " 'sentence2': 'The carrot had a hole.',\n",
       " 'label': 1,\n",
       " 'idx': 0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample a data\n",
    "raw_data[\"train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90c125a",
   "metadata": {
    "papermill": {
     "duration": 0.041088,
     "end_time": "2021-11-30T13:00:39.938933",
     "exception": false,
     "start_time": "2021-11-30T13:00:39.897845",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Each data point contains a sentence, its index and its label. What labels are there? What are their positions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f82cf80e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:40.021644Z",
     "iopub.status.busy": "2021-11-30T13:00:40.020789Z",
     "iopub.status.idle": "2021-11-30T13:00:40.026203Z",
     "shell.execute_reply": "2021-11-30T13:00:40.026709Z",
     "shell.execute_reply.started": "2021-11-30T11:13:53.451334Z"
    },
    "papermill": {
     "duration": 0.047649,
     "end_time": "2021-11-30T13:00:40.026837",
     "exception": false,
     "start_time": "2021-11-30T13:00:39.979188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sentence1': Value(dtype='string', id=None),\n",
       " 'sentence2': Value(dtype='string', id=None),\n",
       " 'label': ClassLabel(num_classes=2, names=['not_entailment', 'entailment'], names_file=None, id=None),\n",
       " 'idx': Value(dtype='int32', id=None)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what features are there in data?\n",
    "# What are the label names?\n",
    "raw_data[\"train\"].features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e18663",
   "metadata": {
    "papermill": {
     "duration": 0.03896,
     "end_time": "2021-11-30T13:00:40.104808",
     "exception": false,
     "start_time": "2021-11-30T13:00:40.065848",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We understand that this dataset consists of the supervised task - *Sequence Entailment Classification* with 2 classes: `not_entailment` [0] and `entailment` [1] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f367cee",
   "metadata": {
    "papermill": {
     "duration": 0.038293,
     "end_time": "2021-11-30T13:00:40.181274",
     "exception": false,
     "start_time": "2021-11-30T13:00:40.142981",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Tokenizer and Data Collator\n",
    "\n",
    "We are about to use a pre-trained Bert_base_uncased model for our fine-tuning. A tokenizer function associated with a data collator can ensure efficient memory usage and quick data handling during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca74e3d0",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-11-30T11:15:41.437002Z"
    },
    "papermill": {
     "duration": 6.91902,
     "end_time": "2021-11-30T13:00:47.182447",
     "exception": false,
     "start_time": "2021-11-30T13:00:40.263427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcda8d795da74649bf939616c940dd70",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
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       "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7cb13d80cde45769a644f84a5762671",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "checkpoint = 'bert-base-uncased'\n",
    "# bert tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "# data collator for dynamic padding as per batch\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d487433e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:47.267698Z",
     "iopub.status.busy": "2021-11-30T13:00:47.266846Z",
     "iopub.status.idle": "2021-11-30T13:00:47.269221Z",
     "shell.execute_reply": "2021-11-30T13:00:47.268805Z",
     "shell.execute_reply.started": "2021-11-30T11:16:31.750888Z"
    },
    "papermill": {
     "duration": 0.046283,
     "end_time": "2021-11-30T13:00:47.269329",
     "exception": false,
     "start_time": "2021-11-30T13:00:47.223046",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# define a tokenize function\n",
    "def Tokenize_function(example):\n",
    "    return tokenizer(example['sentence1'], example['sentence2'], truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87f860fa",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "  0%|          | 0/1 [00:00<?, ?ba/s]"
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     },
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    },
    {
     "data": {
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       "  0%|          | 0/1 [00:00<?, ?ba/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# tokenize entire data\n",
    "tokenized_data = raw_data.map(Tokenize_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97fef0ed",
   "metadata": {
    "papermill": {
     "duration": 0.040377,
     "end_time": "2021-11-30T13:00:47.678606",
     "exception": false,
     "start_time": "2021-11-30T13:00:47.638229",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Remove unnecessary columns. \n",
    "Rename colum `label` to `labels` as expected by a PyTorch Model.\n",
    "What columns are there in tokenized data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4736ba73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:47.766157Z",
     "iopub.status.busy": "2021-11-30T13:00:47.765197Z",
     "iopub.status.idle": "2021-11-30T13:00:47.777365Z",
     "shell.execute_reply": "2021-11-30T13:00:47.777822Z",
     "shell.execute_reply.started": "2021-11-30T11:18:09.070931Z"
    },
    "papermill": {
     "duration": 0.058226,
     "end_time": "2021-11-30T13:00:47.777948",
     "exception": false,
     "start_time": "2021-11-30T13:00:47.719722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_data = tokenized_data.remove_columns(['idx','sentence1','sentence2'])\n",
    "tokenized_data = tokenized_data.rename_column('label','labels')\n",
    "tokenized_data.set_format('pt')\n",
    "tokenized_data[\"train\"].column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7477d2cf",
   "metadata": {
    "papermill": {
     "duration": 0.04127,
     "end_time": "2021-11-30T13:00:47.860092",
     "exception": false,
     "start_time": "2021-11-30T13:00:47.818822",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`attention_mask`, `input_ids`, `token_type_ids` are the necessary input features and `labels` is the target. Other features are useless in the view of modeling.\n",
    "\n",
    "Prepare DataLoader by batching and dynamic padding using data collator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b4562e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:47.949213Z",
     "iopub.status.busy": "2021-11-30T13:00:47.948409Z",
     "iopub.status.idle": "2021-11-30T13:00:47.951112Z",
     "shell.execute_reply": "2021-11-30T13:00:47.950658Z",
     "shell.execute_reply.started": "2021-11-30T12:30:10.710589Z"
    },
    "papermill": {
     "duration": 0.049777,
     "end_time": "2021-11-30T13:00:47.951218",
     "exception": false,
     "start_time": "2021-11-30T13:00:47.901441",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = torch.utils.data.DataLoader(tokenized_data[\"train\"],\n",
    "                                         shuffle=True,\n",
    "                                         batch_size=8,\n",
    "                                         collate_fn=data_collator\n",
    "                                        )\n",
    "val_data = torch.utils.data.DataLoader(tokenized_data[\"validation\"],\n",
    "                                       batch_size=8,\n",
    "                                       collate_fn=data_collator\n",
    "                                      )\n",
    "test_data = torch.utils.data.DataLoader(tokenized_data[\"test\"],\n",
    "                                        batch_size=8,\n",
    "                                        collate_fn=data_collator\n",
    "                                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32caf1dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:48.040376Z",
     "iopub.status.busy": "2021-11-30T13:00:48.039846Z",
     "iopub.status.idle": "2021-11-30T13:00:48.084306Z",
     "shell.execute_reply": "2021-11-30T13:00:48.085060Z",
     "shell.execute_reply.started": "2021-11-30T11:38:55.099153Z"
    },
    "papermill": {
     "duration": 0.092248,
     "end_time": "2021-11-30T13:00:48.085237",
     "exception": false,
     "start_time": "2021-11-30T13:00:47.992989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      attention_mask : torch.Size([8, 49])\n",
      "           input_ids : torch.Size([8, 49])\n",
      "              labels : torch.Size([8])\n",
      "      token_type_ids : torch.Size([8, 49])\n"
     ]
    }
   ],
   "source": [
    "# do a chekck for proper data preprocessing\n",
    "for batch in train_data:\n",
    "    [print('{:>20} : {}'.format(k,v.shape)) for k,v in batch.items()] \n",
    "    break "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874856a9",
   "metadata": {
    "papermill": {
     "duration": 0.041171,
     "end_time": "2021-11-30T13:00:48.168141",
     "exception": false,
     "start_time": "2021-11-30T13:00:48.126970",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Data is ready now for efficient data loading and faster training."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b655192a",
   "metadata": {
    "papermill": {
     "duration": 0.041141,
     "end_time": "2021-11-30T13:00:48.250313",
     "exception": false,
     "start_time": "2021-11-30T13:00:48.209172",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a1161c",
   "metadata": {
    "papermill": {
     "duration": 0.040879,
     "end_time": "2021-11-30T13:00:48.332506",
     "exception": false,
     "start_time": "2021-11-30T13:00:48.291627",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Load a BERT model from the checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e23942a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-30T13:00:48.421042Z",
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     "shell.execute_reply": "2021-11-30T13:01:29.731110Z",
     "shell.execute_reply.started": "2021-11-30T12:07:11.360066Z"
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     "exception": false,
     "start_time": "2021-11-30T13:00:48.374089",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8619f88613dd45559dd11f6d37036aa6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias']\n",
      "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', '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": [
    "# cache a pre-trained BERT model for two-class classification\n",
    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ebb636",
   "metadata": {
    "papermill": {
     "duration": 0.042124,
     "end_time": "2021-11-30T13:01:29.816336",
     "exception": false,
     "start_time": "2021-11-30T13:01:29.774212",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Check for data and model compatibility by passing a sample batch of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c1c1406",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-11-30T13:01:30.761277Z",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.6845, grad_fn=<NllLossBackward>) torch.Size([8, 2])\n"
     ]
    }
   ],
   "source": [
    "outputs = model(**batch)\n",
    "print(outputs.loss, outputs.logits.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d810782",
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   "source": [
    "Data and model are ready for fine-tuning. Now, we need to define an optimizer and a learning rate scheduler."
   ]
  },
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   "cell_type": "code",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 3\n",
    "NUM_TRAINING_STEPS = EPOCHS * len(train_data)\n",
    "print(NUM_TRAINING_STEPS)\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
    "lr_scheduler = get_scheduler(\"linear\",\n",
    "                             optimizer=optimizer,\n",
    "                             num_warmup_steps=0,\n",
    "                             num_training_steps=NUM_TRAINING_STEPS\n",
    "                            )"
   ]
  },
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   "cell_type": "markdown",
   "id": "829488f3",
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    "tags": []
   },
   "source": [
    "We can define the device to put our data and model on."
   ]
  },
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   "cell_type": "code",
   "execution_count": 16,
   "id": "266ba823",
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.to(device)\n",
    "device"
   ]
  },
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   "source": [
    "Define the training loop"
   ]
  },
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   "id": "dd1aba2f",
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   "source": [
    "progress_bar = tqdm(range(NUM_TRAINING_STEPS))\n",
    "\n",
    "model.train()\n",
    "for epoch in range(EPOCHS):\n",
    "    for batch in train_data:\n",
    "        batch = {k:v.to(device) for k,v in batch.items()}\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        progress_bar.update(1)"
   ]
  },
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   "source": [
    "Training is over.\n",
    "Define an Evaluation loop to evaluate the validation set."
   ]
  },
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       "Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"
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     "text": [
      "tensor([0, 1, 0, 1, 1, 0, 1, 1], device='cuda:0') torch.Size([8])\n",
      "tensor([0, 0, 0, 1, 0, 0, 0, 0], device='cuda:0') torch.Size([8])\n",
      "tensor([1, 0, 0, 0, 0, 0, 0, 1], device='cuda:0') torch.Size([8])\n",
      "tensor([0, 1, 0, 1, 1, 1, 1, 0], device='cuda:0') torch.Size([8])\n",
      "tensor([1, 1, 0, 1, 0, 0, 1, 1], device='cuda:0') torch.Size([8])\n",
      "tensor([0, 0, 0, 1, 0, 0, 1, 0], device='cuda:0') torch.Size([8])\n",
      "tensor([1, 0, 0, 1, 0, 0, 1, 0], device='cuda:0') torch.Size([8])\n",
      "tensor([1, 0, 1, 1, 0, 0, 1, 1], device='cuda:0') torch.Size([8])\n",
      "tensor([0, 1, 1, 0, 1, 0, 0], device='cuda:0') torch.Size([7])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.5633802816901409}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric = load_metric(\"glue\",\"wnli\")\n",
    "\n",
    "model.eval()\n",
    "for batch in val_data:\n",
    "    batch = {k:v.to(device) for k,v in batch.items()}\n",
    "    print(batch['labels'], batch['labels'].shape)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**batch)\n",
    "    logits = outputs.logits\n",
    "    preds = torch.argmax(logits, dim=-1)\n",
    "    metric.add_batch(predictions=preds,references=batch['labels'])\n",
    "metric.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28614d4d",
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   "source": [
    "# Prediction \n",
    "\n",
    "Predict the labels for the test data. \n",
    "\n",
    "Being test_data, `labels` are -1 for each data point. However, model expects either 0 or 1 as `labels`. Convert labels into int64 type ones of same batch size before feeding into the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aec0ed50",
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   "source": [
    "# make predictions\n",
    "preds = [] \n",
    "model.eval()\n",
    "for batch in test_data:\n",
    "    batch['labels'] = torch.ones(len(batch['labels'])).type(torch.int64)\n",
    "    batch = {k:v.to(device) for k,v in batch.items()}\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**batch)\n",
    "    logits = outputs.logits\n",
    "    yhat = torch.argmax(logits, dim=-1)\n",
    "    preds.append(yhat)"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0'),\n",
       " tensor([0, 0], device='cuda:0')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "711826a7",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### That's the end. We got a good understanding of fine-tuning a BERT model on WNLI dataset for a sentiment analysis task in PyTorch!\n",
    "\n",
    "##### Key reference: [HuggingFace's NLP Course](https://huggingface.co/course)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6baa6b",
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     "start_time": "2021-11-30T13:01:57.705843",
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    },
    "tags": []
   },
   "source": [
    "### Thank you for your valuable time!"
   ]
  }
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