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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCRwpV32_ZaF",
        "outputId": "98ab5dc5-f905-4aea-a5f6-713c61e717e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Aug 31 01:27:55 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   54C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers -q"
      ],
      "metadata": {
        "id": "-bw70pKh_ci-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f97920a3-1fbd-466a-bd47-1a275f6005aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.7 MB 4.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 120 kB 72.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 59.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets -q"
      ],
      "metadata": {
        "id": "L19IzygE_t5y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd46c07d-f1e0-42f0-f9f2-4104dff767b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 365 kB 5.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 62.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 68.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 70.9 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Creating Data"
      ],
      "metadata": {
        "id": "6BApzyMn_vqX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/csebuetnlp/banglabert/master/question_answering/sample_inputs/train.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqkiIZLB_0Pr",
        "outputId": "9af3d944-4006-4b12-c846-92eb74ffe62c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-31 01:28:43--  https://raw.githubusercontent.com/csebuetnlp/banglabert/master/question_answering/sample_inputs/train.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4562448 (4.4M) [text/plain]\n",
            "Saving to: ‘train.json’\n",
            "\n",
            "train.json          100%[===================>]   4.35M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2022-08-31 01:28:43 (57.7 MB/s) - ‘train.json’ saved [4562448/4562448]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/csebuetnlp/banglabert/master/question_answering/sample_inputs/test.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwB87pu2_-2M",
        "outputId": "7bc4cc25-1aeb-42c8-fee4-23cfc50dead0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-31 01:28:43--  https://raw.githubusercontent.com/csebuetnlp/banglabert/master/question_answering/sample_inputs/test.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4562448 (4.4M) [text/plain]\n",
            "Saving to: ‘test.json’\n",
            "\n",
            "test.json           100%[===================>]   4.35M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2022-08-31 01:28:44 (61.0 MB/s) - ‘test.json’ saved [4562448/4562448]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/csebuetnlp/banglabert/master/question_answering/sample_inputs/validation.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kcam3g8-Am4f",
        "outputId": "d3479c30-1c05-4412-d670-8229ac57611a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-31 01:28:44--  https://raw.githubusercontent.com/csebuetnlp/banglabert/master/question_answering/sample_inputs/validation.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4562448 (4.4M) [text/plain]\n",
            "Saving to: ‘validation.json’\n",
            "\n",
            "validation.json     100%[===================>]   4.35M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2022-08-31 01:28:44 (58.9 MB/s) - ‘validation.json’ saved [4562448/4562448]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from datasets import load_dataset\n",
        "\n",
        "\n",
        "\n",
        "def convert_to_jsonl(input_filename, output_filename):\n",
        "\n",
        "  with open(input_filename) as f:\n",
        "      dataset = json.load(f)\n",
        "\n",
        "  with open(output_filename, \"w\") as f:\n",
        "      for article in dataset[\"data\"]:\n",
        "          title = article[\"title\"]\n",
        "          for paragraph in article[\"paragraphs\"]:\n",
        "              context = paragraph[\"context\"]\n",
        "              answers = {}\n",
        "              for qa in paragraph[\"qas\"]:\n",
        "                  question = qa[\"question\"]\n",
        "                  idx = qa[\"id\"]\n",
        "                  answers[\"text\"] = [a[\"text\"] for a in qa[\"answers\"]]\n",
        "                  answers[\"answer_start\"] = [a[\"answer_start\"] for a in qa[\"answers\"]]\n",
        "                  f.write(\n",
        "                      json.dumps(\n",
        "                          {\n",
        "                              \"id\": idx,\n",
        "                              \"title\": title,\n",
        "                              \"context\": context,\n",
        "                              \"question\": question,\n",
        "                              \"answers\": answers,\n",
        "                          }\n",
        "                      )\n",
        "                  )\n",
        "                  f.write(\"\\n\")\n",
        "\n"
      ],
      "metadata": {
        "id": "ShqRkOG_DZFo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "convert_to_jsonl(\"train.json\",\"train.jsonl\")"
      ],
      "metadata": {
        "id": "z78isO8yEBgu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "convert_to_jsonl(\"test.json\",\"test.jsonl\")"
      ],
      "metadata": {
        "id": "_hznTotXEYia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "convert_to_jsonl(\"validation.json\",\"validaiton.jsonl\")"
      ],
      "metadata": {
        "id": "d0_XC4rtLi8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dataclasses import field\n",
        "from datasets import load_dataset\n",
        "\n",
        "data_files = {\n",
        "    \"train\":\"train.jsonl\",\n",
        "    \"test\":\"test.jsonl\",\n",
        "    \"validation\":\"validaiton.jsonl\"\n",
        "}\n",
        "\n",
        "raw_datasets = load_dataset(\"json\",data_files = data_files)\n",
        "raw_datasets"
      ],
      "metadata": {
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          "height": 442,
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        "id": "G5lwXiT1Arte",
        "outputId": "abc19942-01cc-49dd-9741-5af4f5adbe1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-a9602a990ea80b55\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-a9602a990ea80b55/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "943f2dc22bc846cc915c3dc2954147e5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70b72c2655a841a3b4b35b6bc343159e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
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          "metadata": {}
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        {
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          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-a9602a990ea80b55/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253. Subsequent calls will reuse this data.\n"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44bd2c742e6e4aeeb37902546fcf1a27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 2504\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 2504\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 2504\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets[\"train\"][2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frhK6JqAPdK9",
        "outputId": "c9fa71be-0dd9-415b-b59d-3b90b6d44d27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 'bengali--981248442377505718-0-2649',\n",
              " 'title': 'শেখ মুজিবুর রহমান',\n",
              " 'context': 'শেখ মুজিবুর রহমান (১৭ মার্চ ১৯২০ - ১৫ আগস্ট ১৯৭৫) বাংলাদেশের প্রথম রাষ্ট্রপতি ও ভারতীয় উপমহাদেশের একজন অন্যতম প্রভাবশালী রাজনৈতিক ব্যক্তিত্ব যিনি বাঙালীর অধিকার রক্ষায় ব্রিটিশ ভারত থেকে ভারত বিভাজন আন্দোলন এবং পরবর্তীতে  পূর্ব পাকিস্তান থেকে বাংলাদেশ প্রতিষ্ঠার সংগ্রামে নেতৃত্ব প্রদান করেন। প্রাচীন বাঙ্গালি সভ্যতার আধুনিক স্থপতি হিসাবে শেখ মুজিবুর রহমানকে বাংলাদেশের জাতির জনক বা জাতির পিতা বলা হয়ে থাকে। তিনি মাওলানা আব্দুল হামিদ খান ভাসানী প্রতিষ্ঠিত আওয়ামী লীগের সভাপতি, বাংলাদেশের প্রথম রাষ্ট্রপতি এবং পরবর্তীতে এদেশের প্রধানমন্ত্রীর দায়িত্ব পালন করেন। জনসাধারণের কাছে তিনি শেখ মুজিব এবং শেখ সাহেব হিসাবে বেশি পরিচিত এবং তার উপাধি বঙ্গবন্ধু। তার কন্যা শেখ হাসিনা বাংলাদেশ আওয়ামী লীগের বর্তমান সভানেত্রী এবং বাংলাদেশের বর্তমান প্রধানমন্ত্রী।',\n",
              " 'question': 'শেখ মুজিবুর রহমান কবে জন্মগ্রহণ করেন ?',\n",
              " 'answers': {'text': ['১৭ মার্চ ১৯২০'], 'answer_start': [19]}}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import ClassLabel, Sequence\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "\n",
        "def show_random_elements(dataset, num_examples=10):\n",
        "    assert num_examples <= len(\n",
        "        dataset\n",
        "    ), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset) - 1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset) - 1)\n",
        "        picks.append(pick)\n",
        "\n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "        elif isinstance(typ, Sequence) and isinstance(typ.feature, ClassLabel):\n",
        "            df[column] = df[column].transform(\n",
        "                lambda x: [typ.feature.names[i] for i in x]\n",
        "            )\n",
        "    display(HTML(df.to_html()))"
      ],
      "metadata": {
        "id": "ph1JIGmuPhf5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(raw_datasets[\"train\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "m1MVicGyBjZ4",
        "outputId": "692d06cd-79ba-4961-b368-216031b2799d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>context</th>\n",
              "      <th>question</th>\n",
              "      <th>answers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>bengali-8917570501792524460-34-3072</td>\n",
              "      <td>কাজী নজরুল ইসলাম</td>\n",
              "      <td>১৯২০ এর দশকের জাতীয় পরিষদের নির্বাচনে তিনি অংশগ্রহণের চেষ্টা করেন। প্রথমে কংগ্রেসে সমর্থন লাভের জন্য তিনি কলকাতা যান। কিন্তু কংগ্রেসের কাছ থেকে তেমন সাড়া না পেয়ে তিনি একাই নির্বাচন করার সিদ্ধান্ত নেন। নির্বাচনে তিনি তেমন সাফল্য পান নি। এরপর সাহিত্যের মাধ্যমে তাঁর রাজনৈতিক চিন্তার বহিঃপ্রকাশ অব্যাহত থাকলেও রাজনীতিতে সক্রিয় অংশগ্রহণ কমে যায়।[14]</td>\n",
              "      <td>উপেন্দ্রকিশোর রায় চৌধুরী মোট কতগুলো শিশুসাহিত্য রচনা করেন ?</td>\n",
              "      <td>{'text': [], 'answer_start': []}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>bengali-1349183373529087639-10-2868</td>\n",
              "      <td>ক্রিকেট বিশ্বকাপ ফাইনালের তালিকা</td>\n",
              "      <td>১৯৯৯ সালের বিশ্বকাপ প্রতিযোগিতার মাধ্যমে ১২-বছরব্যাপী অস্ট্রেলিয়ার আধিপত্যের সূচনা ঘটে।[29] এ প্রতিযোগিতাটি ইংল্যান্ডে চতুর্থবারের মতো অনুষ্ঠিত হয়। একচেটিয়াভাবে অনুষ্ঠিত ফাইনাল খেলাটিতে[30] শেন ওয়ার্ন চার উইকেট লাভ করলে পাকিস্তানের রানসংখ্যা ১১ ওভার বাকী থাকতেই মাত্র ১৩২ হয়।[31] অ্যাডাম গিলক্রিস্টের দূর্দান্ত অর্ধ-শতক, মার্ক ওয়াহ, রিকি পন্টিং ও ড্যারেন লেহম্যানের সহযোগিতায় অস্ট্রেলিয়া মাত্র ২০ ওভারের মধ্যেই লক্ষ্যে পৌঁছে যায়। ফলে, অস্ট্রেলিয়া দল আট উইকেটের ব্যবধানে জয়ী হয় ও শিরোপা জয় করে।[31] তারকা খেলোয়াড় শেন ওয়ার্ন বোলিংয়ে অনন্য সাধারণ ক্রীড়াশৈলী প্রদর্শনের মাধ্যমে ম্যান অব দ্য ম্যাচের পুরস্কার লাভ করেন। ম্যান অব দ্য সিরিজ নির্বাচিত হন দক্ষিণ আফ্রিকান খেলোয়াড় ল্যান্স ক্লুজনার।</td>\n",
              "      <td>অস্ট্রেলিয়ার সর্বপ্রথম আন্তর্জাতিক ক্রিকেট দলের অধিনায়ক কে ছিলেন ?</td>\n",
              "      <td>{'text': [], 'answer_start': []}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>bengali--385656867682208009-26-3806</td>\n",
              "      <td>শিব</td>\n",
              "      <td>শতরুদ্রীয় স্তবে \"সসিপঞ্জর\" (\"সোনালি লাল রঙের শিখার মতো আভাযুক্ত\") ও \"তিবষীমতি\" (\"জ্বলন্ত শিখা\") বিশেষণদুটি রুদ্র ও অগ্নির সমরূপত্ব নির্দেশ করছে।[60] অগ্নিকে ষাঁড়ের রূপে কল্পনা করা হয়ে থাকে,[61] এবং শিবের বাহনও হল নন্দী নামে একটি ষাঁড়। ষাঁড়ের মতো অগ্নিরও শিং কল্পনা করা হয়ে থাকে।[62][63] মধ্যযুগীয় ভাস্কর্যে অগ্নি ও ভৈরব শিব – উভয়েরই বিশেষত্ব হল অগ্নিশিখার ন্যায় মুক্ত কেশরাশি।[64]</td>\n",
              "      <td>হিন্দু শাস্ত্র মতে শিবের বাহন কে ?</td>\n",
              "      <td>{'text': ['নন্দী নামে একটি ষাঁড়'], 'answer_start': [216]}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bengali--981248442377505718-0-2649</td>\n",
              "      <td>শেখ মুজিবুর রহমান</td>\n",
              "      <td>শেখ মুজিবুর রহমান (১৭ মার্চ ১৯২০ - ১৫ আগস্ট ১৯৭৫) বাংলাদেশের প্রথম রাষ্ট্রপতি ও ভারতীয় উপমহাদেশের একজন অন্যতম প্রভাবশালী রাজনৈতিক ব্যক্তিত্ব যিনি বাঙালীর অধিকার রক্ষায় ব্রিটিশ ভারত থেকে ভারত বিভাজন আন্দোলন এবং পরবর্তীতে  পূর্ব পাকিস্তান থেকে বাংলাদেশ প্রতিষ্ঠার সংগ্রামে নেতৃত্ব প্রদান করেন। প্রাচীন বাঙ্গালি সভ্যতার আধুনিক স্থপতি হিসাবে শেখ মুজিবুর রহমানকে বাংলাদেশের জাতির জনক বা জাতির পিতা বলা হয়ে থাকে। তিনি মাওলানা আব্দুল হামিদ খান ভাসানী প্রতিষ্ঠিত আওয়ামী লীগের সভাপতি, বাংলাদেশের প্রথম রাষ্ট্রপতি এবং পরবর্তীতে এদেশের প্রধানমন্ত্রীর দায়িত্ব পালন করেন। জনসাধারণের কাছে তিনি শেখ মুজিব এবং শেখ সাহেব হিসাবে বেশি পরিচিত এবং তার উপাধি বঙ্গবন্ধু। তার কন্যা শেখ হাসিনা বাংলাদেশ আওয়ামী লীগের বর্তমান সভানেত্রী এবং বাংলাদেশের বর্তমান প্রধানমন্ত্রী।</td>\n",
              "      <td>শেখ মুজিবুর রহমান কবে জন্মগ্রহণ করেন ?</td>\n",
              "      <td>{'text': ['১৭ মার্চ ১৯২০'], 'answer_start': [19]}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bengali--2660184311490318740-0-4762</td>\n",
              "      <td>অভিজিৎ রায়</td>\n",
              "      <td>অভিজিৎ রায় (১২ সেপ্টেম্বর ১৯৭২ - ২৬ ফেব্রুয়ারি ২০১৫))[1] একজন বাংলাদেশি বংশোদ্ভূত বাংলাদেশী-মার্কিন প্রকৌশলী, লেখক ও ব্লগার।[2] তিনি বাংলাদেশের মুক্ত চিন্তার আন্দোলনের সাথে জড়িত ছিলেন। তিনি বাংলাদেশে সরকারের সেন্সরশিপ এবং ব্লগারদের কারাদণ্ডের বিরুদ্ধে আন্তর্জাতিক প্রতিবাদের সমন্বয়কারক ছিলেন। তিনি পেশায় একজন প্রকৌশলী হলেও তার স্ব-প্রতিষ্ঠিত ওয়েবসাইট মুক্তমনায় লেখালেখির জন্য অধিক পরিচিত ছিলেন। ২০১৫ সালের ফেব্রুয়ারি মাসে একুশে বইমেলা থেকে বের হওয়ার সময় অজ্ঞাত সন্ত্রাসীরা তাকে কুপিয়ে হত্যা ও তার স্ত্রী রাফিদা আহমেদ বন্যা কে আহত করে।[3][4]</td>\n",
              "      <td>অভিজিৎ রায়ের ব্লগের নাম কী ?</td>\n",
              "      <td>{'text': ['মুক্তমনায়', 'মুক্তমনা'], 'answer_start': [357, 357]}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bengali-7720086356526517535-3-3603</td>\n",
              "      <td>শাবনূর</td>\n",
              "      <td>শাবনূরের প্রথম চলচ্চিত্র চাঁদনী রাতে ১৯৯৩ সালে মুক্তি পায়। ছবিটি পরিচালনা করেন এহতেশাম এবং তার বিপরীতে নায়ক ছিল সাব্বির। এই ছবিটি ব্যর্থ হয়। পরে চিত্র নায়ক সালমান শাহের সাথে জুটি বেধে ১৪টি চলচ্চিত্রে অভিনয় করেন যার প্রায় সবগুলোই ছিল ব্যবসায়িক মানদন্ডে সফল।[1] সালমান শাহ-শাবনূর জুটির প্রথম ছায়াছবি জহিরুল হক পরিচালিত তুমি আমার ১৯৯৪ সালে মুক্তি পায়। একই বছর শাহ আলম কিরণ তাদের নিয়ে ফারুক-কবরী জুটির সুজন সখী চলচ্চিত্রের রঙিন পুনঃনির্মাণ সুজন সখি নির্মাণ করেন। ১৯৯৫ সালে স্বপ্নের ঠিকানা, ১৯৯৬ সালে স্বপ্নের পৃথিবী, তোমাকে চাই, ১৯৯৭ সালে শিবলি সাদিক পরিচালিত আনন্দ অশ্রু এ জুটির উল্লেখযোগ্য ছবি।[2] পরে তিনি নায়ক রিয়াজ এর বিপরীতে অভিনয় করে  দারুন সফলতা অর্জন করেন। রিয়াজের বিপরীতে ১৯৯৭ সালে মন মানেনা ও তুমি শুধু তুমি এবং ১৯৯৯ সালে অভিনীত ভালবাসি তোমাকে ও বিয়ের ফুল ছায়াছবিগুলো ব্যবসা সফল হয়। পাশাপাশি তিনি অভিনেতা ফেরদৌসের সাথেও সফল হন। এছাড়াও তিনি মান্না, শাকিব খানের সাথে অভিনয় করে সফল হয়েছেন। এসময়ে তিনি ভালবাসি তোমাকে চলচ্চিত্রের জন্য শ্রেষ্ঠ অভিনেত্রী বিভাগে বাচসাস পুরস্কার লাভ করেন এবং ১৯৯৮ ও ১৯৯৯ সালের চলচ্চিত্রের জন্য পর্যন্ত টানা দুইবার তারকা জরিপে শ্রেষ্ঠ চলচ্চিত্র অভিনেত্রী বিভাগে মেরিল-প্রথম আলো পুরস্কার লাভ করেন।</td>\n",
              "      <td>বাংলাদেশের জনপ্রিয় অভিনেত্রী শাবনূরের অভিনীত প্রথম চলচ্চিত্রের নাম কী ?</td>\n",
              "      <td>{'text': ['চাঁদনী রাতে'], 'answer_start': [25]}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>bengali-5532376808423421416-10-2988</td>\n",
              "      <td>বাংলাদেশী টাকা</td>\n",
              "      <td>১৯৮০ থেকে ১৯৮৩ সালের মধ্যে অর্থপ্রদান ভারসাম্যের অবনতির কারণে টাকা মূল্যে আরো ৫০% পতন দেখা গেল।[4] ১৯৮৫ থেকে ১৯৮৭ সালে, টাকা অসংখ্য ক্রমবর্ধমান পদক্ষেপে মার্কিন ডলারের বিরুদ্ধে ১২% স্থিতিশীল করা হয়েছিল। কিন্তু একই সময়ে সরকারি বিনিময় হার এবং অগ্রাধিকারভিত্তিক মাধ্যমিক বিনিময় হারের মধ্যে পার্থক্য ৭.৫% থেকে ১৫% সংকীর্ণ হল।[4] এই কাঠামোগত সমন্বয়ে অগ্রাধিকারভিত্তিক মাধ্যমিক বিনিময় হারের পরিচালিত বাণিজ্যে মোট রপ্তানির ৫৩% এবং মোট আমদানির ২৮% বিস্তার ও উন্নতি দেখা গেল।[4] ১৯৮৭ সালে সরকারি বিনিময় হার তুলনামূলক স্থিতিশীল ছিল, ৳৩১ প্রতি ১ মার্কিন ডলার চেয়ে কম।[4]</td>\n",
              "      <td>নেপালের সরকারি মুদ্রার প্রচলন প্রথম কোন সাল থেকে শুরু হয় ?</td>\n",
              "      <td>{'text': [], 'answer_start': []}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>bengali-5050156879273265481-0-4746</td>\n",
              "      <td>ইলিয়াড</td>\n",
              "      <td>ইলিয়াড গ্রিক মহাকাব্য। প্রাচীন গ্রিসের ইলিওন শহরের নামানুসারে এই মহাকাব্যের নামকরণ করা হয়। মহাকবি হোমার এই মহাকাব্যের রচয়িতা। এটি গ্রিক ভাষায় রচিত ও ২৪ টি সর্গে বিভক্ত। এর বিষয় ট্রয়ের যুদ্ধ। এতে ১৬,০০০ পঙ্‌ক্তি কবিতা আছে। যুদ্ধ সংঘটিত হয় এক নারীকে কেন্দ্র করে যার নাম হেলেন। যুদ্ধ এ গ্রীকদের সেরা বীর ছিল অ্যাকিলিস আর ট্রয় পক্ষে ছিল হেক্টর। যুদ্ধ যখন শেষ পর্যায় তখন হেক্টর অ্যাকিলিস কর্তৃক নিহত হন এবং এর মাধ্যমে মূলত ট্রয়বাসীর পরাজয় নিশ্চিত হয়। যুদ্ধ শেষে গ্রিক সেনারা সুরক্ষিত ও সাজানো নগরী ট্রয় জ্বালিয়ে দেয়।</td>\n",
              "      <td>কোন শহরের নামানুসারে ইলিয়াড মহাকাব্যের নামকরণ করা হয় ?</td>\n",
              "      <td>{'text': ['ইলিওন', 'ইলিওন'], 'answer_start': [42, 42]}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>bengali--497186371677567635-0-3945</td>\n",
              "      <td>ভাগবত পুরাণ</td>\n",
              "      <td>ভাগবত পুরাণ (দেবনাগরী: भागवतपुराण; অন্যান্য নাম শ্রীমদ্ভাগবত মহাপুরাণ, শ্রীমদ্ভাগবতম্‌ বা ভাগবত; অর্থাৎ, পরমেশ্বরের পবিত্র কাহিনি) হল একটি হিন্দু মহাপুরাণ। এটি একটি ভক্তিবাদী ধর্মগ্রন্থ। বিষ্ণুর পূর্ণ অবতার তথা \"স্বয়ং ভগবান\" কৃষ্ণের প্রতি গভীর ব্যক্তিগত ভক্তিই এই পুরাণের প্রধান আলোচ্য বিষয়।[1] হিন্দু পৌরাণিক সাহিত্যের অনেক কাহিনি তথা বিষ্ণুর চব্বিশ জন অবতারের কাহিনি ভাগবত পুরাণে লিপিবদ্ধ রয়েছে। ভাগবত পুরাণ&lt;/i&gt;ই প্রথম পুরাণ যেটি কোনো ইউরোপীয় ভাষায় অনূদিত হয়। ১৮৪০ থেকে ১৮৫৭ সালের মধ্যে ভাগবত পুরাণে&lt;/i&gt;র তিনটি ফরাসি অনুবাদ প্রকাশিত হয়।[2] পদ্মপুরাণের শ্রেণিবিন্যাস অনুসারে ভাগবত পুরাণ একটি সাত্ত্বিক পুরাণ (অর্থাৎ, এই পুরাণ কল্যাণ ও পবিত্রতার সঙ্গে যুক্ত)।[3] প্রচলিত হিন্দু বিশ্বাস অনুসারে, ব্যাস এই পুরাণের রচয়িতা।</td>\n",
              "      <td>ভাগবত পুরাণ কোন ভাষায় রচিত হয় ?</td>\n",
              "      <td>{'text': ['দেবনাগরী'], 'answer_start': [13]}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>bengali-2203029550064040859-16-2650</td>\n",
              "      <td>আওরঙ্গজেব</td>\n",
              "      <td>কিছুটা সুস্থ হয়ে শাহজাহান আগ্রায় ফিরে আসেন। দারাশিকো তাকে মুরাদ ও সুজার বিরুদ্ধে সৈন্যে প্রেরনের জন্য অনুরোধ করেন। তারা ইতিমধ্যে তাদের নিজ নিজ প্রদেশের স্বাধীন শাসক হিসাবে নিজেদেরকে ঘোষনা করেছিলেন। ইতমধ্যে ১৬৫৮ সালের ফেব্রুয়ারি মাসে শাহজাদা শাহ সুজা বেনারসে দারা শিকোর বাহিনীর কাছে পরাজিত হন। মুরাদের বিরুদ্ধে প্রেরিত বাহিনী আবিষ্কার করে যে আওরঙ্গজেব এবং মুরাদের বাহিনী জোটবদ্ধ হয়েছে । [38] তাদের মধ্যে এই মর্মে চুক্তি হয় যে, ক্ষমতায় যেতে পারলে তারা সমগ্র সাম্রাজ্য নিজেদের মধ্যে ভাগ করে নেবে।[40] ১৬৫৮ সালের এপ্রিল মাসে দারা শিকোর বাহিনী আওরঙ্গজেবের বাহিনীর নিকট পরাজিত হয়। দারাশিকো'র মূলবাহিনী তখন পলাতক সুজাকে অনুসরণ করে বিহার পর্যন্ত চলে গিয়েছিল। আওরঙ্গজেব বাহিনীকে প্রতিহত করার জন্য দারাশিকো কাশিম খাঁ এবং যশোবন্ত সিংহকে পাঠান। এদের মধ্যে কোন একটা ভুল বোঝাবুঝির জন্য আওরঙ্গজেব যুদ্ধে জয়লাভ করেছিলেন। আওরঙ্গজেব এবং মুরাদের সম্মিলিত বাহিনী রাজধানী আগ্রার দিকে অগ্রসর হয়। একদিকে আওরঙ্গজেব বাহিনীর নিকট পরাজিত হয়ে এবং অন্যদিকে মুরাদকে শাহ সুজা কে ধাওয়া করতে যাওয়া তার অপর সেনাবাহিনী বিহার থেকে অতি দ্রুত রাজধানীতে ফেরত আসা সম্ভব নয় বুঝতে পেরে দারাশিকো আওরঙ্গজেব বাহিনীকে প্রতিহত করার জন্য জোট গঠন করার চেষ্টা করেন। কিন্তু ইতিমধ্যে আওরঙ্গজেব  উপযুক্তদের তার জোটে অন্তর্ভুক্ত করে ফেলেছিল। দারাশিকো তরিঘড়ি করে আওরঙ্গজেবের বিরুদ্ধে যুদ্ধের প্রস্তুতি গ্রহণ করে। তার অপ্রস্তুত সেনাবাহিনী আওরঙ্গজেব এর যুদ্ধবাজ এবং অতন্ত সুশৃংখল সেনাবাহিনীর মোকাবেলা করতে মে মাসের শেষের দিকে সামুগর যুদ্ধক্ষেত্রে উপস্থিত হয়। সমর নায়ক হিসেবে দারাশিকো কিংবা তার সৈন্যগণ কেউই আওরঙ্গজেব এর সেনাবাহিনীর সমকক্ষ ছিল না। দারাশিকো এই যুদ্ধ জয়ের বিষয়ে অতি আত্মবিশ্বাসী ছিলেন। তার পিতা জীবিত থাকা অবস্থায় ভাইদের বিরুদ্ধে এ ধরনের যুদ্ধ পরিচালনা করতে অনেকেই তাকে নিষেধ করেছিল। সিংহাসন প্রাপ্তির জন্য এতটাই মরিয়া ছিলেন যে তিনি এ পরামর্শে কর্ণপাত করেননি।[38]\"দারাশিকো'র পরাজয়ের পর শাহজাহানকে আগ্রা দুর্গে বন্দি করা হয়। সেখানে তিনি দীর্ঘ আট বছর তার প্রিয় কন্যা জাহানারার তত্ত্বাবধানে ছিলেন।\"[41]</td>\n",
              "      <td>মুহাম্মদ আফজাল খানের জ্যেষ্ঠ পুত্রের নাম কি ছিল ?</td>\n",
              "      <td>{'text': [], 'answer_start': []}</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"deepset/xlm-roberta-large-squad2\"\n",
        "batch_size = 4"
      ],
      "metadata": {
        "id": "m6ES3T4TPrH3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentencepiece -q"
      ],
      "metadata": {
        "id": "Jn8wJXhhP99G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdcb22d3-0880-4753-8732-c25fce8250d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "id": "diab3HMMP3a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "0a605deab9ae4b68a035fa01f438d1d6",
            "d9dc19d350db47c589b74a32470c3066",
            "6d0fad164285423da75947d9779d5996",
            "b8e4a27f09254788b337c4874b2da969",
            "c7ad1e38a9b74469be1886f31f69104c",
            "700db232c3fa4440b1fe3df5d97fe819",
            "95b1e5f2daeb4092803bf43403e27c64",
            "b03f979120e641e1b89c5496846074f3",
            "d4282a99dbdf4e15bb290e6b38535b1e",
            "05bff1283ca744d1b921e0ee765bd15b",
            "5ee3833167664d39b236c7f9eaf694f9",
            "f380d47b304444d9a7acc57107662941",
            "29d9492168d24e4e9ac939ad11f18e13",
            "e28624413d314481b42c25bef32b58cf",
            "21503212a62d41b1b3ddde3610368d63",
            "a736f442427f460d993d63455d801cc1",
            "9c27e984d14f4e9e8155d07ab29fcaed",
            "b18babbd7e324fd2aa82789fcb33f8e6",
            "e27e555bc14648559eee826b161a6b48",
            "a2d4a27094054aaaa9ce3cae70fa53c7",
            "d0515940f44b405caa2bd31eb8ae1e8a",
            "0c5b18ce66c14e9389314213776edfb8",
            "6e43ca2028364debbb7600ef41bf679d",
            "10f5a809dd9a495bb69f1c78918a08f5",
            "b109034333e4411aa6faaf0bcde012a3",
            "51cb73493abf489eb187c68006143572",
            "844a3aab006e47b6a9674e7974883ebc",
            "713af2b8e48147a393b5cd7b46c2ae55",
            "a5b7c1f1c9bb4278a6c60e24b2084dbb",
            "be689aaffc874087ab48185d3ddfa793",
            "a42daf3fad5e49e692da387a9c8b8ff3",
            "3605590c459341c9b60013bd86dc9948",
            "2673bb99a11f44f5af3168019be9d584",
            "65871b80304e4c42bc37d6965dea0cdc",
            "c83bd569e0404c88ba4708c8d635910d",
            "d6f67c5c6a004895a71db8e364683063",
            "810ff420ee3f4c9cb816bed45b3555f7",
            "92f744cc912d470eab7453618686665a",
            "71fead5a7f9d4a32822cebf671278838",
            "41fe73b45211413f9e065c75b11a9784",
            "b8f15e2323ff4a65b7f13f8fbf54e029",
            "22ca5ddb1ea7434bb9c4bb6b83f27dd8",
            "be1e87ddb1964d879b09dbacc0917521",
            "26e9d716fcb4465c8fdd99ce136c164c"
          ]
        },
        "outputId": "3d95acdc-416b-4670-dae8-95f3cc602823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/179 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a605deab9ae4b68a035fa01f438d1d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/606 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f380d47b304444d9a7acc57107662941"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading sentencepiece.bpe.model:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e43ca2028364debbb7600ef41bf679d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading special_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65871b80304e4c42bc37d6965dea0cdc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check if the tokenizer is fast tokenizer\n",
        "\n",
        "import transformers\n",
        "\n",
        "assert isinstance(tokenizer, transformers.PreTrainedTokenizerFast)"
      ],
      "metadata": {
        "id": "cOHrt6EBRHAI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer(\"আপনার নাম কি?\", \"আমার নাম ফয়সাল\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BsPNe6ZRJAB",
        "outputId": "0a7fbc62-f1ec-4596-c9c0-720615bfec0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 21742, 37564, 14403, 32, 2, 2, 29388, 37564, 22741, 2801, 16984, 2763, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 384  # The maximum length of a feature (question and context)\n",
        "doc_stride = 128  # The allowed overlap between two part of the context "
      ],
      "metadata": {
        "id": "xsO8MuLJRQVl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pad_on_right = tokenizer.padding_side == \"right\""
      ],
      "metadata": {
        "id": "zPFFcVTSRYEe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_train_features(examples):\n",
        "    \n",
        "    #removing left whitespaces\n",
        "    examples[\"question\"] = [q.lstrip() for q in examples[\"question\"]]\n",
        "\n",
        "\n",
        "    tokenized_examples = tokenizer(\n",
        "        examples[\"question\" if pad_on_right else \"context\"],\n",
        "        examples[\"context\" if pad_on_right else \"question\"],\n",
        "        truncation=\"only_second\" if pad_on_right else \"only_first\",\n",
        "        max_length=max_length,\n",
        "        stride=doc_stride,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "   \n",
        "    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n",
        "    \n",
        "    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n",
        "\n",
        "    tokenized_examples[\"start_positions\"] = []\n",
        "    tokenized_examples[\"end_positions\"] = []\n",
        "\n",
        "    for i, offsets in enumerate(offset_mapping):\n",
        "        \n",
        "        input_ids = tokenized_examples[\"input_ids\"][i]\n",
        "        cls_index = input_ids.index(tokenizer.cls_token_id)\n",
        "\n",
        "        \n",
        "        sequence_ids = tokenized_examples.sequence_ids(i)\n",
        "\n",
        "        \n",
        "        sample_index = sample_mapping[i]\n",
        "        answers = examples[\"answers\"][sample_index]\n",
        "        \n",
        "        if len(answers[\"answer_start\"]) == 0:\n",
        "            tokenized_examples[\"start_positions\"].append(cls_index)\n",
        "            tokenized_examples[\"end_positions\"].append(cls_index)\n",
        "        else:\n",
        "            \n",
        "            start_char = answers[\"answer_start\"][0]\n",
        "            end_char = start_char + len(answers[\"text\"][0])\n",
        "\n",
        "           \n",
        "            token_start_index = 0\n",
        "            while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\n",
        "                token_start_index += 1\n",
        "\n",
        "            \n",
        "            token_end_index = len(input_ids) - 1\n",
        "            while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\n",
        "                token_end_index -= 1\n",
        "\n",
        "            \n",
        "            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\n",
        "                tokenized_examples[\"start_positions\"].append(cls_index)\n",
        "                tokenized_examples[\"end_positions\"].append(cls_index)\n",
        "            else:\n",
        "                \n",
        "                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\n",
        "                    token_start_index += 1\n",
        "                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\n",
        "                while offsets[token_end_index][1] >= end_char:\n",
        "                    token_end_index -= 1\n",
        "                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\n",
        "\n",
        "    return tokenized_examples"
      ],
      "metadata": {
        "id": "ZQJvqjy9RbAG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = raw_datasets.map(\n",
        "    prepare_train_features, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "004dda06fb0e4f11a5dba9676c51fdec",
            "e0a841f4fe18467386e089eb552fa5d5",
            "2e596aae4ff74e148816c0ac9d0adfd8",
            "deabc61df116406da4f687a1e582aeff",
            "892b174ba8c24a96aaadc43719c8bee3",
            "4f1fcaf901c34e7f8c4c3e1520c1ce8b",
            "eabefde2205642c98e8df3b0a616f1c1",
            "0bcf6a9c148346478d544efa72f1a86e",
            "b0037e79149e482da46d84e1103e1b20",
            "a4cb291206494663b2ebc61ccf6814b2",
            "6e8b2e1f2628449abe71a6ed58c08839",
            "a6697115e56545cebbdb64a2ecfd9741",
            "908e052674d34b1da140706a5e433c6a",
            "5ee6c7e27a424a69acf9605a23293cdf",
            "e97b5c27396a43f0b82f4c2a42d39e9f",
            "1539be2da57449489e285ce5615366a0",
            "611a127f61264fce84f6080627dafa32",
            "4b6e284f77a8444eabd9f1cbb01cc5e6",
            "3fb7cd4e4b984c57a5190db4868ebc20",
            "88683dd3e96f43d298b1c1cdf3757e22",
            "f75bfd727fd54af68105de387be58c60",
            "0bb52acac1154e38ad9b8e4183df2b30",
            "7e9f1aa6d95c43e0bcdcc2d8831e03ad",
            "3455ad78906449788f9aec38b56d8b19",
            "4c55b22e3b0b4ec69685bd908b32962b",
            "a04f9aecd91f4ec99d009f04184f99f8",
            "38afcd5ca1754a88b368d6238b014339",
            "dd4e9853665e4461aec7b757a94be131",
            "670f2f778fb94864a4d7a8fd66a5cf65",
            "c46c4911b839430ea00ea845cece39b9",
            "4276e8390e8e4b1381471fd5b2e47eba",
            "1ad5799193694d70998ee9d521ad1bbd",
            "4d4498e3e9c246aaad4d1284009ee10d"
          ]
        },
        "id": "I2FRH3uURh3x",
        "outputId": "3abcda89-9c76-4f93-efa0-6dce3f51554e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "004dda06fb0e4f11a5dba9676c51fdec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6697115e56545cebbdb64a2ecfd9741"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e9f1aa6d95c43e0bcdcc2d8831e03ad"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets[\"train\"].column_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5PxEoyQRozp",
        "outputId": "87dfce44-f1f1-4ec8-c983-eb4b1e71a1b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'attention_mask', 'start_positions', 'end_positions']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer\n",
        "\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "801745b9d559468aa2117ada8aa4e8a6",
            "9ae2b0dacd60450e9d67c7c3edc5d513",
            "e932bdb07bcb4a10b16bf9c743dc936d",
            "82e29901b86540d483c5051b684fd4cd",
            "57cfeee845f04648a08e9a76ece9a985",
            "c44541d2190d40c2a77be30dddbab6ad",
            "cf00195798a04901b1e4b66ced2d8343",
            "793a06fb267b47ecb8d0181a1b5b1d41",
            "be0b23718c554e529312815bec48a687",
            "ab39ba3f4b184dbf81822c40e39f57fc",
            "25d95334d1b346dfaf4ca6a54b3257ca"
          ]
        },
        "id": "Wyypku_BRsuu",
        "outputId": "fdf410f5-e4b3-4ffc-9030-05228845c73c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.09G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "801745b9d559468aa2117ada8aa4e8a6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wj611FxHRwoy",
        "outputId": "dcbfbad5-3ecd-45f9-c484-2101606fc375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XLMRobertaConfig {\n",
              "  \"_name_or_path\": \"deepset/xlm-roberta-large-squad2\",\n",
              "  \"architectures\": [\n",
              "    \"XLMRobertaForQuestionAnswering\"\n",
              "  ],\n",
              "  \"attention_probs_dropout_prob\": 0.1,\n",
              "  \"bos_token_id\": 0,\n",
              "  \"classifier_dropout\": null,\n",
              "  \"eos_token_id\": 2,\n",
              "  \"gradient_checkpointing\": false,\n",
              "  \"hidden_act\": \"gelu\",\n",
              "  \"hidden_dropout_prob\": 0.1,\n",
              "  \"hidden_size\": 1024,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 4096,\n",
              "  \"language\": \"english\",\n",
              "  \"layer_norm_eps\": 1e-05,\n",
              "  \"max_position_embeddings\": 514,\n",
              "  \"model_type\": \"xlm-roberta\",\n",
              "  \"name\": \"XLMRoberta\",\n",
              "  \"num_attention_heads\": 16,\n",
              "  \"num_hidden_layers\": 24,\n",
              "  \"output_past\": true,\n",
              "  \"pad_token_id\": 1,\n",
              "  \"position_embedding_type\": \"absolute\",\n",
              "  \"transformers_version\": \"4.21.2\",\n",
              "  \"type_vocab_size\": 1,\n",
              "  \"use_cache\": true,\n",
              "  \"vocab_size\": 250002\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvcOF421SFD_",
        "outputId": "06c7d610-6b37-4ec1-f936-315abb7d9171"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.8 MB 4.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 9.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 181 kB 55.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 63 kB 2.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 53.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 68.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 70.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 157 kB 74.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 156 kB 72.4 MB/s \n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wandb login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jB120yLtSHsg",
        "outputId": "aad64761-10b9-4298-8f04-3b6b99e547dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb \n",
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50OwhyrwSiGB",
        "outputId": "70f85c0e-2726-42b1-fde7-d490be367a0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "7-hsyrtASw9K",
        "outputId": "a23b22f5-9e97-48f4-a73e-4098b5967e92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'xlm-roberta-large-squad2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnIGdykESyKr",
        "outputId": "cc604b2e-7204-4bf0-fd78-d32620a2a612"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gdrive\t     test.json\t train.json   validaiton.jsonl\n",
            "sample_data  test.jsonl  train.jsonl  validation.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wandb_dir = f\"/content/gdrive/MyDrive/DL_models/language_model_bn/wandb_files/\"\n",
        "\n",
        "wandb.init(dir=os.getenv(\"WANDB_DIR\",wandb_dir),project = \"language-model-bn\",id =\"xlm-roberta-deepset-test-1\",resume=\"allow\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "0SSMrtj0S1qd",
        "outputId": "a5f627bd-5180-4fae-8123-6e181f72ff78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mafschowdhury\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.13.2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/gdrive/MyDrive/DL_models/language_model_bn/wandb_files/wandb/run-20220831_013149-xlm-roberta-deepset-test-1</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/afschowdhury/language-model-bn/runs/xlm-roberta-deepset-test-1\" target=\"_blank\">xlm-roberta-deepset-test-1</a></strong> to <a href=\"https://wandb.ai/afschowdhury/language-model-bn\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/afschowdhury/language-model-bn/runs/xlm-roberta-deepset-test-1?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"
            ],
            "text/plain": [
              "<wandb.sdk.wandb_run.Run at 0x7f3982430610>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training Arguments\n",
        "args = TrainingArguments(\n",
        "    seed = 42,\n",
        "    output_dir = f\"/content/gdrive/MyDrive/DL_models/language_model_bn/{model_name}-test-1\",\n",
        "    evaluation_strategy = \"steps\", #default step 500\n",
        "    learning_rate=2e-5,\n",
        "    lr_scheduler_type = \"linear\",\n",
        "    warmup_ratio = 0.2,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    save_strategy = \"steps\",\n",
        "    save_steps = 1000,\n",
        "    save_total_limit=2,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    fp16=True,\n",
        "    report_to = \"wandb\",\n",
        "    run_name =\"language-model-bn(qas)_deepset_xlm\"\n",
        "    \n",
        ")"
      ],
      "metadata": {
        "id": "lFB7a3MbT5OU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LP4j8PoU2f7",
        "outputId": "7525110b-3091-4bb1-8440-0ab021d0dbf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n",
              "        num_rows: 2828\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n",
              "        num_rows: 2828\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n",
              "        num_rows: 2828\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import default_data_collator\n",
        "\n",
        "data_collator = default_data_collator"
      ],
      "metadata": {
        "id": "BenuxjP0U5Aa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args = args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76Tfj0LnU7ef",
        "outputId": "1c07496d-7935-4fbf-e98b-82745b85487a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cuda_amp half precision backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "GbLXsyEsVV4-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.memory_summary(device=None, abbreviated=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "C3wChoPqVb52",
        "outputId": "8fa29bd7-bb7d-482b-eb40-bca086f93145"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'|===========================================================================|\\n|                  PyTorch CUDA memory summary, device ID 0                 |\\n|---------------------------------------------------------------------------|\\n|            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\\n|===========================================================================|\\n|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\\n|---------------------------------------------------------------------------|\\n| Allocated memory      |    2131 MB |    2131 MB |    2131 MB |       0 B  |\\n|       from large pool |    2130 MB |    2130 MB |    2130 MB |       0 B  |\\n|       from small pool |       1 MB |       1 MB |       1 MB |       0 B  |\\n|---------------------------------------------------------------------------|\\n| Active memory         |    2131 MB |    2131 MB |    2131 MB |       0 B  |\\n|       from large pool |    2130 MB |    2130 MB |    2130 MB |       0 B  |\\n|       from small pool |       1 MB |       1 MB |       1 MB |       0 B  |\\n|---------------------------------------------------------------------------|\\n| GPU reserved memory   |    2148 MB |    2148 MB |    2148 MB |       0 B  |\\n|       from large pool |    2146 MB |    2146 MB |    2146 MB |       0 B  |\\n|       from small pool |       2 MB |       2 MB |       2 MB |       0 B  |\\n|---------------------------------------------------------------------------|\\n| Non-releasable memory |   16562 KB |   21932 KB |  398764 KB |  382201 KB |\\n|       from large pool |   15792 KB |   19888 KB |  396720 KB |  380928 KB |\\n|       from small pool |     770 KB |    2044 KB |    2044 KB |    1273 KB |\\n|---------------------------------------------------------------------------|\\n| Allocations           |     393    |     393    |     393    |       0    |\\n|       from large pool |     146    |     146    |     146    |       0    |\\n|       from small pool |     247    |     247    |     247    |       0    |\\n|---------------------------------------------------------------------------|\\n| Active allocs         |     393    |     393    |     393    |       0    |\\n|       from large pool |     146    |     146    |     146    |       0    |\\n|       from small pool |     247    |     247    |     247    |       0    |\\n|---------------------------------------------------------------------------|\\n| GPU reserved segments |      69    |      69    |      69    |       0    |\\n|       from large pool |      68    |      68    |      68    |       0    |\\n|       from small pool |       1    |       1    |       1    |       0    |\\n|---------------------------------------------------------------------------|\\n| Non-releasable allocs |       4    |       4    |      26    |      22    |\\n|       from large pool |       3    |       3    |      25    |      22    |\\n|       from small pool |       1    |       1    |       1    |       0    |\\n|---------------------------------------------------------------------------|\\n| Oversize allocations  |       0    |       0    |       0    |       0    |\\n|---------------------------------------------------------------------------|\\n| Oversize GPU segments |       0    |       0    |       0    |       0    |\\n|===========================================================================|\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "7LTh9eRbVAKM",
        "outputId": "00c77f00-eae3-4fdd-f171-bdb6fe59c8c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 2828\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 4\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 4\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2121\n",
            "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2121' max='2121' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2121/2121 26:21, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.960900</td>\n",
              "      <td>0.548447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.770800</td>\n",
              "      <td>0.450876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.547400</td>\n",
              "      <td>0.269723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.302300</td>\n",
              "      <td>0.114284</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2828\n",
            "  Batch size = 4\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2828\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-1000\n",
            "Configuration saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-1000/config.json\n",
            "Model weights saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-1000/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2828\n",
            "  Batch size = 4\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2828\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-2000\n",
            "Configuration saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-2000/config.json\n",
            "Model weights saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/checkpoint-2000/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2121, training_loss=0.621309877509837, metrics={'train_runtime': 1584.4204, 'train_samples_per_second': 5.355, 'train_steps_per_second': 1.339, 'total_flos': 5909362590910464.0, 'train_loss': 0.621309877509837, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(f\"/content/gdrive/MyDrive/DL_models/language_model_bn/{model_name}-test-1/afs_xlm_roberta\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xAFAwAfewiB",
        "outputId": "08f33c5b-793a-4668-96e5-0a74e87f7929"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta\n",
            "Configuration saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/config.json\n",
            "Model weights saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/pytorch_model.bin\n",
            "tokenizer config file saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/tokenizer_config.json\n",
            "Special tokens file saved in /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/special_tokens_map.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inferencing"
      ],
      "metadata": {
        "id": "dqJFNOxNg70u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_location = f\"/content/gdrive/MyDrive/DL_models/language_model_bn/{model_name}-test-1/afs_xlm_roberta\""
      ],
      "metadata": {
        "id": "VAo0ZBxzh1DP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_location"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "h3KFzv8Wh4SX",
        "outputId": "56d1550c-7304-4b9b-fc75-295bfd003f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oqRwhdfHkfrU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import  pipeline\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "nlp = pipeline('question-answering', model=model_location, tokenizer=model_location)\n",
        "QA_input = {\n",
        "    'question': 'আপনার নাম কি?',\n",
        "    'context': 'আমার নাম দীপ সাহা। আমি ইসলামী বিশ্ববিদ্যালয়ে পড়ি'\n",
        "}\n",
        "res = nlp(QA_input)\n",
        "res"
      ],
      "metadata": {
        "id": "gN-k9pP5hmV3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "589905f2-6c3b-4f2d-ba89-15470f237350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/config.json\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"/content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForQuestionAnswering\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"language\": \"english\",\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"name\": \"XLMRoberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.21.2\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 250002\n",
            "}\n",
            "\n",
            "loading configuration file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/config.json\n",
            "Model config XLMRobertaConfig {\n",
            "  \"_name_or_path\": \"/content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta\",\n",
            "  \"architectures\": [\n",
            "    \"XLMRobertaForQuestionAnswering\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": 0,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 1024,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 4096,\n",
            "  \"language\": \"english\",\n",
            "  \"layer_norm_eps\": 1e-05,\n",
            "  \"max_position_embeddings\": 514,\n",
            "  \"model_type\": \"xlm-roberta\",\n",
            "  \"name\": \"XLMRoberta\",\n",
            "  \"num_attention_heads\": 16,\n",
            "  \"num_hidden_layers\": 24,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 1,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.21.2\",\n",
            "  \"type_vocab_size\": 1,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 250002\n",
            "}\n",
            "\n",
            "loading weights file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing XLMRobertaForQuestionAnswering.\n",
            "\n",
            "All the weights of XLMRobertaForQuestionAnswering were initialized from the model checkpoint at /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use XLMRobertaForQuestionAnswering for predictions without further training.\n",
            "Didn't find file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/added_tokens.json. We won't load it.\n",
            "loading file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/sentencepiece.bpe.model\n",
            "loading file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/tokenizer.json\n",
            "loading file None\n",
            "loading file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/special_tokens_map.json\n",
            "loading file /content/gdrive/MyDrive/DL_models/language_model_bn/xlm-roberta-large-squad2-test-1/afs_xlm_roberta/tokenizer_config.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.9999872446060181, 'start': 8, 'end': 18, 'answer': ' দীপ সাহা।'}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "QA_input = {\n",
        "    'question': 'মেক্সিকো সিটি প্রথম কোন বছর প্যান আমেরিকা গেমস আয়োজন করেছিল?',\n",
        "    'context': 'মেক্সিকো সিটি একমাত্র লাতিন আমেরিকান শহর হিসেবে অলিম্পিক গেমস আয়োজন করেছে। ১৯৬৮ সালে গ্রীষ্মকালীন অলিম্পিক আয়োজন করে তারা। (এটাও পরিবর্তন হবে রিওকে ধন্যবাদ, ২০১৬ গ্রীষ্মকালীন গেমসের আয়োজক)। শহরটি ১৯৫৫ এবং ১৯৭৫ প্যান আমেরিকান গেমস আয়োজন করে, সান্তিয়াগো এবং সাও পাওলো সরে যাওয়ার পর সর্বশেষ। আইসিএফ ফ্লাটওয়াটার রেসিং বিশ্ব চ্যাম্পিয়নশিপ এখানে ১৯৭৪ এবং ১৯৯৪ সালে অনুষ্ঠিত হয়েছিল। লুচা লিব্রে হচ্ছে মেক্সিকোর কুস্তির এক ধরন, এবং এটি সারা দেশে জনপ্রিয় খেলাগুলোর মধ্যে অন্যতম। শহরের প্রধান মাঠগুলি হল এরিনা মেক্সিকো এবং এরিনা কোলিসেও।'\n",
        "    }\n",
        "res = nlp(QA_input)\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Ii_tsniiRcI",
        "outputId": "77d5c307-f687-4a7c-a436-085fbc700300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.9983528256416321, 'start': 198, 'end': 203, 'answer': ' ১৯৫৫'}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Evaluation"
      ],
      "metadata": {
        "id": "kSP-fu7HjEmD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "for batch in trainer.get_eval_dataloader():\n",
        "    break\n",
        "batch = {k: v.to(trainer.args.device) for k, v in batch.items()}\n",
        "with torch.no_grad():\n",
        "    output = trainer.model(**batch)\n",
        "output.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tP-vtYGtsmQf",
        "outputId": "89635a20-6b67-4ea2-85a9-36a9ef2c0489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "odict_keys(['loss', 'start_logits', 'end_logits'])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output.start_logits.shape, output.end_logits.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GasxxScstkr",
        "outputId": "2fff69ff-50eb-4254-cb4c-937ef43fb885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([4, 384]), torch.Size([4, 384]))"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output.start_logits.argmax(dim=-1), output.end_logits.argmax(dim=-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYq37vEvsyCn",
        "outputId": "117f4169-40a6-4abc-cb7c-ca3214b1a06d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([ 0,  0, 21, 19], device='cuda:0'),\n",
              " tensor([ 0,  0, 24, 24], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_best_size = 20"
      ],
      "metadata": {
        "id": "AMvzL5noszn3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "start_logits = output.start_logits[0].cpu().numpy()\n",
        "end_logits = output.end_logits[0].cpu().numpy()\n",
        "# Gather the indices the best start/end logits:\n",
        "start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "valid_answers = []\n",
        "for start_index in start_indexes:\n",
        "    for end_index in end_indexes:\n",
        "        if start_index <= end_index: # We need to refine that test to check the answer is inside the context\n",
        "            valid_answers.append(\n",
        "                {\n",
        "                    \"score\": start_logits[start_index] + end_logits[end_index],\n",
        "                    \"text\": \"\" # We need to find a way to get back the original substring corresponding to the answer in the context\n",
        "                }\n",
        "            )"
      ],
      "metadata": {
        "id": "IzjzpD4ms_99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_validation_features(examples):\n",
        "    # Some of the questions have lots of whitespace on the left, which is not useful and will make the\n",
        "    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that\n",
        "    # left whitespace\n",
        "    examples[\"question\"] = [q.lstrip() for q in examples[\"question\"]]\n",
        "\n",
        "    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\n",
        "    # in one example possible giving several features when a context is long, each of those features having a\n",
        "    # context that overlaps a bit the context of the previous feature.\n",
        "    tokenized_examples = tokenizer(\n",
        "        examples[\"question\" if pad_on_right else \"context\"],\n",
        "        examples[\"context\" if pad_on_right else \"question\"],\n",
        "        truncation=\"only_second\" if pad_on_right else \"only_first\",\n",
        "        max_length=max_length,\n",
        "        stride=doc_stride,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    # Since one example might give us several features if it has a long context, we need a map from a feature to\n",
        "    # its corresponding example. This key gives us just that.\n",
        "    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n",
        "\n",
        "    # We keep the example_id that gave us this feature and we will store the offset mappings.\n",
        "    tokenized_examples[\"example_id\"] = []\n",
        "\n",
        "    for i in range(len(tokenized_examples[\"input_ids\"])):\n",
        "        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\n",
        "        sequence_ids = tokenized_examples.sequence_ids(i)\n",
        "        context_index = 1 if pad_on_right else 0\n",
        "\n",
        "        # One example can give several spans, this is the index of the example containing this span of text.\n",
        "        sample_index = sample_mapping[i]\n",
        "        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\n",
        "\n",
        "        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\n",
        "        # position is part of the context or not.\n",
        "        tokenized_examples[\"offset_mapping\"][i] = [\n",
        "            (o if sequence_ids[k] == context_index else None)\n",
        "            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\n",
        "        ]\n",
        "\n",
        "    return tokenized_examples"
      ],
      "metadata": {
        "id": "odt-GC5stB-U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "validation_features = raw_datasets[\"validation\"].map(\n",
        "    prepare_validation_features,\n",
        "    batched=True,\n",
        "    remove_columns=raw_datasets[\"validation\"].column_names\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "60d49f12d3a44bff8ab4049b83ff28be",
            "3677337e84ce4a3494f5a31d2bfb5377",
            "dbbc1619070a40fba178dd5a1b932710",
            "7f5f8aefe847477392bc52f8065cb8db",
            "b82bf8b9aa1e49ba88ddf889356bac06",
            "b64ac8d2e1df451189540b6b4025404f",
            "dc8f4c79a9d348ce8b4055e890c31ddd",
            "a478f1f1c3d14e5ea79ecd10e1adfeba",
            "f5971c2fecc94bf3856bc71c4f08c932",
            "29832513a9894e409dd67e2bd3ac006f",
            "b9dc2dbcb3a64791a4b4b14003dd7122"
          ]
        },
        "id": "TgZnIU4qtF9l",
        "outputId": "e498fe33-363a-4aac-b563-8238206709c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60d49f12d3a44bff8ab4049b83ff28be"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_predictions = trainer.predict(validation_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "CCuMrh7BtLQG",
        "outputId": "4e3a6460-2b2a-4bcb-b77a-8327d324684a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `XLMRobertaForQuestionAnswering.forward` and have been ignored: example_id, offset_mapping. If example_id, offset_mapping are not expected by `XLMRobertaForQuestionAnswering.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 2828\n",
            "  Batch size = 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_features.set_format(type=validation_features.format[\"type\"], columns=list(validation_features.features.keys()))"
      ],
      "metadata": {
        "id": "Pw0ppYuStZkG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_answer_length = 30"
      ],
      "metadata": {
        "id": "6mJc5XbstrPt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_logits = output.start_logits[0].cpu().numpy()\n",
        "end_logits = output.end_logits[0].cpu().numpy()\n",
        "offset_mapping = validation_features[0][\"offset_mapping\"]\n",
        "# The first feature comes from the first example. For the more general case, we will need to be match the example_id to\n",
        "# an example index\n",
        "context = raw_datasets[\"validation\"][3][\"context\"]\n",
        "\n",
        "# Gather the indices the best start/end logits:\n",
        "start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "valid_answers = []\n",
        "for start_index in start_indexes:\n",
        "    for end_index in end_indexes:\n",
        "        # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond\n",
        "        # to part of the input_ids that are not in the context.\n",
        "        if (\n",
        "            start_index >= len(offset_mapping)\n",
        "            or end_index >= len(offset_mapping)\n",
        "            or offset_mapping[start_index] is None\n",
        "            or offset_mapping[end_index] is None\n",
        "        ):\n",
        "            continue\n",
        "        # Don't consider answers with a length that is either < 0 or > max_answer_length.\n",
        "        if end_index < start_index or end_index - start_index + 1 > max_answer_length:\n",
        "            continue\n",
        "        if start_index <= end_index: # We need to refine that test to check the answer is inside the context\n",
        "            start_char = offset_mapping[start_index][0]\n",
        "            end_char = offset_mapping[end_index][1]\n",
        "            valid_answers.append(\n",
        "                {\n",
        "                    \"score\": start_logits[start_index] + end_logits[end_index],\n",
        "                    \"text\": context[start_char: end_char]\n",
        "                }\n",
        "            )\n",
        "\n",
        "valid_answers = sorted(valid_answers, key=lambda x: x[\"score\"], reverse=True)[:n_best_size]\n",
        "valid_answers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wG9w5O1Jtvyq",
        "outputId": "efee1c4d-9f26-40a4-8432-6bd842e73f02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': -18.757591, 'text': 'ন্দ্রনাথ'},\n",
              " {'score': -19.47488, 'text': 'রবীন্দ্রনাথ'},\n",
              " {'score': -19.475689, 'text': 'রব'},\n",
              " {'score': -19.519253, 'text': 'ন্দ্রনাথ ঠাকুর স'},\n",
              " {'score': -19.62949, 'text': 'ন্দ্রনাথ ঠাকুর সুভাষচন'},\n",
              " {'score': -20.236542, 'text': 'রবীন্দ্রনাথ ঠাকুর স'},\n",
              " {'score': -20.346779, 'text': 'রবীন্দ্রনাথ ঠাকুর সুভাষচন'}]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets[\"validation\"][3][\"answers\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ahI1FiCJt46m",
        "outputId": "1046f089-6193-42ba-9955-ff933fcbf8e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': ['রবীন্দ্রনাথ ঠাকুর'], 'answer_start': [0]}"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "\n",
        "examples = raw_datasets[\"validation\"]\n",
        "features = validation_features\n",
        "\n",
        "example_id_to_index = {k: i for i, k in enumerate(examples[\"id\"])}\n",
        "features_per_example = collections.defaultdict(list)\n",
        "for i, feature in enumerate(features):\n",
        "    features_per_example[example_id_to_index[feature[\"example_id\"]]].append(i)"
      ],
      "metadata": {
        "id": "U2NJrydBuTSB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "def postprocess_qa_predictions(examples, features, raw_predictions, n_best_size = 20, max_answer_length = 30):\n",
        "    all_start_logits, all_end_logits = raw_predictions\n",
        "    # Build a map example to its corresponding features.\n",
        "    example_id_to_index = {k: i for i, k in enumerate(examples[\"id\"])}\n",
        "    features_per_example = collections.defaultdict(list)\n",
        "    for i, feature in enumerate(features):\n",
        "        features_per_example[example_id_to_index[feature[\"example_id\"]]].append(i)\n",
        "\n",
        "    # The dictionaries we have to fill.\n",
        "    predictions = collections.OrderedDict()\n",
        "\n",
        "    # Logging.\n",
        "    print(f\"Post-processing {len(examples)} example predictions split into {len(features)} features.\")\n",
        "\n",
        "    # Let's loop over all the examples!\n",
        "    for example_index, example in enumerate(tqdm(examples)):\n",
        "        # Those are the indices of the features associated to the current example.\n",
        "        feature_indices = features_per_example[example_index]\n",
        "\n",
        "        min_null_score = None # Only used if squad_v2 is True.\n",
        "        valid_answers = []\n",
        "        \n",
        "        context = example[\"context\"]\n",
        "        # Looping through all the features associated to the current example.\n",
        "        for feature_index in feature_indices:\n",
        "            # We grab the predictions of the model for this feature.\n",
        "            start_logits = all_start_logits[feature_index]\n",
        "            end_logits = all_end_logits[feature_index]\n",
        "            # This is what will allow us to map some the positions in our logits to span of texts in the original\n",
        "            # context.\n",
        "            offset_mapping = features[feature_index][\"offset_mapping\"]\n",
        "\n",
        "            # Update minimum null prediction.\n",
        "            cls_index = features[feature_index][\"input_ids\"].index(tokenizer.cls_token_id)\n",
        "            feature_null_score = start_logits[cls_index] + end_logits[cls_index]\n",
        "            if min_null_score is None or min_null_score < feature_null_score:\n",
        "                min_null_score = feature_null_score\n",
        "\n",
        "            # Go through all possibilities for the `n_best_size` greater start and end logits.\n",
        "            start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "            end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "            for start_index in start_indexes:\n",
        "                for end_index in end_indexes:\n",
        "                    # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond\n",
        "                    # to part of the input_ids that are not in the context.\n",
        "                    if (\n",
        "                        start_index >= len(offset_mapping)\n",
        "                        or end_index >= len(offset_mapping)\n",
        "                        or offset_mapping[start_index] is None\n",
        "                        or offset_mapping[end_index] is None\n",
        "                    ):\n",
        "                        continue\n",
        "                    # Don't consider answers with a length that is either < 0 or > max_answer_length.\n",
        "                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:\n",
        "                        continue\n",
        "\n",
        "                    start_char = offset_mapping[start_index][0]\n",
        "                    end_char = offset_mapping[end_index][1]\n",
        "                    valid_answers.append(\n",
        "                        {\n",
        "                            \"score\": start_logits[start_index] + end_logits[end_index],\n",
        "                            \"text\": context[start_char: end_char]\n",
        "                        }\n",
        "                    )\n",
        "        \n",
        "        if len(valid_answers) > 0:\n",
        "            best_answer = sorted(valid_answers, key=lambda x: x[\"score\"], reverse=True)[0]\n",
        "        else:\n",
        "            # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid\n",
        "            # failure.\n",
        "            best_answer = {\"text\": \"\", \"score\": 0.0}\n",
        "        \n",
        "        # Let's pick our final answer: the best one or the null answer (only for squad_v2)\n",
        "        # if not squad_v2:\n",
        "        #     predictions[example[\"id\"]] = best_answer[\"text\"]\n",
        "        # else:\n",
        "        answer = best_answer[\"text\"] if best_answer[\"score\"] > min_null_score else \"\"\n",
        "        predictions[example[\"id\"]] = answer\n",
        "\n",
        "    return predictions"
      ],
      "metadata": {
        "id": "oeTHqHeot9tJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_predictions = postprocess_qa_predictions( raw_datasets[\"validation\"], validation_features, raw_predictions.predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "504422c49ab343b4a986a7f38d7d12d8",
            "84034cd20ec442209b3dbd45c9348a5c",
            "09ddbc6e6dd5429189380b9a41feed58",
            "5afeec2f8fa140209111359869e854df",
            "97a0e010a2594710a656ce7dd94489da",
            "d2f1130c9caf4515ad88d6a5dbff1767",
            "42dccb7c81784b558aa531c52680124f",
            "a700cdfbdc804d3e992cf3c40e2ade75",
            "3868103c94cc48d28492e8dbb6fef741",
            "1cf2ef80aedf4aed927690ee05f1dcfe",
            "668fd46e2504442aafc3f57385bf04e9"
          ]
        },
        "id": "fUG-7LyDufn-",
        "outputId": "1c69f8a9-c8a6-413b-ac01-9682708fac00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Post-processing 2504 example predictions split into 2828 features.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2504 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "504422c49ab343b4a986a7f38d7d12d8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate -q"
      ],
      "metadata": {
        "id": "Jjdu2lk1vKKD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"squad_v2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "72dfdbd9b62845539763891391d251b4",
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            "034daeb857ea4f45a5c3805777b6face",
            "ace6187a9d74402584078081ec210852",
            "800ffdad2a3e440aa935076a67ac3317",
            "90a90db6b06f4fdc8b9283b6efb7107c",
            "0f42ddd8b656470eaf1482f44bc67f6a",
            "16205db412f2420cb361a6bdae0d66e6",
            "5d3aadb38092438fbdfd66d4cba3eeee",
            "c1c53de548f24b4b85db33be499ab5df",
            "b6f66b99d16c4da68b2c2cba7d4d2ab2",
            "1893425664724e76a8285641b7a2d6ef",
            "44e56ba9cdba495ba942b520f69383e9",
            "6db935eab93b4971b9e66f0a2c3f2203",
            "c31d85e1ef2c4f81a2b6007ffd746b60",
            "7b633eb334b74fa392e3145e74d9aa88",
            "d26a000ca66f4523b7aae4185c94d0ca",
            "ebc2b5387b694c25827fe0ed5cb1b48a",
            "5c14ba0c4aaa4bda9b1ba906e03f8951",
            "e6dde04e03a8467d8d20bee14fcb14cd",
            "5a8f0b1a0fe84ccaa6f8ee6cfae59466"
          ]
        },
        "id": "gSMSKBQwujfr",
        "outputId": "d44f8b33-3f34-478b-b061-28bf5623f380"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.47k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72dfdbd9b62845539763891391d251b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/11.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6f66b99d16c4da68b2c2cba7d4d2ab2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "theoretical_answers = [\n",
        "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in raw_datasets[\"validation\"]\n",
        "]"
      ],
      "metadata": {
        "id": "Bu-9BQuwwTbT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# formatted_predictions = [{\"id\": k, \"prediction_text\": v} for k, v in final_predictions.items()]\n",
        "formatted_predictions = [{\"id\": k, \"prediction_text\": v, \"no_answer_probability\": 0.0} for k, v in final_predictions.items()]"
      ],
      "metadata": {
        "id": "lISHKqYa1IBB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "theoretical_answers[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2J2t4F2-1qq6",
        "outputId": "25f5c648-8ca8-49da-b02d-b423e651c20a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 'bengali--981248442377505718-0-2649',\n",
              " 'answers': {'text': ['১৭ মার্চ ১৯২০'], 'answer_start': [19]}}"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "formatted_predictions[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdq4a5sN12OF",
        "outputId": "88b13282-890a-489e-b0d6-8d0bd52d4f80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 'bengali--981248442377505718-0-2649',\n",
              " 'prediction_text': '১৭ মার্চ ১৯২০',\n",
              " 'no_answer_probability': 0.0}"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yHsFALzB1xVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metric.compute(predictions=formatted_predictions, references=theoretical_answers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uI2sBBNnva3m",
        "outputId": "fa78c55e-37c5-439d-f52a-86e4b12d4891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'exact': 94.52875399361022,\n",
              " 'f1': 96.56710191654284,\n",
              " 'total': 2504,\n",
              " 'HasAns_exact': 89.29712460063898,\n",
              " 'HasAns_f1': 93.37382044650411,\n",
              " 'HasAns_total': 1252,\n",
              " 'NoAns_exact': 99.76038338658147,\n",
              " 'NoAns_f1': 99.76038338658147,\n",
              " 'NoAns_total': 1252,\n",
              " 'best_exact': 94.52875399361022,\n",
              " 'best_exact_thresh': 0.0,\n",
              " 'best_f1': 96.56710191654284,\n",
              " 'best_f1_thresh': 0.0}"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qas-QVp_wq7I"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}