nlp-ner-recognition-by-fine-tuning-distilbert.ipynb

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   "source": [
    "## The Dataset\n",
    "\n",
    "### MIT Restaurant Dataset\n",
    "\n",
    "https://groups.csail.mit.edu/sls/downloads/restaurant/\n",
    "\n",
    "https://huggingface.co/datasets/tner/mit_restaurant\n",
    "\n",
    "\n",
    "```\n",
    "{\n",
    "    'tags': [0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 4, 0],\n",
    "    'tokens': ['can', 'you', 'find', 'the', 'phone', 'number', 'for', 'the', 'closest', 'family', 'style', 'restaurant']\n",
    "}\n",
    "\n",
    "```\n",
    "\n",
    "```\n",
    "{\n",
    "    \"O\": 0,\n",
    "    \"B-Rating\": 1,\n",
    "    \"I-Rating\": 2,\n",
    "    \"B-Amenity\": 3,\n",
    "    \"I-Amenity\": 4,\n",
    "    \"B-Location\": 5,\n",
    "    \"I-Location\": 6,\n",
    "    \"B-Restaurant_Name\": 7,\n",
    "    \"I-Restaurant_Name\": 8,\n",
    "    \"B-Price\": 9,\n",
    "    \"B-Hours\": 10,\n",
    "    \"I-Hours\": 11,\n",
    "    \"B-Dish\": 12,\n",
    "    \"I-Dish\": 13,\n",
    "    \"B-Cuisine\": 14,\n",
    "    \"I-Price\": 15,\n",
    "    \"I-Cuisine\": 16\n",
    "}\n",
    "\n",
    "```"
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   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
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   "source": [
    "# hide warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# https://groups.csail.mit.edu/sls/downloads/restaurant/\n"
   ]
  },
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      "Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: evaluate\r\n",
      "Successfully installed evaluate-0.4.3\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -U transformers\n",
    "!pip install -U accelerate\n",
    "!pip install -U datasets\n",
    "!pip install seqeval\n",
    "!pip install evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "361bc730",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:02.086420Z",
     "iopub.status.busy": "2024-11-23T10:31:02.085083Z",
     "iopub.status.idle": "2024-11-23T10:31:03.357354Z",
     "shell.execute_reply": "2024-11-23T10:31:03.356188Z"
    },
    "papermill": {
     "duration": 1.28782,
     "end_time": "2024-11-23T10:31:03.360351",
     "exception": false,
     "start_time": "2024-11-23T10:31:02.072531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4c7ce20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:03.389341Z",
     "iopub.status.busy": "2024-11-23T10:31:03.388608Z",
     "iopub.status.idle": "2024-11-23T10:31:03.670097Z",
     "shell.execute_reply": "2024-11-23T10:31:03.668817Z"
    },
    "papermill": {
     "duration": 0.301193,
     "end_time": "2024-11-23T10:31:03.672946",
     "exception": false,
     "start_time": "2024-11-23T10:31:03.371753",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = requests.get(\"https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/master/mit_restaurant_search_ner/train.bio\")\n",
    "response = response.text\n",
    "response = response.splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2218db3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:03.696662Z",
     "iopub.status.busy": "2024-11-23T10:31:03.696216Z",
     "iopub.status.idle": "2024-11-23T10:31:03.754085Z",
     "shell.execute_reply": "2024-11-23T10:31:03.752695Z"
    },
    "papermill": {
     "duration": 0.072602,
     "end_time": "2024-11-23T10:31:03.756680",
     "exception": false,
     "start_time": "2024-11-23T10:31:03.684078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_tokens = []\n",
    "train_tags = []\n",
    "\n",
    "temp_tokens = []\n",
    "temp_tags = []\n",
    "for line in response:\n",
    "    if line != \"\":\n",
    "        tag, token = line.strip().split(\"\\t\")\n",
    "        temp_tags.append(tag)\n",
    "        temp_tokens.append(token)\n",
    "    else:\n",
    "        train_tokens.append(temp_tokens)\n",
    "        train_tags.append(temp_tags)\n",
    "\n",
    "        temp_tokens, temp_tags = [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89cd527b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:03.780694Z",
     "iopub.status.busy": "2024-11-23T10:31:03.780322Z",
     "iopub.status.idle": "2024-11-23T10:31:03.788723Z",
     "shell.execute_reply": "2024-11-23T10:31:03.787494Z"
    },
    "papermill": {
     "duration": 0.023081,
     "end_time": "2024-11-23T10:31:03.790964",
     "exception": false,
     "start_time": "2024-11-23T10:31:03.767883",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7659, 7659)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_tokens), len(train_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f69c307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:03.816517Z",
     "iopub.status.busy": "2024-11-23T10:31:03.816128Z",
     "iopub.status.idle": "2024-11-23T10:31:04.029553Z",
     "shell.execute_reply": "2024-11-23T10:31:04.028306Z"
    },
    "papermill": {
     "duration": 0.229325,
     "end_time": "2024-11-23T10:31:04.031735",
     "exception": false,
     "start_time": "2024-11-23T10:31:03.802410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1520, 1520)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = requests.get(\"https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/master/mit_restaurant_search_ner/test.bio\")\n",
    "response = response.text\n",
    "response = response.splitlines()\n",
    "\n",
    "test_tokens = []\n",
    "test_tags = []\n",
    "\n",
    "temp_tokens = []\n",
    "temp_tags = []\n",
    "for line in response:\n",
    "    if line != \"\":\n",
    "        tag, token = line.strip().split(\"\\t\")\n",
    "        temp_tags.append(tag)\n",
    "        temp_tokens.append(token)\n",
    "    else:\n",
    "        test_tokens.append(temp_tokens)\n",
    "        test_tags.append(temp_tags)\n",
    "\n",
    "        temp_tokens, temp_tags = [], []\n",
    "\n",
    "len(test_tokens), len(test_tags)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1439e627",
   "metadata": {
    "papermill": {
     "duration": 0.011747,
     "end_time": "2024-11-23T10:31:04.054758",
     "exception": false,
     "start_time": "2024-11-23T10:31:04.043011",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## HuggingFace Dataset Prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "312a1f73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:04.079172Z",
     "iopub.status.busy": "2024-11-23T10:31:04.078815Z",
     "iopub.status.idle": "2024-11-23T10:31:05.049915Z",
     "shell.execute_reply": "2024-11-23T10:31:05.048787Z"
    },
    "papermill": {
     "duration": 0.985875,
     "end_time": "2024-11-23T10:31:05.052136",
     "exception": false,
     "start_time": "2024-11-23T10:31:04.066261",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['tokens', 'ner_tags_str'],\n",
       "        num_rows: 7659\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['tokens', 'ner_tags_str'],\n",
       "        num_rows: 1520\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['tokens', 'ner_tags_str'],\n",
       "        num_rows: 1520\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import Dataset, DatasetDict\n",
    "\n",
    "df = pd.DataFrame({'tokens': train_tokens, 'ner_tags_str': train_tags})\n",
    "train = Dataset.from_pandas(df)\n",
    "\n",
    "df = pd.DataFrame({'tokens': test_tokens, 'ner_tags_str': test_tags})\n",
    "test = Dataset.from_pandas(df)\n",
    "\n",
    "dataset = DatasetDict({'train': train, 'test': test, 'validation': test})\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e0e72e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:05.077045Z",
     "iopub.status.busy": "2024-11-23T10:31:05.076097Z",
     "iopub.status.idle": "2024-11-23T10:31:05.087604Z",
     "shell.execute_reply": "2024-11-23T10:31:05.086449Z"
    },
    "papermill": {
     "duration": 0.026267,
     "end_time": "2024-11-23T10:31:05.089881",
     "exception": false,
     "start_time": "2024-11-23T10:31:05.063614",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tokens': ['2', 'start', 'restaurants', 'with', 'inside', 'dining'],\n",
       " 'ner_tags_str': ['B-Rating', 'I-Rating', 'O', 'O', 'B-Amenity', 'I-Amenity']}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f659e7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:05.114394Z",
     "iopub.status.busy": "2024-11-23T10:31:05.113969Z",
     "iopub.status.idle": "2024-11-23T10:31:05.213700Z",
     "shell.execute_reply": "2024-11-23T10:31:05.212553Z"
    },
    "papermill": {
     "duration": 0.114965,
     "end_time": "2024-11-23T10:31:05.216366",
     "exception": false,
     "start_time": "2024-11-23T10:31:05.101401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "unique_tags = set()\n",
    "for tag in dataset['train']['ner_tags_str']:\n",
    "    unique_tags.update(tag)\n",
    "\n",
    "unique_tags = list(set([x[2:] for x in list(unique_tags) if x!='O']))\n",
    "\n",
    "tag2index = {\"O\": 0}\n",
    "for i, tag in enumerate(unique_tags):\n",
    "    tag2index[f'B-{tag}'] = len(tag2index)\n",
    "    tag2index[f'I-{tag}'] = len(tag2index)\n",
    "\n",
    "index2tag = {v:k for k,v in tag2index.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b0f5e402",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:05.241436Z",
     "iopub.status.busy": "2024-11-23T10:31:05.241022Z",
     "iopub.status.idle": "2024-11-23T10:31:06.057392Z",
     "shell.execute_reply": "2024-11-23T10:31:06.056033Z"
    },
    "papermill": {
     "duration": 0.831607,
     "end_time": "2024-11-23T10:31:06.059800",
     "exception": false,
     "start_time": "2024-11-23T10:31:05.228193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "522acd1717d5416ba32db3dee7475ebb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/7659 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a216751cd9f451188cdc4400cf7e38d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1520 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "144134cc7dc04afaa5e0d90ddb089a12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1520 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = dataset.map(lambda example: {\"ner_tags\": [tag2index[tag] for tag in example['ner_tags_str']]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "366b105b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:06.087421Z",
     "iopub.status.busy": "2024-11-23T10:31:06.086199Z",
     "iopub.status.idle": "2024-11-23T10:31:06.094184Z",
     "shell.execute_reply": "2024-11-23T10:31:06.093054Z"
    },
    "papermill": {
     "duration": 0.023866,
     "end_time": "2024-11-23T10:31:06.096489",
     "exception": false,
     "start_time": "2024-11-23T10:31:06.072623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['tokens', 'ner_tags_str', 'ner_tags'],\n",
       "        num_rows: 7659\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['tokens', 'ner_tags_str', 'ner_tags'],\n",
       "        num_rows: 1520\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['tokens', 'ner_tags_str', 'ner_tags'],\n",
       "        num_rows: 1520\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1564b5a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:06.122127Z",
     "iopub.status.busy": "2024-11-23T10:31:06.121734Z",
     "iopub.status.idle": "2024-11-23T10:31:06.129650Z",
     "shell.execute_reply": "2024-11-23T10:31:06.128349Z"
    },
    "papermill": {
     "duration": 0.024133,
     "end_time": "2024-11-23T10:31:06.132661",
     "exception": false,
     "start_time": "2024-11-23T10:31:06.108528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 8, 0, 0, 1, 2]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"train\"][0][\"ner_tags\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a1b000",
   "metadata": {
    "papermill": {
     "duration": 0.01171,
     "end_time": "2024-11-23T10:31:06.161567",
     "exception": false,
     "start_time": "2024-11-23T10:31:06.149857",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Building\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60709249",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:06.204315Z",
     "iopub.status.busy": "2024-11-23T10:31:06.203862Z",
     "iopub.status.idle": "2024-11-23T10:31:10.707615Z",
     "shell.execute_reply": "2024-11-23T10:31:10.706463Z"
    },
    "papermill": {
     "duration": 4.529122,
     "end_time": "2024-11-23T10:31:10.710087",
     "exception": false,
     "start_time": "2024-11-23T10:31:06.180965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ba81c1bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:10.736278Z",
     "iopub.status.busy": "2024-11-23T10:31:10.735583Z",
     "iopub.status.idle": "2024-11-23T10:31:11.492185Z",
     "shell.execute_reply": "2024-11-23T10:31:11.491182Z"
    },
    "papermill": {
     "duration": 0.772534,
     "end_time": "2024-11-23T10:31:11.494715",
     "exception": false,
     "start_time": "2024-11-23T10:31:10.722181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "699b3f18adbd41058803d41e489b986a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e10d962adf8a4e2a97e9fdbc61eeba5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "562e8f9055c5473bbfe5d658067c10f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc1de55227464cfcb439e99737cc20a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_ckpt = \"distilbert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5097999",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:11.522088Z",
     "iopub.status.busy": "2024-11-23T10:31:11.521594Z",
     "iopub.status.idle": "2024-11-23T10:31:11.529855Z",
     "shell.execute_reply": "2024-11-23T10:31:11.528547Z"
    },
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     "duration": 0.02416,
     "end_time": "2024-11-23T10:31:11.532167",
     "exception": false,
     "start_time": "2024-11-23T10:31:11.508007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tokens': ['5', 'star', 'resturants', 'in', 'my', 'town'],\n",
       " 'ner_tags_str': ['B-Rating',\n",
       "  'I-Rating',\n",
       "  'O',\n",
       "  'B-Location',\n",
       "  'I-Location',\n",
       "  'I-Location'],\n",
       " 'ner_tags': [7, 8, 0, 3, 4, 4]}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "367685a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:11.560419Z",
     "iopub.status.busy": "2024-11-23T10:31:11.559159Z",
     "iopub.status.idle": "2024-11-23T10:31:11.571517Z",
     "shell.execute_reply": "2024-11-23T10:31:11.570333Z"
    },
    "papermill": {
     "duration": 0.029216,
     "end_time": "2024-11-23T10:31:11.573974",
     "exception": false,
     "start_time": "2024-11-23T10:31:11.544758",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS]', '5', 'star', 'rest', '##ura', '##nts', 'in', 'my', 'town', '[SEP]']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input = dataset['train'][2]['tokens']\n",
    "output = tokenizer(input, is_split_into_words=True)\n",
    "tokenizer.convert_ids_to_tokens(output.input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a51ed80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:11.600842Z",
     "iopub.status.busy": "2024-11-23T10:31:11.600447Z",
     "iopub.status.idle": "2024-11-23T10:31:11.610588Z",
     "shell.execute_reply": "2024-11-23T10:31:11.609413Z"
    },
    "papermill": {
     "duration": 0.026421,
     "end_time": "2024-11-23T10:31:11.613138",
     "exception": false,
     "start_time": "2024-11-23T10:31:11.586717",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def tokenize_and_align_labels(examples):\n",
    "    tokenized_inputs = tokenizer(examples['tokens'], truncation=True, is_split_into_words=True)\n",
    "\n",
    "    labels = []\n",
    "    for i, label in enumerate(examples['ner_tags']):\n",
    "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
    "\n",
    "        previous_word_idx = None\n",
    "        label_ids = []\n",
    "\n",
    "        for word_idx in word_ids:\n",
    "            # if id=-100 then loss is not calculated\n",
    "            if word_idx is None:\n",
    "                label_ids.append(-100)\n",
    "\n",
    "            elif word_idx != previous_word_idx:\n",
    "                label_ids.append(label[word_idx])\n",
    "            \n",
    "            else:\n",
    "                label_ids.append(-100)\n",
    "\n",
    "            previous_word_idx = word_idx\n",
    "        \n",
    "        labels.append(label_ids)\n",
    "\n",
    "    tokenized_inputs['labels'] = labels\n",
    "\n",
    "    return tokenized_inputs\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5d1fe9e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:11.640154Z",
     "iopub.status.busy": "2024-11-23T10:31:11.639762Z",
     "iopub.status.idle": "2024-11-23T10:31:12.717174Z",
     "shell.execute_reply": "2024-11-23T10:31:12.715727Z"
    },
    "papermill": {
     "duration": 1.093897,
     "end_time": "2024-11-23T10:31:12.719657",
     "exception": false,
     "start_time": "2024-11-23T10:31:11.625760",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4062bdb6049462e9d0afeb45cf13d22",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/7659 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fbc853cb3884dabb98833292318270c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1520 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70f55f4161574bda894b7eae8a34a56d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1520 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_dataset = dataset.map(tokenize_and_align_labels, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2d86d1c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:12.751318Z",
     "iopub.status.busy": "2024-11-23T10:31:12.750438Z",
     "iopub.status.idle": "2024-11-23T10:31:12.759299Z",
     "shell.execute_reply": "2024-11-23T10:31:12.758182Z"
    },
    "papermill": {
     "duration": 0.027245,
     "end_time": "2024-11-23T10:31:12.761509",
     "exception": false,
     "start_time": "2024-11-23T10:31:12.734264",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tokens': ['5', 'star', 'resturants', 'in', 'my', 'town'],\n",
       " 'ner_tags_str': ['B-Rating',\n",
       "  'I-Rating',\n",
       "  'O',\n",
       "  'B-Location',\n",
       "  'I-Location',\n",
       "  'I-Location'],\n",
       " 'ner_tags': [7, 8, 0, 3, 4, 4],\n",
       " 'input_ids': [101, 1019, 2732, 2717, 4648, 7666, 1999, 2026, 2237, 102],\n",
       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
       " 'labels': [-100, 7, 8, 0, -100, -100, 3, 4, 4, -100]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_dataset['train'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552a1035",
   "metadata": {
    "papermill": {
     "duration": 0.013243,
     "end_time": "2024-11-23T10:31:12.788760",
     "exception": false,
     "start_time": "2024-11-23T10:31:12.775517",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Note: \n",
    "- 0, -100, -100\n",
    "- 'rest', '##ura', '##nts'\n",
    "- For the loss function we put -100 on '##ura', '##nts' for example to align"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab193ac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:12.819347Z",
     "iopub.status.busy": "2024-11-23T10:31:12.818873Z",
     "iopub.status.idle": "2024-11-23T10:31:12.827752Z",
     "shell.execute_reply": "2024-11-23T10:31:12.826660Z"
    },
    "papermill": {
     "duration": 0.027568,
     "end_time": "2024-11-23T10:31:12.830268",
     "exception": false,
     "start_time": "2024-11-23T10:31:12.802700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tokens': ['5', 'star', 'resturants', 'in', 'my', 'town'],\n",
       " 'ner_tags_str': ['B-Rating',\n",
       "  'I-Rating',\n",
       "  'O',\n",
       "  'B-Location',\n",
       "  'I-Location',\n",
       "  'I-Location'],\n",
       " 'ner_tags': [7, 8, 0, 3, 4, 4]}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e323e3dc",
   "metadata": {
    "papermill": {
     "duration": 0.014205,
     "end_time": "2024-11-23T10:31:12.858771",
     "exception": false,
     "start_time": "2024-11-23T10:31:12.844566",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Collation and Metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "267f6a1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:12.889165Z",
     "iopub.status.busy": "2024-11-23T10:31:12.888751Z",
     "iopub.status.idle": "2024-11-23T10:31:29.842494Z",
     "shell.execute_reply": "2024-11-23T10:31:29.841491Z"
    },
    "papermill": {
     "duration": 16.971374,
     "end_time": "2024-11-23T10:31:29.844930",
     "exception": false,
     "start_time": "2024-11-23T10:31:12.873556",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForTokenClassification\n",
    "\n",
    "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8f396e47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:29.873222Z",
     "iopub.status.busy": "2024-11-23T10:31:29.872503Z",
     "iopub.status.idle": "2024-11-23T10:31:32.708069Z",
     "shell.execute_reply": "2024-11-23T10:31:32.706642Z"
    },
    "papermill": {
     "duration": 2.852539,
     "end_time": "2024-11-23T10:31:32.710691",
     "exception": false,
     "start_time": "2024-11-23T10:31:29.858152",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97f3a4805b1b4928abfda4e8f6234462",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import evaluate\n",
    "import numpy as np\n",
    "\n",
    "metric = evaluate.load('seqeval')\n",
    "label_names = list(tag2index)\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    logits, labels = eval_preds\n",
    "\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    true_labels = [[label_names[l] for l in label if l!=-100] for label in labels]\n",
    "\n",
    "    true_predictions = [[label_names[p] for p, l in zip(prediction, label) if l != -100] \n",
    "                        for prediction, label in zip(predictions, labels)]\n",
    "    \n",
    "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
    "\n",
    "    return {\n",
    "        \"precision\": all_metrics['overall_precision'],\n",
    "        'recall': all_metrics['overall_recall'],\n",
    "        'f1': all_metrics['overall_f1'],\n",
    "        'accuracy': all_metrics['overall_accuracy'],\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc95daab",
   "metadata": {
    "papermill": {
     "duration": 0.013697,
     "end_time": "2024-11-23T10:31:32.738163",
     "exception": false,
     "start_time": "2024-11-23T10:31:32.724466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e8f5bda9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:32.768312Z",
     "iopub.status.busy": "2024-11-23T10:31:32.766176Z",
     "iopub.status.idle": "2024-11-23T10:31:34.292818Z",
     "shell.execute_reply": "2024-11-23T10:31:34.291912Z"
    },
    "papermill": {
     "duration": 1.543864,
     "end_time": "2024-11-23T10:31:34.295306",
     "exception": false,
     "start_time": "2024-11-23T10:31:32.751442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e303202e10e4435b391879dc08b685e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForTokenClassification were not initialized from the model checkpoint at distilbert-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": [
    "from transformers import AutoModelForTokenClassification\n",
    "\n",
    "model = AutoModelForTokenClassification.from_pretrained(model_ckpt, id2label=index2tag, label2id=tag2index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a3453034",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:34.325380Z",
     "iopub.status.busy": "2024-11-23T10:31:34.324543Z",
     "iopub.status.idle": "2024-11-23T10:31:35.806819Z",
     "shell.execute_reply": "2024-11-23T10:31:35.805655Z"
    },
    "papermill": {
     "duration": 1.500394,
     "end_time": "2024-11-23T10:31:35.809490",
     "exception": false,
     "start_time": "2024-11-23T10:31:34.309096",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments, Trainer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9926f434",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:35.838179Z",
     "iopub.status.busy": "2024-11-23T10:31:35.837813Z",
     "iopub.status.idle": "2024-11-23T10:31:35.848812Z",
     "shell.execute_reply": "2024-11-23T10:31:35.847869Z"
    },
    "papermill": {
     "duration": 0.02809,
     "end_time": "2024-11-23T10:31:35.851126",
     "exception": false,
     "start_time": "2024-11-23T10:31:35.823036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "args = TrainingArguments(\"finetuned-ner\", \n",
    "                         evaluation_strategy='epoch',\n",
    "                         save_strategy='epoch',\n",
    "                         learning_rate=2e-5,\n",
    "                         num_train_epochs=3,\n",
    "                         weight_decay=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d038a1c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-23T10:31:35.880114Z",
     "iopub.status.busy": "2024-11-23T10:31:35.879726Z",
     "iopub.status.idle": "2024-11-23T10:31:36.857214Z",
     "shell.execute_reply": "2024-11-23T10:31:36.856151Z"
    },
    "papermill": {
     "duration": 0.994881,
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     "exception": false,
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   "source": [
    "trainer = Trainer(model=model, args=args,\n",
    "                  train_dataset=tokenized_dataset['train'],\n",
    "                  eval_dataset=tokenized_dataset['validation'],\n",
    "                  data_collator=data_collator,\n",
    "                  compute_metrics=compute_metrics,\n",
    "                  tokenizer=tokenizer)"
   ]
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       "      \n",
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       "      [2874/2874 45:36, Epoch 3/3]\n",
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       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Accuracy</th>\n",
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       "TrainOutput(global_step=2874, training_loss=0.31571376166081544, metrics={'train_runtime': 2739.1258, 'train_samples_per_second': 8.388, 'train_steps_per_second': 1.049, 'total_flos': 105239751014754.0, 'train_loss': 0.31571376166081544, 'epoch': 3.0})"
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    "trainer.train()"
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  {
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   "execution_count": 30,
   "id": "a95b14c8",
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   "source": [
    "trainer.save_model(\"ner_distilbert\")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Prediction and Load & Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c803eb3a",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "checkpoint = \"ner_distilbert\"\n",
    "pipe = pipeline('token-classification', model=checkpoint, aggregation_strategy='simple')"
   ]
  },
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   "execution_count": 32,
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    {
     "data": {
      "text/plain": [
       "[{'entity_group': 'Rating',\n",
       "  'score': 0.97273916,\n",
       "  'word': 'best',\n",
       "  'start': 28,\n",
       "  'end': 32},\n",
       " {'entity_group': 'Dish',\n",
       "  'score': 0.9182242,\n",
       "  'word': 'shushi',\n",
       "  'start': 33,\n",
       "  'end': 39},\n",
       " {'entity_group': 'Location',\n",
       "  'score': 0.90122294,\n",
       "  'word': 'new york',\n",
       "  'start': 43,\n",
       "  'end': 51}]"
      ]
     },
     "execution_count": 32,
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    }
   ],
   "source": [
    "pipe(\"which restaurant serves the best shushi in new york?\")"
   ]
  }
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