diff --git a/Question_Answering_uit_vquad.ipynb b/Question_Answering_uit_vquad.ipynb
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+++ b/Question_Answering_uit_vquad.ipynb
@@ -0,0 +1,7468 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MOsHUjgdIrIW"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install datasets transformers\n",
+        "!pip install evaluate\n",
+        "!apt install git-lfs\n",
+        "!pip install datasets"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "X00WJ3oo2j4V",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a3b44d3f-c1e3-4dba-ecd9-1ead81bdb0b7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332,
+          "referenced_widgets": [
+            "7965cebd5b2b472696ff9f73b9ffbb67",
+            "9d3a56235900426fac2db7b3eb5220c6",
+            "fcaa69eee1a34892b23fea82e33105f6",
+            "cc944dfded3a4407803d9de0749e9e7e",
+            "aa87aa9e0fb3402fb1463d5add169dfd",
+            "b7cdd2e6cd054e30900a913424d785ae",
+            "72b13aedd8c54e1b8db2c00ffc26bfb0",
+            "7dc71f11c021437ba70ed0ebde69466a",
+            "c5f35341e9014e1b9901615b964843e0",
+            "37845bbb4eec4600a6a23a0652763d01",
+            "d8456f20343a49609f34da26bdbef1c6",
+            "3dd0d6ecf623437cae714ad7b1b02998",
+            "7eaa209626fa41768072ed6a3a7cbbac",
+            "882c5481b591444aaf1149d7d45fed6e",
+            "08182849a8924f7a8ce3caff1d70520c",
+            "0c49f225df2c40f095be5c2b44f2a9b2",
+            "3298cb2427274425be1dc454ebdf3e8f"
+          ]
+        },
+        "id": "fSHETU42GNMT",
+        "outputId": "66782f13-3ae8-4c34-8ae1-37f2f162984c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Token is valid.\n",
+            "Your token has been saved in your configured git credential helpers (store).\n",
+            "Your token has been saved to /root/.huggingface/token\n",
+            "Login successful\n"
+          ]
+        }
+      ],
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "ENbIYyjNGNMU"
+      },
+      "outputs": [],
+      "source": [
+        "import transformers\n",
+        "import json\n",
+        "from collections import namedtuple\n",
+        "from random import shuffle\n",
+        "import datasets\n",
+        "import pandas as pd\n",
+        "import evaluate\n",
+        "from tqdm import tqdm \n",
+        "from datasets import load_dataset, load_metric"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rEJBSTyZIrIb"
+      },
+      "source": [
+        "# Fine-tuning a model on a question-answering task"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "zVvslsfMIrIh"
+      },
+      "outputs": [],
+      "source": [
+        "squad_v2 = False\n",
+        "model_checkpoint = \"xlm-roberta-base\"\n",
+        "batch_size = 16"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "whPRbBNbIrIl"
+      },
+      "source": [
+        "## Loading the dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_question_agument():\n",
+        "    qs = []\n",
+        "    with open(f\"/content/drive/MyDrive/Colab/UIT_vQuaD/augument.txt\",'r') as f:\n",
+        "        for line in f:\n",
+        "          qs.append(line[:-1])\n",
+        "    return qs \n",
+        "qs = load_question_agument()\n",
+        "qs[:3]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iQCAxz0dpS4r",
+        "outputId": "30ce01fe-970d-4b10-e279-8c2804e773c6"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Phạm Văn Đồng đã sử dụng tên gì khi còn là phó giám đốc Sở Tư pháp ở Quế Lâm?',\n",
+              " 'Phạm Văn Đồng giữ chức vụ gì trong bộ máy nhà nước CHXHCNVN?',\n",
+              " 'Từ 1955 đến 1976, Phạm Văn Đồng giữ chức vụ gì?']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install sacrebleu"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0M-sYrFRwKlB",
+        "outputId": "5b751812-0da5-421f-e282-9268629421b2"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Collecting sacrebleu\n",
+            "  Downloading sacrebleu-2.3.1-py3-none-any.whl (118 kB)\n",
+            "\u001b[K     |████████████████████████████████| 118 kB 36.2 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (1.21.6)\n",
+            "Collecting portalocker\n",
+            "  Downloading portalocker-2.6.0-py2.py3-none-any.whl (15 kB)\n",
+            "Collecting colorama\n",
+            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
+            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (0.8.10)\n",
+            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (4.9.1)\n",
+            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (2022.6.2)\n",
+            "Installing collected packages: portalocker, colorama, sacrebleu\n",
+            "Successfully installed colorama-0.4.6 portalocker-2.6.0 sacrebleu-2.3.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "bleu = evaluate.load(\"sacrebleu\")\n",
+        "def bleu_2_sent(s,t):\n",
+        "  results = bleu.compute(predictions=[s], references=[[t]])\n",
+        "  # print(results)\n",
+        "  return results['score']\n",
+        "\n",
+        "bleu_2_sent('toi la chieu ddd','toi la chieu a b c')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67,
+          "referenced_widgets": [
+            "f2cdf72ddc4840a88072d64fbd3e4c0f",
+            "b572c0edfe6b42a6a811c89dc8be1da0",
+            "d645c33adfda4cd99e63b7edd0f86189",
+            "49e3035261ac4a65b5aaef5c84a71fe9",
+            "890a2024c9cb4f2b8d195617372d1cef",
+            "dab3735ee6c441ed93aa71a1101c7390",
+            "47e6c1c6f8ff4c0c8b1b7e7cd013d363",
+            "b6a8c0a2362a49778923256e032fb41a",
+            "6e884b2a47234fdc84b60de03116d639",
+            "788a3f163e304f1aa589a57c8790579b",
+            "f6168df40f43432383b24269327a3b55"
+          ]
+        },
+        "id": "Lfs7F-6nr1KL",
+        "outputId": "6ad6dd37-1685-4a8a-b278-c61dcf52c196"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f2cdf72ddc4840a88072d64fbd3e4c0f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "36.06452879987789"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "OxSCMIMx8-Ei",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5107b49c-eb60-4eb5-f996-7fdb36c1e504"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 228/228 [00:00<00:00, 62774.14it/s]\n",
+            "100%|██████████| 228/228 [00:00<00:00, 87071.05it/s]\n",
+            "100%|██████████| 2454/2454 [00:00<00:00, 133865.99it/s]\n",
+            "100%|██████████| 2454/2454 [00:00<00:00, 190979.16it/s]\n",
+            "100%|██████████| 138/138 [01:04<00:00,  2.15it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def load_train_data(name):\n",
+        "\n",
+        "  data = []\n",
+        "\n",
+        "  dirs = [\"/content/drive/MyDrive/Colab/UIT_vQuaD/dev-context-en-question-vi.json\", \n",
+        "          \"/content/drive/MyDrive/Colab/UIT_vQuaD/dev-context-vi-question-en.json\",\n",
+        "          \"/content/drive/MyDrive/Colab/UIT_vQuaD/test-context-en-question-vi.json\",\n",
+        "          \"/content/drive/MyDrive/Colab/UIT_vQuaD/test-context-vi-question-en.json\"\n",
+        "          ]\n",
+        "  for dir in dirs :\n",
+        "      with open(dir,'r') as f:\n",
+        "           tmp  = json.load(f)\n",
+        "           for i in tqdm(range(len(tmp['data']))):\n",
+        "                o = tmp['data'][i]\n",
+        "                title = tmp['data'][i]['title']\n",
+        "                n = len(tmp['data'][i]['paragraphs'])\n",
+        "                for j in range(n):\n",
+        "                    context = tmp['data'][i]['paragraphs'][j]['context']\n",
+        "                    qas = tmp['data'][i]['paragraphs'][j]['qas']\n",
+        "                    for qa in qas:\n",
+        "                        question = qa['question']\n",
+        "                        id = qa['id'] \n",
+        "                        # print(qa['answers'])\n",
+        "                        if len(qa['answers']) == 0:\n",
+        "                            continue\n",
+        "                        answer_start = qa['answers'][0]['answer_start']\n",
+        "                        answer_text = qa['answers'][0]['text']\n",
+        "                        \n",
+        "                        data.append({\n",
+        "                                'id':id,\n",
+        "                                'title':title,\n",
+        "                                'context': context,\n",
+        "                                'question': question ,\n",
+        "                                'answers':{\n",
+        "                                    'text':[answer_text],\n",
+        "                                    'answer_start':[answer_start]\n",
+        "                                }\n",
+        "                            })\n",
+        "\n",
+        "  tmp = []\n",
+        "  with open(f\"/content/drive/MyDrive/Colab/UIT_vQuaD/{name}.json\",'r') as f:\n",
+        "       tmp  = json.load(f)\n",
+        "\n",
+        "  \n",
+        "  cnt = 0 \n",
+        "  for i in tqdm(range(len(tmp['data']))):\n",
+        "      o = tmp['data'][i]\n",
+        "      title = tmp['data'][i]['title']\n",
+        "      n = len(tmp['data'][i]['paragraphs'])\n",
+        "\n",
+        "      for j in range(n):\n",
+        "          context = tmp['data'][i]['paragraphs'][j]['context']\n",
+        "          qas = tmp['data'][i]['paragraphs'][j]['qas']\n",
+        "          for qa in qas:\n",
+        "              question = qa['question']\n",
+        "              id = qa['id'] \n",
+        "              # print(qa['answers'])\n",
+        "              if len(qa['answers']) == 0:\n",
+        "                  continue\n",
+        "              answer_start = qa['answers'][0]['answer_start']\n",
+        "              answer_text = qa['answers'][0]['text']\n",
+        "              \n",
+        "              data.append({\n",
+        "                      'id':id,\n",
+        "                      'title':title,\n",
+        "                      'context': context,\n",
+        "                      'question': question ,\n",
+        "                      'answers':{\n",
+        "                          'text':[answer_text],\n",
+        "                          'answer_start':[answer_start]\n",
+        "                      }\n",
+        "                  })\n",
+        "              \n",
+        "              # agument data \n",
+        "              bleu_score = bleu_2_sent(qs[cnt],question)\n",
+        "              if 15 <= bleu_score and bleu_score <= 95:\n",
+        "                data.append({\n",
+        "                        'id':id,\n",
+        "                        'title':title,\n",
+        "                        'context': context,\n",
+        "                        'question': qs[cnt] ,\n",
+        "                        'answers':{\n",
+        "                            'text':[answer_text],\n",
+        "                            'answer_start':[answer_start]\n",
+        "                        }\n",
+        "                    })\n",
+        "              cnt += 1\n",
+        "  tdata = pd.DataFrame(data)\n",
+        "  tdata = datasets.Dataset.from_pandas(tdata)\n",
+        "  return tdata\n",
+        "\n",
+        "data_train = load_train_data('train')\n",
+        "# data_test = load_train_data('test')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_train"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pTxpO-5Ipreq",
+        "outputId": "df5660b4-fbfa-4d3f-afbd-7eac09087a8c"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Dataset({\n",
+              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
+              "    num_rows: 44326\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_test_data(name):\n",
+        "  with open(f\"/content/drive/MyDrive/Colab/UIT_vQuaD/{name}.json\",'r') as f:\n",
+        "       tmp  = json.load(f)\n",
+        "  data = []\n",
+        "  for i in range(len(tmp['data'])):\n",
+        "      o = tmp['data'][i]\n",
+        "      title = tmp['data'][i]['title']\n",
+        "      n = len(tmp['data'][i]['paragraphs'])\n",
+        "\n",
+        "      for j in range(n):\n",
+        "          context = tmp['data'][i]['paragraphs'][j]['context']\n",
+        "          qas = tmp['data'][i]['paragraphs'][j]['qas']\n",
+        "          for qa in qas:\n",
+        "              question = qa['question']\n",
+        "              id = qa['id'] \n",
+        "              # print(qa['answers'])\n",
+        "              if len(qa['answers']) == 0:\n",
+        "                  continue\n",
+        "              answer_start = []\n",
+        "              answer_text = []\n",
+        "              for t in range(len(qa['answers'])):\n",
+        "                  answer_start.append(qa['answers'][t]['answer_start'])\n",
+        "                  answer_text.append(qa['answers'][t]['text'])\n",
+        "              data.append({\n",
+        "                  'id':id,\n",
+        "                  'title':title,\n",
+        "                  'context': context,\n",
+        "                  'question': question ,\n",
+        "                  'answers':{\n",
+        "                          'text':answer_text,\n",
+        "                          'answer_start':answer_start\n",
+        "                      }\n",
+        "                  \n",
+        "              })\n",
+        "  tdata = pd.DataFrame(data)\n",
+        "  tdata = datasets.Dataset.from_pandas(tdata)\n",
+        "  return tdata\n",
+        "\n",
+        "data_test = load_test_data('test')\n"
+      ],
+      "metadata": {
+        "id": "1pRlg0QNhmM1"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_train = data_train.shuffle(seed=42)"
+      ],
+      "metadata": {
+        "id": "cgE56tdmiLO8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "dqD0EvznTONq"
+      },
+      "outputs": [],
+      "source": [
+        "DATA = namedtuple('data', ['train', 'test',])\n",
+        "data = DATA(data_train,data_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n9qywopnIrJH"
+      },
+      "source": [
+        "## Preprocessing the training data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "eXNLu_-nIrJI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
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+          ]
+        },
+        "outputId": "cd5fc70d-9c77-4ac1-cc97-372f312f688e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/615 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c710965efc564f31b649aa51b859353d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8b4366eb07664a53baa07932cd2b2e5a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/9.10M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1838f24bb8aa4b00bafa2be27448daec"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from transformers import AutoTokenizer\n",
+        "tokenizer = AutoTokenizer.from_pretrained('xlm-roberta-base')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "pgPS82OBGNMa"
+      },
+      "outputs": [],
+      "source": [
+        "max_length = 384 # The maximum length of a feature (question and context)\n",
+        "doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\n",
+        "pad_on_right = tokenizer.padding_side == \"right\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "70b71c744367412a963c1b1a6b13495c",
+            "18f7167142634402a4f8bd02cfbc96a9",
+            "297aff3fbbb2459eb414a168f9c65b9a",
+            "a60032c9741e42b589c73dbcb18bbe45",
+            "d948de0369324f1fa96e76d5cc607fb7",
+            "a366257c448d4e5f9804cfd96bb625da",
+            "2fc522595abb45639e29a93d80c9e82b",
+            "2bf7b6f9c7e149929bbfe04f08435aaa",
+            "d84255e8c2304f3ea46e0be1ec5800e0",
+            "e292237e6da84a3e9b11b9682fb54fea",
+            "80aef040ff8e48d2b9bf76284d87aa2d",
+            "bb7f7717c1724d95ae0184a27fd2d696",
+            "802608a7fae74fccb1e0b9c86da7688b",
+            "21720e0204a14281b30a587681c4909f",
+            "0908668f628f4b2fb3d3f9f0229b1e6a",
+            "ab3a3be274174aaabaa26e297150c1fa",
+            "d637181739ef4bfdab76472da0fbd827",
+            "ec385d8738104bee8ca179e59b397978",
+            "34082d6d95784e3a8180a07b40c0ab83",
+            "e5310e30464443698a9b95dc3350aca4",
+            "ebe71376d5fc4411ba65d7de22821d30",
+            "2447087476364922afbeae26d2df685f"
+          ]
+        },
+        "id": "daEZhxSYGNMf",
+        "outputId": "67d5b184-4514-49ea-c0bc-bf144feffae3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/45 [00:00<?, ?ba/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "70b71c744367412a963c1b1a6b13495c"
+            }
+          },
+          "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": "bb7f7717c1724d95ae0184a27fd2d696"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def prepare_train_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 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",
+        "    # The offset mappings will give us a map from token to character position in the original context. This will\n",
+        "    # help us compute the start_positions and end_positions.\n",
+        "    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n",
+        "\n",
+        "    # Let's label those examples!\n",
+        "    tokenized_examples[\"start_positions\"] = []\n",
+        "    tokenized_examples[\"end_positions\"] = []\n",
+        "\n",
+        "    for i, offsets in enumerate(offset_mapping):\n",
+        "        # We will label impossible answers with the index of the CLS token.\n",
+        "        input_ids = tokenized_examples[\"input_ids\"][i]\n",
+        "        cls_index = input_ids.index(tokenizer.cls_token_id)\n",
+        "\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",
+        "\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",
+        "        answers = examples[\"answers\"][sample_index]\n",
+        "        # If no answers are given, set the cls_index as answer.\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",
+        "            # Start/end character index of the answer in the text.\n",
+        "            start_char = answers[\"answer_start\"][0]\n",
+        "            end_char = start_char + len(answers[\"text\"][0])\n",
+        "\n",
+        "            # Start token index of the current span in the text.\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",
+        "            # End token index of the current span in the text.\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",
+        "            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\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",
+        "                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\n",
+        "                # Note: we could go after the last offset if the answer is the last word (edge case).\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\n",
+        "\n",
+        "tokenized_train = data.train.map(prepare_train_features, batched=True, remove_columns=data.train.column_names)\n",
+        "tokenized_test = data.test.map(prepare_train_features, batched=True, remove_columns=data.test.column_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O9TnjmWfmN6g",
+        "outputId": "106f7418-b58b-4bdf-852d-f0c2abc8507d"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "data(train=Dataset({\n",
+              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
+              "    num_rows: 44326\n",
+              "}), test=Dataset({\n",
+              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
+              "    num_rows: 2210\n",
+              "}))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "a90d7e04f8b74a33a8585673bbaafedd",
+            "3434ae92b1ca4eccae156a8fdcf76007",
+            "3fc180272408438c99321d5bb948750b",
+            "3bb356853c58496e84b7278d02c245e1",
+            "e81107327615470eb88fe8948d0d289e",
+            "abe24b089c6845a8b3710e2d4a1f8ac4",
+            "429ba42c18384df8b18a91567c79801b",
+            "e4b4ef2432a646418c54a55f4d0a00a9",
+            "60185934e2b043b4be035be0b9403572",
+            "77b4237ed7d2473fba16882cccb48df8",
+            "8c6323f45b2f4d0c8aaf5de061391426"
+          ]
+        },
+        "id": "-TohBOlvf4Sx",
+        "outputId": "bd3eaf6d-2837-4e1d-c759-331b2ac2ce06"
+      },
+      "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": "a90d7e04f8b74a33a8585673bbaafedd"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "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\n",
+        "tokenized_test_prepared = data.test.map(prepare_validation_features, batched=True, remove_columns=data.test.column_names)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qLlQKmnbm4Kl",
+        "outputId": "0179420f-d5cb-4543-ff1f-e227466bda05"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Dataset({\n",
+              "    features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n",
+              "    num_rows: 50611\n",
+              "})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "tokenized_train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "545PP3o8IrJV"
+      },
+      "source": [
+        "## Fine-tuning the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 159,
+          "referenced_widgets": [
+            "0f74f34a708a430399714c6ce5da0b69",
+            "fc8276d04e4244c2bd15931160b824d8",
+            "9c7de3cf61fe4979bf266074b71253ab",
+            "7fe5ee7505b54d92b05d52a1d94979bc",
+            "169c248a21564132af273ecfa031b3c8",
+            "3cc34977c5c144f492e425452ddad581",
+            "5a81020104d24ca6bdc6fa1dd16a0649",
+            "35384ae06dbf47258aedafa49bdf4021",
+            "81bb7d67eef94a30814447ad9c1fdf70",
+            "85e79fe1e286403bb5ee48af43471ca0",
+            "e0fe418be4bf46b89e149e63e57d8a71"
+          ]
+        },
+        "id": "TlqNaB8jIrJW",
+        "outputId": "1ae4805b-9c7f-4c15-9a8f-758c1cc9b10f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0f74f34a708a430399714c6ce5da0b69"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForQuestionAnswering: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias']\n",
+            "- This IS expected if you are initializing XLMRobertaForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing XLMRobertaForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+            "Some weights of XLMRobertaForQuestionAnswering were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer\n",
+        "model = AutoModelForQuestionAnswering.from_pretrained(\"xlm-roberta-base\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "9TqCPaDqdpQ-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "2ec28932b52d448e93cd4cc8849ca25c",
+            "33675b3b4b534bdea3c14db59045aefa",
+            "4ac03141c0be4e5b8fca51cee8f0d97c",
+            "d0f86cecda2440d69adb4fbd72775621",
+            "08bc71b5de564f14a4c30ab37dca0cf4",
+            "0fa56ce4e75f4096aabd67c35d710445",
+            "74f289b2b65145aa885b12714155771b",
+            "e7dd02a7c59f4abd84ab4b04522b59c0",
+            "c8830556d4574790a0ddbb1cf17ddd50",
+            "1a9dec52d12b4de9bf6203f6e0f600bc",
+            "e827bb4e5fe3486e8477e38d55d1737d",
+            "4ea338b836f74b6aa35ff9d159be8d59",
+            "87b5df458c2a49a88d8a380fbec0d480",
+            "75efe71c5c664debb2fd42e61c589b95",
+            "3e2321ff1b1a490d9c8a091eeacc02b9",
+            "c4a1916600e74fec9434560f20741048",
+            "2d98132834aa467683da798e52ea2183",
+            "6bc02775127d42faac35b132387d609b",
+            "ce17553366384e97a0d7ea39d72e7146",
+            "13ca101ede164983b7c7680410bb0a40",
+            "fd1fc735a9cf457d82a99992d4c618ba",
+            "0720e897e1724aba81ac799c7ea94619"
+          ]
+        },
+        "outputId": "0acfcbea-c50c-49e1-e908-461fe7397aa8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/4.53k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2ec28932b52d448e93cd4cc8849ca25c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading extra modules:   0%|          | 0.00/3.32k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4ea338b836f74b6aa35ff9d159be8d59"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import evaluate\n",
+        "metric = evaluate.load(\"squad\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "Yz1vCUjncLKq"
+      },
+      "outputs": [],
+      "source": [
+        "import collections \n",
+        "import numpy as np\n",
+        "from tqdm.auto import tqdm\n",
+        "\n",
+        "n_best=20 \n",
+        "max_answer_length = 30\n",
+        "examples = data.test\n",
+        "features = tokenized_test_prepared\n",
+        "\n",
+        "def compute_metrics(evals):\n",
+        "    (start_logits, end_logits) , _ = evals\n",
+        "    # print(start_logits)\n",
+        "    # print('-'*100)\n",
+        "    example_to_features = collections.defaultdict(list)\n",
+        "    for idx, feature in enumerate(features):\n",
+        "        example_to_features[feature[\"example_id\"]].append(idx)\n",
+        "\n",
+        "    predicted_answers = []\n",
+        "    for example in tqdm(examples):\n",
+        "        example_id = example[\"id\"]\n",
+        "        context = example[\"context\"]\n",
+        "        answers = []\n",
+        "\n",
+        "        # Loop through all features associated with that example\n",
+        "        for feature_index in example_to_features[example_id]:\n",
+        "            start_logit = start_logits[feature_index]\n",
+        "            end_logit = end_logits[feature_index]\n",
+        "            offsets = features[feature_index][\"offset_mapping\"]\n",
+        "\n",
+        "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
+        "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
+        "            for start_index in start_indexes:\n",
+        "                for end_index in end_indexes:\n",
+        "                    # Skip answers that are not fully in the context\n",
+        "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
+        "                        continue\n",
+        "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
+        "                    if (\n",
+        "                        end_index < start_index\n",
+        "                        or end_index - start_index + 1 > max_answer_length\n",
+        "                    ):\n",
+        "                        continue\n",
+        "\n",
+        "                    answer = {\n",
+        "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
+        "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
+        "                    }\n",
+        "                    answers.append(answer)\n",
+        "\n",
+        "        # Select the answer with the best score\n",
+        "        if len(answers) > 0:\n",
+        "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
+        "            predicted_answers.append(\n",
+        "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
+        "            )\n",
+        "        else:\n",
+        "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
+        "\n",
+        "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
+        "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pJbmjTg4GNMh"
+      },
+      "source": [
+        "Then we will need a data collator that will batch our processed examples together, here the default one will work:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "_zwcYs9Jew4y"
+      },
+      "outputs": [],
+      "source": [
+        "total = len(data.train)\n",
+        "batchsz = 16 \n",
+        "st = int(total / 64)\n",
+        "model_name = model_checkpoint.split(\"/\")[-1]\n",
+        "args = TrainingArguments(\n",
+        "    f\"./{model_name}-finetuned-squad-4\",\n",
+        "    evaluation_strategy = \"steps\",\n",
+        "    logging_strategy = \"steps\",\n",
+        "    eval_steps=st,\n",
+        "    logging_steps=st,\n",
+        "    save_steps=st,\n",
+        "    learning_rate=2e-5,\n",
+        "    per_device_train_batch_size=batchsz,\n",
+        "    per_device_eval_batch_size=batchsz,\n",
+        "    gradient_accumulation_steps=1,\n",
+        "    num_train_epochs=6,\n",
+        "    weight_decay=0.01,\n",
+        "    # logging_steps=2,\n",
+        "    metric_for_best_model='f1',\n",
+        "    save_total_limit=10,\n",
+        "    push_to_hub=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "args"
+      ],
+      "metadata": {
+        "id": "oFoFqboob8RG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "PNW3dqXsGNMh"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import default_data_collator\n",
+        "data_collator = default_data_collator"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nqGq6V6qK85e"
+      },
+      "outputs": [],
+      "source": [
+        "import gc\n",
+        "gc.collect()\n",
+        "trainer = None"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rWBNqHeXL0fA"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "torch.cuda.empty_cache()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "id": "s2A4vhIbUvWp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 908,
+          "referenced_widgets": [
+            "304d23dbd4bf4b74b841f2b5589809ad",
+            "e1eb9e000fd740bfb2185cc4ff8ce821",
+            "ef67495985b34d56bc6746eae53ecdaf",
+            "4636efdadbae4fdc963f2eeb3186b6de",
+            "5f9901aeb1d8432cae9ccd60e5ae03bb",
+            "7f99dff88caa482985633f92a2a90bc4",
+            "65558162e8a54da59a052c4f49de263f",
+            "aaee453bde2246ee806d2f076b6141ff",
+            "2f74200b93f94caca01aba4408bf88be",
+            "d026f9b191664a7bb66f442e9209135b",
+            "9aa2d50e720a48b0a51312231f860b3e",
+            "1caaaecbabba43edac97ee30f0f97057",
+            "f0e5cbf228c849e3a2274b00285cb224",
+            "2daa5c88c3ad4a73ac7d9b5941e5fcb7",
+            "9f7ee61d01d44cccb82755c89fe53696",
+            "d1ff63fe99a74afaafdfaf7ed24986e3",
+            "0b5cad556a07436d903835c6aac40894",
+            "a74bc0d955a2444a81a7e836435ec58f",
+            "fc662d2d6840430dad6d6b2586a803a2",
+            "951acd2f91134ab2b609388d5aa813ee",
+            "1e3c28911f4f4680ae389ab8fba04ffd",
+            "f6f2057afd9f447186edeb63abe22dcf"
+          ]
+        },
+        "id": "imY1oC3SIrJf",
+        "outputId": "e7a40bf6-28b3-4f05-f01a-8cf3166afdda"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/repository.py:729: FutureWarning: Creating a repository through 'clone_from' is deprecated and will be removed in v0.12. Please create the repository first using `create_repo(..., exists_ok=True)`.\n",
+            "  FutureWarning,\n",
+            "Cloning https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad-4 into local empty directory.\n",
+            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad-4 into local empty directory.\n",
+            "/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 = 50611\n",
+            "  Num Epochs = 6\n",
+            "  Instantaneous batch size per device = 16\n",
+            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
+            "  Gradient Accumulation steps = 1\n",
+            "  Total optimization steps = 18984\n",
+            "  Number of trainable parameters = 277454594\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='2077' max='18984' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [ 2077/18984 47:38 < 6:28:12, 0.73 it/s, Epoch 0.66/6]\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",
+              "      <th>Exact Match</th>\n",
+              "      <th>F1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>692</td>\n",
+              "      <td>2.830200</td>\n",
+              "      <td>1.767404</td>\n",
+              "      <td>48.371041</td>\n",
+              "      <td>69.385328</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>1384</td>\n",
+              "      <td>1.750100</td>\n",
+              "      <td>1.443659</td>\n",
+              "      <td>56.968326</td>\n",
+              "      <td>75.988236</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='47' max='148' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [ 47/148 00:19 < 00:42, 2.40 it/s]\n",
+              "    </div>\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "***** Running Evaluation *****\n",
+            "  Num examples = 2359\n",
+            "  Batch size = 16\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/2210 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "304d23dbd4bf4b74b841f2b5589809ad"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Saving model checkpoint to ./xlm-roberta-base-finetuned-squad-4/checkpoint-692\n",
+            "Configuration saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/config.json\n",
+            "Model weights saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/pytorch_model.bin\n",
+            "tokenizer config file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/tokenizer_config.json\n",
+            "Special tokens file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-692/special_tokens_map.json\n",
+            "tokenizer config file saved in ./xlm-roberta-base-finetuned-squad-4/tokenizer_config.json\n",
+            "Special tokens file saved in ./xlm-roberta-base-finetuned-squad-4/special_tokens_map.json\n",
+            "Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n",
+            "WARNING:huggingface_hub.repository:Adding files tracked by Git LFS: ['tokenizer.json']. This may take a bit of time if the files are large.\n",
+            "***** Running Evaluation *****\n",
+            "  Num examples = 2359\n",
+            "  Batch size = 16\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/2210 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1caaaecbabba43edac97ee30f0f97057"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Saving model checkpoint to ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384\n",
+            "Configuration saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/config.json\n",
+            "Model weights saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/pytorch_model.bin\n",
+            "tokenizer config file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/tokenizer_config.json\n",
+            "Special tokens file saved in ./xlm-roberta-base-finetuned-squad-4/checkpoint-1384/special_tokens_map.json\n",
+            "***** Running Evaluation *****\n",
+            "  Num examples = 2359\n",
+            "  Batch size = 16\n"
+          ]
+        }
+      ],
+      "source": [
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=args,\n",
+        "    train_dataset=tokenized_train,\n",
+        "    eval_dataset=tokenized_test,\n",
+        "    data_collator=data_collator,\n",
+        "    tokenizer=tokenizer,\n",
+        "    compute_metrics=compute_metrics\n",
+        ")\n",
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = AutoModelForQuestionAnswering.from_pretrained(\"xlm-roberta-base-finetuned-squad_au/checkpoint-4000\")\n",
+        "model_name = model_checkpoint.split(\"/\")[-1]\n",
+        "args = TrainingArguments(\n",
+        "    f\"{model_name}-finetuned-squad_au\",\n",
+        "    evaluation_strategy = \"epoch\",\n",
+        "    logging_strategy = \"epoch\",\n",
+        "    learning_rate=2e-5,\n",
+        "    per_device_train_batch_size=16,\n",
+        "    per_device_eval_batch_size=16,\n",
+        "    num_train_epochs=5,\n",
+        "    weight_decay=0.01,\n",
+        "    logging_steps=2,\n",
+        "    metric_for_best_model='f1',\n",
+        "    save_total_limit=4,\n",
+        "    push_to_hub=True,\n",
+        ")\n",
+        "trainer = Trainer(\n",
+        "    model,\n",
+        "    args,\n",
+        "    train_dataset=tokenized_train,\n",
+        "    eval_dataset=tokenized_test,\n",
+        "    data_collator=data_collator,\n",
+        "    tokenizer=tokenizer,\n",
+        "    compute_metrics=compute_metrics\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "p5vU4R7LKHDL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "849e0e85-827c-48dd-bb73-2e84465cb208"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "loading configuration file xlm-roberta-base-finetuned-squad_au/checkpoint-4000/config.json\n",
+            "Model config XLMRobertaConfig {\n",
+            "  \"_name_or_path\": \"xlm-roberta-base-finetuned-squad_au/checkpoint-4000\",\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",
+            "  \"hidden_act\": \"gelu\",\n",
+            "  \"hidden_dropout_prob\": 0.1,\n",
+            "  \"hidden_size\": 768,\n",
+            "  \"initializer_range\": 0.02,\n",
+            "  \"intermediate_size\": 3072,\n",
+            "  \"layer_norm_eps\": 1e-05,\n",
+            "  \"max_position_embeddings\": 514,\n",
+            "  \"model_type\": \"xlm-roberta\",\n",
+            "  \"num_attention_heads\": 12,\n",
+            "  \"num_hidden_layers\": 12,\n",
+            "  \"output_past\": true,\n",
+            "  \"pad_token_id\": 1,\n",
+            "  \"position_embedding_type\": \"absolute\",\n",
+            "  \"torch_dtype\": \"float32\",\n",
+            "  \"transformers_version\": \"4.24.0\",\n",
+            "  \"type_vocab_size\": 1,\n",
+            "  \"use_cache\": true,\n",
+            "  \"vocab_size\": 250002\n",
+            "}\n",
+            "\n",
+            "loading weights file xlm-roberta-base-finetuned-squad_au/checkpoint-4000/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 xlm-roberta-base-finetuned-squad_au/checkpoint-4000.\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",
+            "PyTorch: setting up devices\n",
+            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
+            "/content/xlm-roberta-base-finetuned-squad_au is already a clone of https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au. Make sure you pull the latest changes with `repo.git_pull()`.\n",
+            "WARNING:huggingface_hub.repository:/content/xlm-roberta-base-finetuned-squad_au is already a clone of https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au. Make sure you pull the latest changes with `repo.git_pull()`.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "trainer.push_to_hub(commit_message=\"Training complete baseline\")"
+      ],
+      "metadata": {
+        "id": "aNYuF0wwKecb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 683,
+          "referenced_widgets": [
+            "e4a4b1a3b92c431caa561303449ab59f",
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+            "c0a11f3405424884a1dbc6eed5f3cdec",
+            "f1035428dd0349319239ffb65414121d",
+            "da2d42b74b3f401e926b29b67afba1d1",
+            "54db563c167f43f791ea0f889cb178bc",
+            "bc0057747c5f4c5ea3760d9e2f331a10",
+            "ec1b08ff07784b70b44fc3511834eeab",
+            "1dcb4a2849d246b8a72ee802a4caaab9",
+            "a131c1a367e3484c81409e30633fc97a",
+            "4b795323b8e845a5b7a46b0796f233f4",
+            "332f21d5fdcf417aac63460c038d1251",
+            "ef89cb0be4014d2191d62531589b4b6c",
+            "f598ecc4d643463dba95e096154dda8c",
+            "02f1fe2847684c5899dec4c83c5469b8",
+            "505e447192eb4c58b42416bffe857ec0",
+            "f848f24e71134da8b53b048a3805c6ac",
+            "85649d09acff4445a77102a78cb5db34",
+            "c15f41c1b2964c158d48222e49215591",
+            "ab0514c6932d4d77babdf76e8ebb6443",
+            "6763d78bb520461e92ad278b7b7a0ab7",
+            "65a99cbc9eab44a48110937e67c555d6",
+            "29007ef6348541d180c9b31ee0c44f80",
+            "d7bbefd0a81b446fb0aa8a7d9c70a6e5",
+            "22aa5fd8ffd945b89e5bb342dcc8dca3",
+            "df7dceda2f9843a9a977cba8a2afb367",
+            "f8717eccaff94a7681b9fb084b27c913",
+            "002cd9d24f2d4c39801146a0bae7210a",
+            "96916d3a68ab4ab88b6af00199b104e7",
+            "d40db84d2d2048138315502e5f104b2b",
+            "0536df16611a46e291639cdb95747244"
+          ]
+        },
+        "outputId": "ff366267-f9f1-4a2c-eee4-3f0b872f0640"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Saving model checkpoint to xlm-roberta-base-finetuned-squad_au\n",
+            "Configuration saved in xlm-roberta-base-finetuned-squad_au/config.json\n",
+            "Model weights saved in xlm-roberta-base-finetuned-squad_au/pytorch_model.bin\n",
+            "tokenizer config file saved in xlm-roberta-base-finetuned-squad_au/tokenizer_config.json\n",
+            "Special tokens file saved in xlm-roberta-base-finetuned-squad_au/special_tokens_map.json\n",
+            "Several commits (2) will be pushed upstream.\n",
+            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
+            "The progress bars may be unreliable.\n",
+            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload file pytorch_model.bin:   0%|          | 3.33k/1.03G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e4a4b1a3b92c431caa561303449ab59f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload file training_args.bin:  99%|#########9| 3.34k/3.36k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a131c1a367e3484c81409e30633fc97a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload file runs/Nov21_15-19-13_391905903de9/events.out.tfevents.1669043963.391905903de9.79.0:  64%|######3   …"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6763d78bb520461e92ad278b7b7a0ab7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "remote: Scanning LFS files for validity, may be slow...        \n",
+            "remote: LFS file scan complete.        \n",
+            "To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
+            "   3aad80f..c46c2b4  main -> main\n",
+            "\n",
+            "WARNING:huggingface_hub.repository:remote: Scanning LFS files for validity, may be slow...        \n",
+            "remote: LFS file scan complete.        \n",
+            "To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
+            "   3aad80f..c46c2b4  main -> main\n",
+            "\n",
+            "Dropping the following result as it does not have all the necessary fields:\n",
+            "{'task': {'name': 'Question Answering', 'type': 'question-answering'}}\n",
+            "To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
+            "   c46c2b4..58475d8  main -> main\n",
+            "\n",
+            "WARNING:huggingface_hub.repository:To https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au\n",
+            "   c46c2b4..58475d8  main -> main\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'https://huggingface.co/chieunq/xlm-roberta-base-finetuned-squad_au/commit/c46c2b4af346673e68b62f1533d6e9ebb6a84f05'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 106
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CdzABDVcIrJg"
+      },
+      "source": [
+        "We can now finetune our model by just calling the `train` method:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xz0HMpBoGNMi"
+      },
+      "source": [
+        "Since this training is particularly long, let's save the model just in case we need to restart."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SWK3JrSBGNMi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a1661e51-a1d9-4eba-b301-dc55ee3941bc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Saving model checkpoint to /content/drive/MyDrive/Colab/UIT_vQuaD/model\n",
+            "Configuration saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/config.json\n",
+            "Model weights saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/pytorch_model.bin\n",
+            "tokenizer config file saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/tokenizer_config.json\n",
+            "Special tokens file saved in /content/drive/MyDrive/Colab/UIT_vQuaD/model/special_tokens_map.json\n",
+            "Saving model checkpoint to xlm-roberta-base-finetuned-squad_1\n",
+            "Configuration saved in xlm-roberta-base-finetuned-squad_1/config.json\n",
+            "Model weights saved in xlm-roberta-base-finetuned-squad_1/pytorch_model.bin\n",
+            "tokenizer config file saved in xlm-roberta-base-finetuned-squad_1/tokenizer_config.json\n",
+            "Special tokens file saved in xlm-roberta-base-finetuned-squad_1/special_tokens_map.json\n",
+            "Dropping the following result as it does not have all the necessary fields:\n",
+            "{'task': {'name': 'Question Answering', 'type': 'question-answering'}}\n"
+          ]
+        }
+      ],
+      "source": [
+        "trainer.save_model(\"/content/drive/MyDrive/Colab/UIT_vQuaD/model\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import pipeline\n",
+        "\n",
+        "# Replace this with your own checkpoint\n",
+        "model_checkpoint = \"chieunq/xlm-roberta-base-finetuned-squad-2\"\n",
+        "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
+        "\n",
+        "context = \"\"\"\n",
+        "Nhóm của chúng tôi là sinh viên năm 4 trường ĐH Công Nghệ - ĐHQG Hà Nội. Nhóm gồm 3 thành viên : Nguyễn Quang Chiều, Nguyễn Quang Huy và Nguyễn Trần Anh Đức.\\\n",
+        " Đây là pha Reader trong dự án cuồi kì môn Các vấn đề hiện đại trong CNTT của nhóm . \n",
+        "\"\"\"\n",
+        "question = \"3 thành viên trong nhóm gồm những ai ?\"\n",
+        "question_answerer(question=question, context=context)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HWv5EyQCXPuW",
+        "outputId": "aeed3bc1-d96b-42df-c339-512db4b71533"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/config.json\n",
+            "Model config XLMRobertaConfig {\n",
+            "  \"_name_or_path\": \"chieunq/xlm-roberta-base-finetuned-squad-2\",\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",
+            "  \"hidden_act\": \"gelu\",\n",
+            "  \"hidden_dropout_prob\": 0.1,\n",
+            "  \"hidden_size\": 768,\n",
+            "  \"initializer_range\": 0.02,\n",
+            "  \"intermediate_size\": 3072,\n",
+            "  \"layer_norm_eps\": 1e-05,\n",
+            "  \"max_position_embeddings\": 514,\n",
+            "  \"model_type\": \"xlm-roberta\",\n",
+            "  \"num_attention_heads\": 12,\n",
+            "  \"num_hidden_layers\": 12,\n",
+            "  \"output_past\": true,\n",
+            "  \"pad_token_id\": 1,\n",
+            "  \"position_embedding_type\": \"absolute\",\n",
+            "  \"torch_dtype\": \"float32\",\n",
+            "  \"transformers_version\": \"4.24.0\",\n",
+            "  \"type_vocab_size\": 1,\n",
+            "  \"use_cache\": true,\n",
+            "  \"vocab_size\": 250002\n",
+            "}\n",
+            "\n",
+            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/config.json\n",
+            "Model config XLMRobertaConfig {\n",
+            "  \"_name_or_path\": \"chieunq/xlm-roberta-base-finetuned-squad-2\",\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",
+            "  \"hidden_act\": \"gelu\",\n",
+            "  \"hidden_dropout_prob\": 0.1,\n",
+            "  \"hidden_size\": 768,\n",
+            "  \"initializer_range\": 0.02,\n",
+            "  \"intermediate_size\": 3072,\n",
+            "  \"layer_norm_eps\": 1e-05,\n",
+            "  \"max_position_embeddings\": 514,\n",
+            "  \"model_type\": \"xlm-roberta\",\n",
+            "  \"num_attention_heads\": 12,\n",
+            "  \"num_hidden_layers\": 12,\n",
+            "  \"output_past\": true,\n",
+            "  \"pad_token_id\": 1,\n",
+            "  \"position_embedding_type\": \"absolute\",\n",
+            "  \"torch_dtype\": \"float32\",\n",
+            "  \"transformers_version\": \"4.24.0\",\n",
+            "  \"type_vocab_size\": 1,\n",
+            "  \"use_cache\": true,\n",
+            "  \"vocab_size\": 250002\n",
+            "}\n",
+            "\n",
+            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/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 chieunq/xlm-roberta-base-finetuned-squad-2.\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",
+            "loading file sentencepiece.bpe.model from cache at None\n",
+            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/tokenizer.json\n",
+            "loading file added_tokens.json from cache at None\n",
+            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/special_tokens_map.json\n",
+            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--chieunq--xlm-roberta-base-finetuned-squad-2/snapshots/2ca64af4fbc75716fb417662c29b9ab8e4e015f5/tokenizer_config.json\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'score': 0.9928902387619019,\n",
+              " 'start': 98,\n",
+              " 'end': 158,\n",
+              " 'answer': 'Nguyễn Quang Chiều, Nguyễn Quang Huy và Nguyễn Trần Anh Đức.'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
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