Transformer_application_Named_Entity_Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer Network Application: Named-Entity Recognition\n",
    "\n",
    "\n",
    "* **Use tokenizers and pre-trained models from the HuggingFace Library.**\n",
    "* **Fine-tune a pre-trained transformer model for Named-Entity Recognition.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "- [Packages](#0)\n",
    "- [1 - Named-Entity Recogniton to Process Resumes](#1)\n",
    "    - [1.1 - Data Cleaning](#1-1)\n",
    "    - [1.2 - Padding and Generating Tags](#1-2)\n",
    "    - [1.3 - Tokenize and Align Labels with 🤗 Library](#1-3)\n",
    "        - [Exercise 1 - tokenize_and_align_labels](#ex-1)\n",
    "    - [1.4 - Optimization](#1-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='0'></a>\n",
    "## Packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import json\n",
    "import random\n",
    "import logging\n",
    "import re\n",
    "\n",
    "tf.get_logger().setLevel('ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Named-Entity Recogniton to Process Resumes\n",
    "\n",
    "When faced with a large amount of unstructured text data, named-entity recognition (NER) can help detect and classify important information in your dataset. For instance, in the example \"John is from Germany\", NER would help detect \"John\" and \"Germany\" as named-entities and classify them as person and location. \n",
    "\n",
    "* We will use a variation of the Transformer model to process a large dataset of resumes.\n",
    "* We will find and classify relavent information such as the companies the applicant worked at, skills, type of degree, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1-1'></a>\n",
    "### 1.1 - Dataset Cleaning\n",
    "\n",
    "We will optimize a Transformer model on a dataset of resumes. Take a look at how the data we will be working with are structured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data = pd.read_json(\"ner.json\", lines=True)\n",
    "df_data = df_data.drop(['extras'], axis=1)\n",
    "df_data['content'] = df_data['content'].str.replace(\"\\n\", \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>annotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abhishek Jha Application Development Associate...</td>\n",
       "      <td>[{'label': ['Skills'], 'points': [{'start': 12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afreen Jamadar Active member of IIIT Committee...</td>\n",
       "      <td>[{'label': ['Email Address'], 'points': [{'sta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akhil Yadav Polemaina Hyderabad, Telangana - E...</td>\n",
       "      <td>[{'label': ['Skills'], 'points': [{'start': 37...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alok Khandai Operational Analyst (SQL DBA) Eng...</td>\n",
       "      <td>[{'label': ['Skills'], 'points': [{'start': 80...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ananya Chavan lecturer - oracle tutorials  Mum...</td>\n",
       "      <td>[{'label': ['Degree'], 'points': [{'start': 20...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  Abhishek Jha Application Development Associate...   \n",
       "1  Afreen Jamadar Active member of IIIT Committee...   \n",
       "2  Akhil Yadav Polemaina Hyderabad, Telangana - E...   \n",
       "3  Alok Khandai Operational Analyst (SQL DBA) Eng...   \n",
       "4  Ananya Chavan lecturer - oracle tutorials  Mum...   \n",
       "\n",
       "                                          annotation  \n",
       "0  [{'label': ['Skills'], 'points': [{'start': 12...  \n",
       "1  [{'label': ['Email Address'], 'points': [{'sta...  \n",
       "2  [{'label': ['Skills'], 'points': [{'start': 37...  \n",
       "3  [{'label': ['Skills'], 'points': [{'start': 80...  \n",
       "4  [{'label': ['Degree'], 'points': [{'start': 20...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': ['Skills'],\n",
       "  'points': [{'start': 1295,\n",
       "    'end': 1621,\n",
       "    'text': '\\n• Programming language: C, C++, Java\\n• Oracle PeopleSoft\\n• Internet Of Things\\n• Machine Learning\\n• Database Management System\\n• Computer Networks\\n• Operating System worked on: Linux, Windows, Mac\\n\\nNon - Technical Skills\\n\\n• Honest and Hard-Working\\n• Tolerant and Flexible to Different Situations\\n• Polite and Calm\\n• Team-Player'}]},\n",
       " {'label': ['Skills'],\n",
       "  'points': [{'start': 993,\n",
       "    'end': 1153,\n",
       "    'text': 'C (Less than 1 year), Database (Less than 1 year), Database Management (Less than 1 year),\\nDatabase Management System (Less than 1 year), Java (Less than 1 year)'}]},\n",
       " {'label': ['College Name'],\n",
       "  'points': [{'start': 939, 'end': 956, 'text': 'Kendriya Vidyalaya'}]},\n",
       " {'label': ['College Name'],\n",
       "  'points': [{'start': 883, 'end': 904, 'text': 'Woodbine modern school'}]},\n",
       " {'label': ['Graduation Year'],\n",
       "  'points': [{'start': 856, 'end': 860, 'text': '2017\\n'}]},\n",
       " {'label': ['College Name'],\n",
       "  'points': [{'start': 771,\n",
       "    'end': 813,\n",
       "    'text': 'B.v.b college of engineering and technology'}]},\n",
       " {'label': ['Designation'],\n",
       "  'points': [{'start': 727,\n",
       "    'end': 769,\n",
       "    'text': 'B.E in Information science and engineering\\n'}]},\n",
       " {'label': ['Companies worked at'],\n",
       "  'points': [{'start': 407, 'end': 415, 'text': 'Accenture'}]},\n",
       " {'label': ['Designation'],\n",
       "  'points': [{'start': 372,\n",
       "    'end': 404,\n",
       "    'text': 'Application Development Associate'}]},\n",
       " {'label': ['Email Address'],\n",
       "  'points': [{'start': 95,\n",
       "    'end': 145,\n",
       "    'text': 'Indeed: indeed.com/r/Abhishek-Jha/10e7a8cb732bc43a\\n'}]},\n",
       " {'label': ['Location'],\n",
       "  'points': [{'start': 60, 'end': 68, 'text': 'Bengaluru'}]},\n",
       " {'label': ['Companies worked at'],\n",
       "  'points': [{'start': 49, 'end': 57, 'text': 'Accenture'}]},\n",
       " {'label': ['Designation'],\n",
       "  'points': [{'start': 13,\n",
       "    'end': 45,\n",
       "    'text': 'Application Development Associate'}]},\n",
       " {'label': ['Name'],\n",
       "  'points': [{'start': 0, 'end': 11, 'text': 'Abhishek Jha'}]}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.iloc[0]['annotation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mergeIntervals(intervals):\n",
    "    sorted_by_lower_bound = sorted(intervals, key=lambda tup: tup[0])\n",
    "    merged = []\n",
    "\n",
    "    for higher in sorted_by_lower_bound:\n",
    "        if not merged:\n",
    "            merged.append(higher)\n",
    "        else:\n",
    "            lower = merged[-1]\n",
    "            if higher[0] <= lower[1]:\n",
    "                if lower[2] is higher[2]:\n",
    "                    upper_bound = max(lower[1], higher[1])\n",
    "                    merged[-1] = (lower[0], upper_bound, lower[2])\n",
    "                else:\n",
    "                    if lower[1] > higher[1]:\n",
    "                        merged[-1] = lower\n",
    "                    else:\n",
    "                        merged[-1] = (lower[0], higher[1], higher[2])\n",
    "            else:\n",
    "                merged.append(higher)\n",
    "    return merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_entities(df):\n",
    "    \n",
    "    entities = []\n",
    "    \n",
    "    for i in range(len(df)):\n",
    "        entity = []\n",
    "    \n",
    "        for annot in df['annotation'][i]:\n",
    "            try:\n",
    "                ent = annot['label'][0]\n",
    "                start = annot['points'][0]['start']\n",
    "                end = annot['points'][0]['end'] + 1\n",
    "                entity.append((start, end, ent))\n",
    "            except:\n",
    "                pass\n",
    "    \n",
    "        entity = mergeIntervals(entity)\n",
    "        entities.append(entity)\n",
    "    \n",
    "    return entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>annotation</th>\n",
       "      <th>entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abhishek Jha Application Development Associate...</td>\n",
       "      <td>[{'label': ['Skills'], 'points': [{'start': 12...</td>\n",
       "      <td>[(0, 12, Name), (13, 46, Designation), (49, 58...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afreen Jamadar Active member of IIIT Committee...</td>\n",
       "      <td>[{'label': ['Email Address'], 'points': [{'sta...</td>\n",
       "      <td>[(0, 14, Name), (62, 68, Location), (104, 148,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akhil Yadav Polemaina Hyderabad, Telangana - E...</td>\n",
       "      <td>[{'label': ['Skills'], 'points': [{'start': 37...</td>\n",
       "      <td>[(0, 21, Name), (22, 31, Location), (65, 117, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alok Khandai Operational Analyst (SQL DBA) Eng...</td>\n",
       "      <td>[{'label': ['Skills'], 'points': [{'start': 80...</td>\n",
       "      <td>[(0, 12, Name), (13, 51, Designation), (54, 60...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ananya Chavan lecturer - oracle tutorials  Mum...</td>\n",
       "      <td>[{'label': ['Degree'], 'points': [{'start': 20...</td>\n",
       "      <td>[(0, 13, Name), (14, 22, Designation), (24, 41...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  \\\n",
       "0  Abhishek Jha Application Development Associate...   \n",
       "1  Afreen Jamadar Active member of IIIT Committee...   \n",
       "2  Akhil Yadav Polemaina Hyderabad, Telangana - E...   \n",
       "3  Alok Khandai Operational Analyst (SQL DBA) Eng...   \n",
       "4  Ananya Chavan lecturer - oracle tutorials  Mum...   \n",
       "\n",
       "                                          annotation  \\\n",
       "0  [{'label': ['Skills'], 'points': [{'start': 12...   \n",
       "1  [{'label': ['Email Address'], 'points': [{'sta...   \n",
       "2  [{'label': ['Skills'], 'points': [{'start': 37...   \n",
       "3  [{'label': ['Skills'], 'points': [{'start': 80...   \n",
       "4  [{'label': ['Degree'], 'points': [{'start': 20...   \n",
       "\n",
       "                                            entities  \n",
       "0  [(0, 12, Name), (13, 46, Designation), (49, 58...  \n",
       "1  [(0, 14, Name), (62, 68, Location), (104, 148,...  \n",
       "2  [(0, 21, Name), (22, 31, Location), (65, 117, ...  \n",
       "3  [(0, 12, Name), (13, 51, Designation), (54, 60...  \n",
       "4  [(0, 13, Name), (14, 22, Designation), (24, 41...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data['entities'] = get_entities(df_data)\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_dataturks_to_spacy(dataturks_JSON_FilePath):\n",
    "    try:\n",
    "        training_data = []\n",
    "        lines=[]\n",
    "        with open(dataturks_JSON_FilePath, 'r') as f:\n",
    "            lines = f.readlines()\n",
    "\n",
    "        for line in lines:\n",
    "            data = json.loads(line)\n",
    "            text = data['content'].replace(\"\\n\", \" \")\n",
    "            entities = []\n",
    "            data_annotations = data['annotation']\n",
    "            if data_annotations is not None:\n",
    "                for annotation in data_annotations:\n",
    "                    #only a single point in text annotation.\n",
    "                    point = annotation['points'][0]\n",
    "                    labels = annotation['label']\n",
    "                    # handle both list of labels or a single label.\n",
    "                    if not isinstance(labels, list):\n",
    "                        labels = [labels]\n",
    "\n",
    "                    for label in labels:\n",
    "                        point_start = point['start']\n",
    "                        point_end = point['end']\n",
    "                        point_text = point['text']\n",
    "                        \n",
    "                        lstrip_diff = len(point_text) - len(point_text.lstrip())\n",
    "                        rstrip_diff = len(point_text) - len(point_text.rstrip())\n",
    "                        if lstrip_diff != 0:\n",
    "                            point_start = point_start + lstrip_diff\n",
    "                        if rstrip_diff != 0:\n",
    "                            point_end = point_end - rstrip_diff\n",
    "                        entities.append((point_start, point_end + 1 , label))\n",
    "            training_data.append((text, {\"entities\" : entities}))\n",
    "        return training_data\n",
    "    except Exception as e:\n",
    "        logging.exception(\"Unable to process \" + dataturks_JSON_FilePath + \"\\n\" + \"error = \" + str(e))\n",
    "        return None\n",
    "\n",
    "def trim_entity_spans(data: list) -> list:\n",
    "    \"\"\"Removes leading and trailing white spaces from entity spans.\n",
    "\n",
    "    Args:\n",
    "        data (list): The data to be cleaned in spaCy JSON format.\n",
    "\n",
    "    Returns:\n",
    "        list: The cleaned data.\n",
    "    \"\"\"\n",
    "    invalid_span_tokens = re.compile(r'\\s')\n",
    "\n",
    "    cleaned_data = []\n",
    "    for text, annotations in data:\n",
    "        entities = annotations['entities']\n",
    "        valid_entities = []\n",
    "        for start, end, label in entities:\n",
    "            valid_start = start\n",
    "            valid_end = end\n",
    "            while valid_start < len(text) and invalid_span_tokens.match(\n",
    "                    text[valid_start]):\n",
    "                valid_start += 1\n",
    "            while valid_end > 1 and invalid_span_tokens.match(\n",
    "                    text[valid_end - 1]):\n",
    "                valid_end -= 1\n",
    "            valid_entities.append([valid_start, valid_end, label])\n",
    "        cleaned_data.append([text, {'entities': valid_entities}])\n",
    "    return cleaned_data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = trim_entity_spans(convert_dataturks_to_spacy(\"ner.json\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "def clean_dataset(data):\n",
    "    cleanedDF = pd.DataFrame(columns=[\"setences_cleaned\"])\n",
    "    sum1 = 0\n",
    "    for i in tqdm(range(len(data))):\n",
    "        start = 0\n",
    "        emptyList = [\"Empty\"] * len(data[i][0].split())\n",
    "        numberOfWords = 0\n",
    "        lenOfString = len(data[i][0])\n",
    "        strData = data[i][0]\n",
    "        strDictData = data[i][1]\n",
    "        lastIndexOfSpace = strData.rfind(' ')\n",
    "        for i in range(lenOfString):\n",
    "            if (strData[i]==\" \" and strData[i+1]!=\" \"):\n",
    "                for k,v in strDictData.items():\n",
    "                    for j in range(len(v)):\n",
    "                        entList = v[len(v)-j-1]\n",
    "                        if (start>=int(entList[0]) and i<=int(entList[1])):\n",
    "                            emptyList[numberOfWords] = entList[2]\n",
    "                            break\n",
    "                        else:\n",
    "                            continue\n",
    "                start = i + 1  \n",
    "                numberOfWords += 1\n",
    "            if (i == lastIndexOfSpace):\n",
    "                for j in range(len(v)):\n",
    "                        entList = v[len(v)-j-1]\n",
    "                        if (lastIndexOfSpace>=int(entList[0]) and lenOfString<=int(entList[1])):\n",
    "                            emptyList[numberOfWords] = entList[2]\n",
    "                            numberOfWords += 1\n",
    "        cleanedDF = cleanedDF.append(pd.Series([emptyList],  index=cleanedDF.columns ), ignore_index=True )\n",
    "        sum1 = sum1 + numberOfWords\n",
    "    return cleanedDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebceedc331074d479d4e8efd130ac1b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/220 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cleanedDF = clean_dataset(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the cleaned dataset and the categories the named-entities are matched to, or 'tags'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setences_cleaned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Name, Name, Designation, Designation, Designa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Name, Name, Empty, Empty, Empty, Empty, Empty...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Name, Name, Name, Empty, Empty, Empty, Empty,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Name, Name, Designation, Designation, Designa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Name, Name, Designation, Empty, Companies wor...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    setences_cleaned\n",
       "0  [Name, Name, Designation, Designation, Designa...\n",
       "1  [Name, Name, Empty, Empty, Empty, Empty, Empty...\n",
       "2  [Name, Name, Name, Empty, Empty, Empty, Empty,...\n",
       "3  [Name, Name, Designation, Designation, Designa...\n",
       "4  [Name, Name, Designation, Empty, Companies wor..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleanedDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1-2'></a>\n",
    "### 1.2 - Padding and Generating Tags\n",
    "\n",
    "To generate a list of unique tags to match the named-entities to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_tags = set(cleanedDF['setences_cleaned'].explode().unique())#pd.unique(cleanedDF['setences_cleaned'])#set(tag for doc in cleanedDF['setences_cleaned'].values.tolist() for tag in doc)\n",
    "tag2id = {tag: id for id, tag in enumerate(unique_tags)}\n",
    "id2tag = {id: tag for tag, id in tag2id.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'College Name',\n",
       " 'Companies worked at',\n",
       " 'Degree',\n",
       " 'Designation',\n",
       " 'Email Address',\n",
       " 'Empty',\n",
       " 'Graduation Year',\n",
       " 'Location',\n",
       " 'Name',\n",
       " 'Skills',\n",
       " 'UNKNOWN',\n",
       " 'Years of Experience'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create an array of tags from the now cleaned dataset. Oftentimes the input sequence will exceed the maximum length of a sequence your network can process. In this case, the sequence will be cut off, and we need to append zeroes onto the end of the shortened sequences using this [Keras padding API](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LEN = 512\n",
    "labels = cleanedDF['setences_cleaned'].values.tolist()\n",
    "\n",
    "tags = pad_sequences([[tag2id.get(l) for l in lab] for lab in labels],\n",
    "                     maxlen=MAX_LEN, value=tag2id[\"Empty\"], padding=\"post\",\n",
    "                     dtype=\"long\", truncating=\"post\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10,  6, ...,  2,  2,  2],\n",
       "       [10, 10,  2, ...,  2,  2,  2],\n",
       "       [10, 10, 10, ...,  2,  5,  2],\n",
       "       ...,\n",
       "       [10, 10,  6, ...,  2,  2,  2],\n",
       "       [10, 10,  6, ...,  2,  2,  2],\n",
       "       [10, 10,  6, ...,  2,  2,  2]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1-3'></a>\n",
    "### 1.3 - Tokenize and Align Labels with 🤗 Library\n",
    "\n",
    "Before feeding the texts to a Transformer model, we need to tokenize the input using a [🤗 Transformer tokenizer](https://huggingface.co/transformers/main_classes/tokenizer.html). It is crucial that the tokenizer we use must match the Transformer model type being used! Here, we will use the 🤗 [DistilBERT fast tokenizer](https://huggingface.co/transformers/model_doc/distilbert.html), which standardizes the length of your sequence to 512 and pads with zeros. Notice this matches the maximum length we used when creating tags. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "edittable": false
   },
   "outputs": [],
   "source": [
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    for gpu in gpus:\n",
    "        tf.config.experimental.set_virtual_device_configuration(gpu,[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DistilBertTokenizerFast #, TFDistilBertModel\n",
    "tokenizer = DistilBertTokenizerFast.from_pretrained('tokenizer/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transformer models are often trained by tokenizers that split words into subwords. For instance, the word 'Germany' might get split into multiple subtokens. This can create some misalignment between the list of tags for the dataset and the list of labels generated by the tokenizer, since the tokenizer can split one word into several, or add special tokens. Before processing, it is important that we align the lists of tags and the list of labels generated by the selected tokenizer with a `tokenize_and_align_labels()` function.\n",
    "\n",
    "<a name='ex-1'></a>\n",
    "### tokenize_and_align_labels\n",
    "\n",
    "Implement `tokenize_and_align_labels()`. The function should perform the following:\n",
    "* The tokenizer cuts sequences that exceed the maximum size allowed by your model with the parameter `truncation=True`\n",
    "* Aligns the list of tags and labels with the tokenizer `word_ids` method returns a list that maps the subtokens to the original word in the sentence and special tokens to `None`. \n",
    "* Set the labels of all the special tokens (`None`) to -100 to prevent them from affecting the loss function. \n",
    "* Label of the first subtoken of a word and set the label for the following subtokens to -100. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_all_tokens = True\n",
    "def tokenize_and_align_labels(tokenizer, examples, tags):\n",
    "    tokenized_inputs = tokenizer(examples, truncation=True, is_split_into_words=False, padding='max_length', max_length=512)\n",
    "    labels = []\n",
    "    for i, label in enumerate(tags):\n",
    "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
    "        previous_word_idx = None\n",
    "        label_ids = []\n",
    "        for word_idx in word_ids:\n",
    "            # Special tokens have a word id that is None. We set the label to -100 so they are automatically\n",
    "            # ignored in the loss function.\n",
    "            if word_idx is None:\n",
    "                label_ids.append(-100)\n",
    "            # We set the label for the first token of each word.\n",
    "            elif word_idx != previous_word_idx:\n",
    "                label_ids.append(label[word_idx])\n",
    "            # For the other tokens in a word, we set the label to either the current label or -100, depending on\n",
    "            # the label_all_tokens flag.\n",
    "            else:\n",
    "                label_ids.append(label[word_idx] if label_all_tokens else -100)\n",
    "            previous_word_idx = word_idx\n",
    "\n",
    "        labels.append(label_ids)\n",
    "\n",
    "    tokenized_inputs[\"labels\"] = labels\n",
    "    return tokenized_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have tokenized inputs, we can create train and test datasets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = tokenize_and_align_labels(tokenizer, df_data['content'].values.tolist(), tags)\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
    "    test['input_ids'],\n",
    "    test['labels']\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1-4'></a>\n",
    "### 1.4 - Optimization\n",
    "\n",
    "We can finally feed the data into into a pretrained 🤗 model. We will optimize a DistilBERT model, which matches the tokenizer we used to preprocess the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFDistilBertForTokenClassification.\n",
      "\n",
      "All the layers of TFDistilBertForTokenClassification were initialized from the model checkpoint at model/.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForTokenClassification for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers import TFDistilBertForTokenClassification\n",
    "\n",
    "model = TFDistilBertForTokenClassification.from_pretrained('model/', num_labels=len(unique_tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "55/55 [==============================] - 14s 93ms/step - loss: 0.8163 - accuracy: 0.7162\n",
      "Epoch 2/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.4755 - accuracy: 0.7538\n",
      "Epoch 3/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.4298 - accuracy: 0.7606\n",
      "Epoch 4/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.4010 - accuracy: 0.7621\n",
      "Epoch 5/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.3783 - accuracy: 0.7626\n",
      "Epoch 6/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.3563 - accuracy: 0.7662\n",
      "Epoch 7/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.3387 - accuracy: 0.7696\n",
      "Epoch 8/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.3259 - accuracy: 0.7725\n",
      "Epoch 9/10\n",
      "55/55 [==============================] - 5s 93ms/step - loss: 0.3215 - accuracy: 0.7755\n",
      "Epoch 10/10\n",
      "55/55 [==============================] - 5s 92ms/step - loss: 0.3043 - accuracy: 0.7784\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f33fe33db80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5)\n",
    "model.compile(optimizer=optimizer, loss=model.hf_compute_loss, metrics=['accuracy']) # can also use any keras loss fn\n",
    "model.fit(train_dataset.batch(4),\n",
    "          epochs=10, \n",
    "          batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"Manisha Bharti. 3.5 years of professional IT experience in Banking and Finance domain\"\n",
    "inputs = tokenizer(text, return_tensors=\"tf\", truncation=True, is_split_into_words=False, padding=\"max_length\", max_length=512 )\n",
    "input_ids = inputs[\"input_ids\"]\n",
    "#inputs[\"labels\"] = tf.reshape(tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2 10 10 10 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2 10  2  2  2\n",
      "   2  2 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2 10  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2 10  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2 10  2  2  2 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2 10  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2 10  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2 10  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "  10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2 10  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2\n",
      "   2  2  2  2  2  2  2  2]]\n"
     ]
    }
   ],
   "source": [
    "output = model(inputs).logits\n",
    "prediction = np.argmax(output, axis=2)\n",
    "print( prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TFTokenClassifierOutput(loss=None, logits=<tf.Tensor: shape=(1, 512, 12), dtype=float32, numpy=\n",
       "array([[[-0.6516601 , -0.37835136,  1.5768892 , ...,  0.07997867,\n",
       "          0.44134203, -0.16031717],\n",
       "        [-0.6105441 , -0.68149674,  0.9744507 , ..., -0.01203116,\n",
       "          1.8909634 , -0.13952908],\n",
       "        [-0.7441909 , -0.9323001 ,  1.3811982 , ..., -0.15127942,\n",
       "          1.9903725 , -0.24406867],\n",
       "        ...,\n",
       "        [-0.79348606, -0.61279404,  1.4557046 , ...,  0.09894287,\n",
       "         -0.40713027,  0.13012731],\n",
       "        [-0.701042  , -0.430378  ,  1.4507649 , ...,  0.08550031,\n",
       "         -0.41010368,  0.12222695],\n",
       "        [-0.83250594, -0.5958986 ,  1.5163158 , ...,  0.02876091,\n",
       "         -0.4220398 ,  0.20200033]]], dtype=float32)>, hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seqeval\n",
      "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.22.4)\n",
      "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.2.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.9.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n",
      "Building wheels for collected packages: seqeval\n",
      "  Building wheel for seqeval (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16166 sha256=e43b44d83f0b096e8baa3694e00db21f0d7c9faf166615b66c464abc0eb5fd31\n",
      "  Stored in directory: /root/.cache/pip/wheels/e3/30/9b/6b670dac34775f2b7cc4e9b172202e81fbb4f9cdb103c1ca66\n",
      "Successfully built seqeval\n",
      "Installing collected packages: seqeval\n",
      "Successfully installed seqeval-1.2.2\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\u001b[33m\n",
      "\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install seqeval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(220, 512)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_labels = [[id2tag.get(true_index, \"Empty\") for true_index in test['labels'][i]] for i in range(len(test['labels']))]\n",
    "np.array(true_labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220/220 [==============================] - 2s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "output = model.predict(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = np.argmax(output['logits'].reshape(220, -1, 12), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(220, 512)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "\n",
    "p = plt.hist(np.array(true_labels).flatten())\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Empty': 103155,\n",
       "         'Name': 1035,\n",
       "         'Designation': 1100,\n",
       "         'Email Address': 76,\n",
       "         'Companies worked at': 908,\n",
       "         'College Name': 792,\n",
       "         'Skills': 4704,\n",
       "         'Degree': 598,\n",
       "         'Graduation Year': 80,\n",
       "         'Location': 116,\n",
       "         'Years of Experience': 75,\n",
       "         'UNKNOWN': 1})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(np.array(true_labels).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = [[id2tag.get(index, \"Empty\") for index in predictions[i]] for i in range(len(predictions))]\n",
    "p = plt.hist(np.array(pred_labels).flatten())\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    precision    recall  f1-score   support\n",
      "\n",
      "            NKNOWN       0.00      0.00      0.00         1\n",
      "               ame       0.62      0.63      0.62       220\n",
      "ears of Experience       0.00      0.00      0.00        37\n",
      "             egree       0.00      0.00      0.00       144\n",
      "        esignation       0.40      0.20      0.27       430\n",
      "             kills       0.78      0.54      0.64      4704\n",
      "      mail Address       0.00      0.00      0.00        76\n",
      "              mpty       0.95      0.99      0.97    103155\n",
      "           ocation       0.00      0.00      0.00        73\n",
      "       ollege Name       0.00      0.00      0.00       214\n",
      "ompanies worked at       0.00      0.00      0.00       470\n",
      "    raduation Year       0.00      0.00      0.00        58\n",
      "\n",
      "         micro avg       0.94      0.96      0.95    109582\n",
      "         macro avg       0.23      0.20      0.21    109582\n",
      "      weighted avg       0.93      0.96      0.94    109582\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from seqeval.metrics import classification_report\n",
    "print(classification_report(true_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}