labs/notebooks_2023/lab_6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9OrmVqQMBWOb"
   },
   "source": [
    "# Multilingual Question Answering w/ Transformers\n",
    "\n",
    "[Link to the lab](https://colab.research.google.com/drive/10b26Jxho7EsWevWnItaumPoiSMKAXEXa?usp=sharing)\n",
    "\n",
    "This lab will focus on how to train and evaluate a model for multilingual question answering using the HuggingFace transformers library \n",
    "\n",
    "For this lab, we will use the multilingual [XLM RoBERTa model](https://huggingface.co/xlm-roberta-base). \n",
    "\n",
    "The task is extractive question answering. In this, the data consists of a question, and answer, and the span in the context which contains the correct answer. To model this, we will train our model to simply predict the start and end tokens of the answer.\n",
    "\n",
    "![](https://miro.medium.com/max/680/1*gwu3JjZ3hM08dIUziSJ3yg.png)\n",
    "\n",
    "Much of the code for this lab is cribbed from [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb#scrollTo=brBgQe9uAM3F)\n",
    "\n",
    "\n",
    "\n",
    "**It must be noted** that the raw output of the model we are going to train is not just 2 single numbers like the misleading diagram above, but rather 2 numbers for each token in the input. That is, we end up with a distribution of logits for a `start of the answer` and `end of the answer` tokens akin to the following:\n",
    "\n",
    "\n",
    "![logits_qa_example](https://user-images.githubusercontent.com/8036160/195606786-019c88d1-5e06-4434-b2f7-621815362f58.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-CdMFPLVV-Q0"
   },
   "outputs": [],
   "source": [
    "!pip install update transformers\n",
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ccDnA6JJCOBB"
   },
   "source": [
    "The usual housekeeping to ensure reproducible results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Nm4J_0FOWE9g"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from datasets import load_metric\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForQuestionAnswering\n",
    "from transformers import AutoConfig\n",
    "from functools import partial\n",
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from transformers import AdamW\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch import nn\n",
    "from collections import defaultdict, OrderedDict\n",
    "MODEL_NAME = 'xlm-roberta-base'\n",
    "#MODEL_NAME = 'bert-base-uncased'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "56EivecrpXmv"
   },
   "outputs": [],
   "source": [
    "def enforce_reproducibility(seed=42):\n",
    "    # Sets seed manually for both CPU and CUDA\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    # For atomic operations there is currently \n",
    "    # no simple way to enforce determinism, as\n",
    "    # the order of parallel operations is not known.\n",
    "    # CUDNN\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    # System based\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "device = torch.device(\"cpu\")\n",
    "if torch.cuda.is_available():\n",
    "  device = torch.device(\"cuda\")\n",
    "\n",
    "enforce_reproducibility()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "n7_yg1MicosB"
   },
   "outputs": [],
   "source": [
    "\"\"\" Official evaluation script for v1.1 of the SQuAD dataset. \"\"\"\n",
    "from __future__ import print_function\n",
    "from collections import Counter\n",
    "import string\n",
    "import re\n",
    "import argparse\n",
    "import json\n",
    "import sys\n",
    "\n",
    "\n",
    "def normalize_answer(s):\n",
    "    \"\"\"Lower text and remove punctuation, articles and extra whitespace.\"\"\"\n",
    "    def remove_articles(text):\n",
    "        return re.sub(r'\\b(a|an|the)\\b', ' ', text)\n",
    "\n",
    "    def white_space_fix(text):\n",
    "        return ' '.join(text.split())\n",
    "\n",
    "    def remove_punc(text):\n",
    "        exclude = set(string.punctuation)\n",
    "        return ''.join(ch for ch in text if ch not in exclude)\n",
    "\n",
    "    def lower(text):\n",
    "        return text.lower()\n",
    "\n",
    "    return white_space_fix(remove_articles(remove_punc(lower(s))))\n",
    "\n",
    "\n",
    "def f1_score(prediction, ground_truth):\n",
    "    prediction_tokens = normalize_answer(prediction).split()\n",
    "    ground_truth_tokens = normalize_answer(ground_truth).split()\n",
    "    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)\n",
    "    num_same = sum(common.values())\n",
    "    if num_same == 0:\n",
    "        return 0\n",
    "    precision = 1.0 * num_same / len(prediction_tokens)\n",
    "    recall = 1.0 * num_same / len(ground_truth_tokens)\n",
    "    f1 = (2 * precision * recall) / (precision + recall)\n",
    "    return f1\n",
    "\n",
    "\n",
    "def exact_match_score(prediction, ground_truth):\n",
    "    return (normalize_answer(prediction) == normalize_answer(ground_truth))\n",
    "\n",
    "\n",
    "def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):\n",
    "    scores_for_ground_truths = []\n",
    "    for ground_truth in ground_truths:\n",
    "        score = metric_fn(prediction, ground_truth)\n",
    "        scores_for_ground_truths.append(score)\n",
    "    return max(scores_for_ground_truths)\n",
    "\n",
    "\n",
    "def evaluate_squad(dataset, predictions):\n",
    "    f1 = exact_match = total = 0\n",
    "    for article in dataset:\n",
    "        for paragraph in article['paragraphs']:\n",
    "            for qa in paragraph['qas']:\n",
    "                total += 1\n",
    "                if qa['id'] not in predictions:\n",
    "                    message = 'Unanswered question ' + qa['id'] + \\\n",
    "                              ' will receive score 0.'\n",
    "                    print(message, file=sys.stderr)\n",
    "                    continue\n",
    "                ground_truths = list(map(lambda x: x['text'], qa['answers']))\n",
    "                prediction = predictions[qa['id']]\n",
    "                exact_match += metric_max_over_ground_truths(\n",
    "                    exact_match_score, prediction, ground_truths)\n",
    "                f1 += metric_max_over_ground_truths(\n",
    "                    f1_score, prediction, ground_truths)\n",
    "\n",
    "    exact_match = 100.0 * exact_match / total\n",
    "    f1 = 100.0 * f1 / total\n",
    "\n",
    "    return {'exact_match': exact_match, 'f1': f1}\n",
    "\n",
    "def compute_squad(predictions, references):\n",
    "  pred_dict = {prediction[\"id\"]: prediction[\"prediction_text\"] for prediction in predictions}\n",
    "  dataset = [\n",
    "      {\n",
    "          \"paragraphs\": [\n",
    "              {\n",
    "                  \"qas\": [\n",
    "                      {\n",
    "                          \"answers\": [{\"text\": answer_text} for answer_text in ref[\"answers\"][\"text\"]],\n",
    "                          \"id\": ref[\"id\"],\n",
    "                      }\n",
    "                      for ref in references\n",
    "                  ]\n",
    "              }\n",
    "          ]\n",
    "      }\n",
    "  ]\n",
    "  score = evaluate_squad(dataset=dataset, predictions=pred_dict)\n",
    "  return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is also equivalent to those 2 lines. I recommend going with that, unless you want more control over your code\n",
    "from datasets import load_metric\n",
    "compute_squad = load_metric(\"squad\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For your project, use load_metric(\"squad_v2\")**. SQuAD v2, like the TyDiQA dataset, contains unanswerable questions, and it's evaluation script supports that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RRJ2QQN7CSpl"
   },
   "source": [
    "Here we are using the huggingface datasets library to load the [MLQA dataset](https://github.com/facebookresearch/MLQA). MLQA contains QA data in SQuAD format for 7 different languages. To start, we will load the English only data to train and test our model."
   ]
  },
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    },
    "id": "IXT1MELFXSov",
    "outputId": "e950d0f4-46d2-4ce2-fec4-6a5f87890672"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c589d765fbf64405bce8c3f11122f099",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/2.29k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/4.09k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset mlqa/mlqa.en.en (download: 72.21 MiB, generated: 14.40 MiB, post-processed: Unknown size, total: 86.61 MiB) to /root/.cache/huggingface/datasets/mlqa/mlqa.en.en/1.0.0/1a1ae267d8d9e8e9ff25bd8811a27c5f8752ee58c5d75cf6c6451cbaba777c87...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7890a520709f4d80a55e382a7d12164d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/75.7M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e567ba0ab547401e9b539b26bf6bd88f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3b740d84200e4b44ae6127052dd0ffa1",
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      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset mlqa downloaded and prepared to /root/.cache/huggingface/datasets/mlqa/mlqa.en.en/1.0.0/1a1ae267d8d9e8e9ff25bd8811a27c5f8752ee58c5d75cf6c6451cbaba777c87. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
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       "model_id": "9f69fd43eedd49daaff5cd162df4af31",
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "mlqa = load_dataset('mlqa', 'mlqa.en.en')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mY-KD8ugOOgk"
   },
   "source": [
    "Here we will use the \"test\" split for training and the validation split for test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wur8j5R4XdDY",
    "outputId": "5fac14c7-525c-474f-d75a-dee69723c6cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    test: Dataset({\n",
       "        features: ['context', 'question', 'answers', 'id'],\n",
       "        num_rows: 11590\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['context', 'question', 'answers', 'id'],\n",
       "        num_rows: 1148\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlqa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "x8FoLgXe7A9q",
    "outputId": "ea44c264-af29-4d6e-d1cd-908da5842357"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'answers': {'answer_start': [1223], 'text': ['four to six hours']},\n",
       " 'context': 'Inhaled bronchodilators are the primary medications used, and result in a small overall benefit. The two major types are β2 agonists and anticholinergics; both exist in long-acting and short-acting forms. They reduce shortness of breath, wheeze, and exercise limitation, resulting in an improved quality of life. It is unclear if they change the progression of the underlying disease.In those with mild disease, short-acting agents are recommended on an as needed basis. In those with more severe disease, long-acting agents are recommended. Long-acting agents partly work by reducing hyperinflation. If long-acting bronchodilators are insufficient, then inhaled corticosteroids are typically added. Which type of long-acting agent, tiotropium (a long-acting anticholinergic) or a long-acting beta agonist (LABA) is better is unclear, and trying each and continuing with the one that works best may be advisable. Both types of agent appear to reduce the risk of acute exacerbations by 15–25%. While both may be used at the same time, any added benefit is of questionable significance.Several short-acting β2 agonists are available, including salbutamol (albuterol) and terbutaline. They provide some relief of symptoms for four to six hours. LABAs such as salmeterol, formoterol, and indacaterol are often used as maintenance therapy. Some feel the evidence of benefits is limited, while others view the evidence of benefit as established. Long-term use appears safe in COPD with adverse effects include shakiness and heart palpitations. When used with inhaled steroids they increase the risk of pneumonia. While steroids and LABAs may work better together, it is unclear if this slight benefit outweighs the increased risks. There is some evidence that combined treatment of LABAs with long-acting muscarinic antagonists (LAMA), an anticholinergic, may result in less exacerbations, less pneumonia, an improvement in forced expiratory volume (FEV1%), and potential improvements in quality of life when compared to treatment with LABA and an inhaled corticosteriod (ICS). All three together, LABA, LAMA, and ICS, have some evidence of benefits. Indacaterol requires an inhaled dose once a day, and is as effective as the other long-acting β2 agonist drugs that require twice-daily dosing for people with stable COPD.Two main anticholinergics are used in COPD, ipratropium and tiotropium. Ipratropium is a short-acting agent, while tiotropium is long-acting. Tiotropium is associated with a decrease in exacerbations and improved quality of life, and tiotropium provides those benefits better than ipratropium. It does not appear to affect mortality or the overall hospitalization rate. Anticholinergics can cause dry mouth and urinary tract symptoms. They are also associated with increased risk of heart disease and stroke. Aclidinium, another long-acting agent, reduces hospitalizations associated with COPD and improves quality of life. The LAMA umeclidinium bromide is another anticholinergic alternative. When compared to tiotropium, the LAMAs aclidinium, glycopyrronium, and umeclidinium appear to have a similar level of efficacy; with all four being more effective than placebo. Further research is needed comparing aclidinium to tiotropium.',\n",
       " 'id': '7bad1acabcddee223bee211e4c906330fbdb7b31',\n",
       " 'question': 'What is the duration of time that agonists are potent for?'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlqa['test'][70]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UetUzYccCyLE"
   },
   "source": [
    "Here we will load the tokenizer for XLM RoBERTa. We will make use of HuggingFace's AutoTokenizer, where we only need to specify what model it is we are loading based on its ID in the [model hub](https://huggingface.co/models)"
   ]
  },
  {
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    "id": "0AQrLuQpec_s",
    "outputId": "dfcfc328-1097-4373-8eb7-51e1a7a1f1f1"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ce7603ea1b1496b98e56cb9096d0a95",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Downloading:   0%|          | 0.00/512 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56fd327c80ce4d2ebd536de93804dc5e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "396ef1c9613848c3ba3f5964962dc3d5",
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      "text/plain": [
       "Downloading:   0%|          | 0.00/8.68M [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tk = AutoTokenizer.from_pretrained(MODEL_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WLJoP30mDGnf"
   },
   "source": [
    "Getting features for our text involves tokenizing the question and answer and finding the correct token indices for the start and end tokens corresponding to the answer. This turns out to be non-trivial to obtain! Additionally, our model has only been trained to handle input up to 512 tokens, so what do we do with very long contexts?\n",
    "\n",
    "Fortunately, the tokenizer has a lot of nice features to help with this. Basically what we will do is the following:\n",
    "\n",
    "1. Tokenize a batch of samples, specifying the following:\n",
    " - Truncate the sequences to the maximum allowable input, but only truncate the contexts so that the question always appears in the input\n",
    " - If the input is too long, split it into multiple inputs and return all of them\n",
    " - For split inputs, use a stride of 128 tokens so that there is some overlap between them\n",
    " - Return the character offsets into the original text of each returned token so we can calculate the start and end token indices\n",
    "2. Iterate through all of the returned features, finding the start and end tokens of the answer if they exist in that input. Otherwise default to (0,0), meaning the answer does not exist in that input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "CdjgqHLq7SPM"
   },
   "outputs": [],
   "source": [
    "samples = mlqa['test'][70]\n",
    "batch = tk.encode_plus(\n",
    "        samples['question'], \n",
    "        samples['context'], \n",
    "        padding='max_length', \n",
    "        truncation='only_second',\n",
    "        stride=128,\n",
    "        return_overflowing_tokens=True,\n",
    "        return_offsets_mapping=True\n",
    "    )\n",
    "\n",
    "# Get a list which maps the input features index to their original index in the \n",
    "# samples list (for split inputs). E.g. if our batch size is 4 and the second sample\n",
    "# is split into 3 inputs because it is very large, sample_mapping would look like\n",
    "# [0, 1, 1, 1, 2, 3]\n",
    "sample_mapping = batch.pop('overflow_to_sample_mapping')\n",
    "# Get all of the character offsets for each token\n",
    "offset_mapping = batch.pop('offset_mapping')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iqxQhkSEG0Pj",
    "outputId": "14862c11-7f82-4d38-e055-09638a0c9d67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['input_ids', 'attention_mask']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(batch.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DeDu1U-Z7eIg",
    "outputId": "83dc14d4-ef2f-4a86-b9cc-e728dac2b201"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "doFQoSw77sHX",
    "outputId": "a19f7d28-916f-4874-9a02-fdd8b6adc7e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(offset_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9XPqj4rL8xca",
    "outputId": "f3b20189-d703-466a-de71-337d56428dfd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0),\n",
       " (0, 4),\n",
       " (5, 7),\n",
       " (8, 11),\n",
       " (12, 14),\n",
       " (14, 20),\n",
       " (21, 23),\n",
       " (24, 28),\n",
       " (29, 33),\n",
       " (34, 37),\n",
       " (37, 41),\n",
       " (41, 42),\n",
       " (43, 46),\n",
       " (47, 48),\n",
       " (47, 53),\n",
       " (54, 57),\n",
       " (57, 58),\n",
       " (0, 0),\n",
       " (0, 0),\n",
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       " (5, 7),\n",
       " (8, 12),\n",
       " (12, 16),\n",
       " (16, 19),\n",
       " (19, 23),\n",
       " (24, 27),\n",
       " (28, 31),\n",
       " (32, 39),\n",
       " (40, 50),\n",
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       " (52, 56),\n",
       " (56, 57),\n",
       " (58, 61),\n",
       " (62, 68),\n",
       " (69, 71),\n",
       " (72, 73),\n",
       " (74, 79),\n",
       " (80, 87),\n",
       " (88, 95),\n",
       " (95, 96),\n",
       " (97, 100),\n",
       " (101, 104),\n",
       " (105, 110),\n",
       " (111, 116),\n",
       " (117, 120),\n",
       " (121, 122),\n",
       " (122, 123),\n",
       " (124, 127),\n",
       " (127, 131)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset_mapping[0][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "XQRzyhyEfS1X"
   },
   "outputs": [],
   "source": [
    "def get_train_features(tk, samples):\n",
    "  '''\n",
    "  Tokenizes all of the text in the given samples, splittling inputs that are too long for our model\n",
    "  across multiple features. Finds the token offsets of the answers, which serve as the labels for\n",
    "  our inputs.\n",
    "  '''\n",
    "  batch = tk.batch_encode_plus(\n",
    "        [[q,c] for q,c in zip(samples['question'], samples['context'])], \n",
    "        padding='max_length', \n",
    "        truncation='only_second',\n",
    "        stride=128,\n",
    "        return_overflowing_tokens=True,\n",
    "        return_offsets_mapping=True\n",
    "    )\n",
    "\n",
    "  # Get a list which maps the input features index to their original index in the \n",
    "  # samples list (for split inputs). E.g. if our batch size is 4 and the second sample\n",
    "  # is split into 3 inputs because it is very large, sample_mapping would look like\n",
    "  # [0, 1, 1, 1, 2, 3]\n",
    "  sample_mapping = batch.pop('overflow_to_sample_mapping')\n",
    "  # Get all of the character offsets for each token\n",
    "  offset_mapping = batch.pop('offset_mapping')\n",
    "\n",
    "  # Store the start and end tokens\n",
    "  batch['start_tokens'] = []\n",
    "  batch['end_tokens'] = []\n",
    "\n",
    "  # Iterate through all of the offsets\n",
    "  for i, offsets in enumerate(offset_mapping):\n",
    "    # Get the right sample by mapping it to its original index\n",
    "    sample_idx = sample_mapping[i]\n",
    "    # Get the sequence IDs to know where context starts so we can ignore question tokens\n",
    "    sequence_ids = batch.sequence_ids(i)\n",
    "\n",
    "    # Get the start and end character positions of the answer\n",
    "    ans = samples['answers'][sample_idx]\n",
    "    start_char = ans['answer_start'][0]\n",
    "    end_char = start_char + len(ans['text'][0])\n",
    "    # while end_char > 0 and (end_char >= len(samples['context'][sample_idx]) or samples['context'][sample_idx][end_char] == ' '):\n",
    "    #   end_char -= 1\n",
    "\n",
    "    # Start from the first token in the context, which can be found by going to the \n",
    "    # first token where sequence_ids is 1\n",
    "    start_token = 0\n",
    "    while sequence_ids[start_token] != 1:\n",
    "      start_token += 1\n",
    "\n",
    "    end_token = len(offsets) - 1\n",
    "    while sequence_ids[end_token] != 1:\n",
    "      end_token -= 1\n",
    "\n",
    "    # By default set it to the CLS token if the answer isn't in this input\n",
    "    if start_char < offsets[start_token][0] or end_char > offsets[end_token][1]:\n",
    "      start_token = 0\n",
    "      end_token = 0\n",
    "    # Otherwise find the correct token indices\n",
    "    else:\n",
    "      # Advance the start token index until we have passed the start character index \n",
    "      while start_token < len(offsets) and offsets[start_token][0] <= start_char:\n",
    "        start_token += 1\n",
    "      start_token -= 1\n",
    "      \n",
    "      # Decrease the end token index until we have passed the end character index\n",
    "      while end_token >= 0 and offsets[end_token][1] >= end_char:\n",
    "        end_token -= 1\n",
    "      end_token += 1\n",
    "\n",
    "    batch['start_tokens'].append(start_token)\n",
    "    batch['end_tokens'].append(end_token)\n",
    "\n",
    "  #batch['start_tokens'] = np.array(batch['start_tokens'])\n",
    "  #batch['end_tokens'] = np.array(batch['end_tokens'])\n",
    "\n",
    "  return batch\n",
    "\n",
    "def collate_fn(inputs):\n",
    "  '''\n",
    "  Defines how to combine different samples in a batch\n",
    "  '''\n",
    "  input_ids = torch.tensor([i['input_ids'] for i in inputs])\n",
    "  attention_mask = torch.tensor([i['attention_mask'] for i in inputs])\n",
    "  start_tokens = torch.tensor([i['start_tokens'] for i in inputs])\n",
    "  end_tokens = torch.tensor([i['end_tokens'] for i in inputs])\n",
    "\n",
    "  # Truncate to max length\n",
    "  max_len = max(attention_mask.sum(-1))\n",
    "  input_ids = input_ids[:,:max_len]\n",
    "  attention_mask = attention_mask[:,:max_len]\n",
    "  \n",
    "  return {'input_ids': input_ids, 'attention_mask': attention_mask, 'start_tokens': start_tokens, 'end_tokens': end_tokens}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tx2KcgYuOWjM"
   },
   "source": [
    "We can easily tokenize the whole dataset by calling the \"map\" function on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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      "1df0135be0d84c78a85d428403b977da"
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    },
    "id": "TBWSklJXf_q-",
    "outputId": "c3455ed8-c7ed-4d21-f114-6df3ff8115a3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91e962c8df9c43fca98ded3c08ddeea1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_dataset = mlqa['test'].map(partial(get_train_features, tk), batched=True, remove_columns=mlqa['test'].column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7jYiwsNysefS",
    "outputId": "ac64c0e0-869a-4732-be94-a294c98947e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['attention_mask', 'end_tokens', 'input_ids', 'start_tokens'],\n",
       "    num_rows: 13254\n",
       "})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KiiMnOgItz1q",
    "outputId": "d861c3b8-7c41-4d68-e028-b4442cce4a9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 113, 118),\n",
       " (1, 66, 73),\n",
       " (2, 205, 246),\n",
       " (3, 155, 166),\n",
       " (4, 13, 14),\n",
       " (5, 50, 51),\n",
       " (6, 68, 69),\n",
       " (7, 61, 62),\n",
       " (8, 91, 92),\n",
       " (9, 13, 15),\n",
       " (10, 377, 381),\n",
       " (11, 218, 221),\n",
       " (12, 109, 111),\n",
       " (13, 62, 67),\n",
       " (14, 14, 19),\n",
       " (15, 47, 52),\n",
       " (16, 21, 35),\n",
       " (17, 40, 44),\n",
       " (18, 223, 243),\n",
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       " (20, 112, 115),\n",
       " (21, 47, 50),\n",
       " (22, 29, 29),\n",
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       " (34, 43, 44),\n",
       " (35, 0, 0),\n",
       " (36, 0, 0),\n",
       " (37, 180, 187),\n",
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       " (39, 149, 152),\n",
       " (40, 46, 49),\n",
       " (41, 0, 0),\n",
       " (42, 182, 186),\n",
       " (43, 0, 0),\n",
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       " (45, 43, 58),\n",
       " (46, 92, 95),\n",
       " (47, 241, 242),\n",
       " (48, 157, 159),\n",
       " (49, 231, 244)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(range(len(tokenized_dataset['start_tokens'])),tokenized_dataset['start_tokens'],tokenized_dataset['end_tokens']))[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jQtFcygcupR8",
    "outputId": "3c076b51-580c-4ff6-88cc-bca3877e797f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'answers': {'answer_start': [826],\n",
       "  'text': ['the USAF had illegally handled toxic materials, and that the EPA had failed in its duty to enforce the Resource Conservation and Recovery Act (which governs handling of dangerous materials)']},\n",
       " 'context': 'In 1994, five unnamed civilian contractors and the widows of contractors Walter Kasza and Robert Frost sued the USAF and the United States Environmental Protection Agency. Their suit, in which they were represented by George Washington University law professor Jonathan Turley, alleged they had been present when large quantities of unknown chemicals had been burned in open pits and trenches at Groom. Biopsies taken from the complainants were analyzed by Rutgers University biochemists, who found high levels of dioxin, dibenzofuran, and trichloroethylene in their body fat. The complainants alleged they had sustained skin, liver, and respiratory injuries due to their work at Groom, and that this had contributed to the deaths of Frost and Kasza. The suit sought compensation for the injuries they had sustained, claiming the USAF had illegally handled toxic materials, and that the EPA had failed in its duty to enforce the Resource Conservation and Recovery Act (which governs handling of dangerous materials). They also sought detailed information about the chemicals to which they were allegedly exposed, hoping this would facilitate the medical treatment of survivors. Congressman Lee H. Hamilton, former chairman of the House Intelligence Committee, told 60 Minutes reporter Lesley Stahl, \"The Air Force is classifying all information about Area 51 in order to protect themselves from a lawsuit.\"',\n",
       " 'id': '04ecd5555635bc05fd2f379d1b9027edd663cebf',\n",
       " 'question': 'What was the law suit against Groom about'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlqa['test'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "ylHsAkOFu-oO",
    "outputId": "a99829c0-b441-4862-e232-c1e5b42b6dea"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'the USAF had illegally handled toxic materials, and that the EPA had failed in its duty to enforce the Resource Conservation and Recovery Act (which governs handling of dangerous materials).'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tk.decode(tokenized_dataset['input_ids'][2][205:247])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "iK2esswRUPRv"
   },
   "outputs": [],
   "source": [
    "samples = random.sample(list(range(len(tokenized_dataset))), 4000)\n",
    "tokenized_dataset = tokenized_dataset.select(samples)\n",
    "train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P2zZx8FNO0KR"
   },
   "source": [
    "Here we have the main training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-lNot1dlrL0W"
   },
   "outputs": [],
   "source": [
    "def train(\n",
    "    model: nn.Module, \n",
    "    train_dl: DataLoader, \n",
    "    optimizer: torch.optim.Optimizer, \n",
    "    schedule: LambdaLR,\n",
    "    n_epochs: int, \n",
    "    device: torch.device\n",
    "):\n",
    "  \"\"\"\n",
    "  The main training loop which will optimize a given model on a given dataset\n",
    "  :param model: The model being optimized\n",
    "  :param train_dl: The training dataset\n",
    "  :param optimizer: The optimizer used to update the model parameters\n",
    "  :param n_epochs: Number of epochs to train for\n",
    "  :param device: The device to train on\n",
    "  \"\"\"\n",
    "\n",
    "  # Keep track of the loss and best accuracy\n",
    "  losses = []\n",
    "  best_acc = 0.0\n",
    "  pcounter = 0\n",
    "\n",
    "  # Iterate through epochs\n",
    "  for ep in range(n_epochs):\n",
    "\n",
    "    loss_epoch = []\n",
    "\n",
    "    #Iterate through each batch in the dataloader\n",
    "    for batch in tqdm(train_dl):\n",
    "      # VERY IMPORTANT: Make sure the model is in training mode, which turns on \n",
    "      # things like dropout and layer normalization\n",
    "      model.train()\n",
    "\n",
    "      # VERY IMPORTANT: zero out all of the gradients on each iteration -- PyTorch\n",
    "      # keeps track of these dynamically in its computation graph so you need to explicitly\n",
    "      # zero them out\n",
    "      optimizer.zero_grad()\n",
    "\n",
    "      # Place each tensor on the GPU\n",
    "      batch = {b: batch[b].to(device) for b in batch}\n",
    "\n",
    "      # Pass the inputs through the model, get the current loss and logits\n",
    "      outputs = model(\n",
    "          input_ids=batch['input_ids'],\n",
    "          attention_mask=batch['attention_mask'],\n",
    "          start_positions=batch['start_tokens'],\n",
    "          end_positions=batch['end_tokens']\n",
    "      )\n",
    "      loss = outputs['loss']\n",
    "      losses.append(loss.item())\n",
    "      loss_epoch.append(loss.item())\n",
    "      \n",
    "      # Calculate all of the gradients and weight updates for the model\n",
    "      loss.backward()\n",
    "\n",
    "      # Optional: clip gradients\n",
    "      #torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "      # Finally, update the weights of the model and advance the LR schedule\n",
    "      optimizer.step()\n",
    "      scheduler.step()\n",
    "      #gc.collect()\n",
    "  return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GAi2BCkZO6Qj"
   },
   "source": [
    "We load the model using HuggingFace's AutoModel interface, which again just needs us to specify the ID of the model we wish to use. Additionally, we specify using \"AutoModelForQuestionAnswering\", which includes a classifier on top of the base model and allows us to input labels for the start and end token of the answer, handling computation of the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 154,
     "referenced_widgets": [
      "1dec7194aa8e4d70a7eb94f36a4edcb4",
      "9fb7ac48747f41799b9c84e12ce6e6c0",
      "a32315e41e844e3885f02172ba4ace11",
      "126e71f49ac94df6a5f2cdb3ad8e7a24",
      "ead70f96cb7649669c9803aebc93dbe2",
      "1746a93665fe4bfdbdcf61d508b0659a",
      "4b457dfa947c40a2aa199d7270c4a1fb",
      "18f46e89cc81447a8da67b1a6541b9f5",
      "a1692da6cfd7426ba4e6ac96b8939a00",
      "978ec16bb13c4bb98352e1f35433d4c9",
      "228f8b7fc26c487ca1ca61e7241e1c47"
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    },
    "id": "Pao8GVgppm5D",
    "outputId": "dcd10587-132c-46bb-8186-b2a0e02520b5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dec7194aa8e4d70a7eb94f36a4edcb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForQuestionAnswering: ['lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight']\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": [
    "model = AutoModelForQuestionAnswering.from_pretrained(MODEL_NAME).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OJRWSQOPPfSY"
   },
   "source": [
    "Here we create the optimizer, which is the weighted Adam optimizer used in the BERT paper. We also add weight decay to all parameters in the model except for LayerNormalization and bias parameters. Finally, we create a learning rate schedule which linearly increases the learning rate to a max value for a certain number of steps, then linearly decreases it to 0 over the course of training (this has been shown to improve training for pre-trained transformers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h1q3aeOjrKeN"
   },
   "outputs": [],
   "source": [
    "# Create the optimizer\n",
    "lr=2e-5\n",
    "n_epochs = 3\n",
    "weight_decay = 0.01\n",
    "warmup_steps = 200\n",
    "\n",
    "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "optimizer_grouped_parameters = [\n",
    "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "      'weight_decay': weight_decay},\n",
    "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "]\n",
    "# optimizer = Adam(optimizer_grouped_parameters, lr=1e-3)\n",
    "# scheduler = None\n",
    "optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    warmup_steps,\n",
    "    n_epochs * len(train_dl)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6GOvUC6yP_7G"
   },
   "source": [
    "Train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g7ijFE0kskYN",
    "outputId": "a8f11246-04a4-46fd-a7ef-e422db0db7b9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [13:01<00:00,  1.28it/s]\n",
      "100%|██████████| 1000/1000 [13:01<00:00,  1.28it/s]\n",
      "100%|██████████| 1000/1000 [12:58<00:00,  1.28it/s]\n"
     ]
    }
   ],
   "source": [
    "losses = train(\n",
    "    model, \n",
    "    train_dl,\n",
    "    optimizer, \n",
    "    scheduler,\n",
    "    n_epochs, \n",
    "    device\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "daqiHrDlQDiy"
   },
   "source": [
    "## How are answers Evaluated ?\n",
    "\n",
    "The next step is running our validation data through the model, getting predictions for the start and end tokens, converting these back into text from the original context, and evaluating using an appropriate metric.\n",
    "\n",
    "Getting the absolute best answer span is non-trivial. In the simplest case, we can sort the logits for the start token prediction, sort the logits for the end token prediction, and take the max of each. But the start and end tokens can be predicted anywhere in the input. What if the end is predicted before the start (i.e. the answer is impossible)? We then need a way to determine what is the \"second\" best answer.\n",
    "\n",
    "To do this, we need some way to score the possible valid answers. A valid answer is one where the start token comes before the end token, and the tokens are within the context part of the input. As a scoring function, we will simply add the value of the start token logits with the value of the end token logits. To get candidates, we will then iterate through the highest $N$ scoring logits from the start and end logits, where $N$ is a hyperparameter, determine if the pair is valid, and if so add it to a list of candidate answers. We can then rank all of the candidate answers by their score and take the highest scoring answer.\n",
    "\n",
    "Going back to answer text then involves using the same character offset map we used in training to get the start and end character offsets based on the token index. As such, we'll do a similar procedure to get features for the validation samples, this time storing the ID of each sample so we can map them back to the original text, as well as storing the offsets to map back the characters. We don't need to calculate the start and end token positions in this case because we will evaluate the model based on the retrieved answer text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JwNH06lbuhs2"
   },
   "outputs": [],
   "source": [
    "def get_validation_features(tk, samples):\n",
    "  # First, tokenize the text. We get the offsets and return overflowing sequences in\n",
    "  # order to break up long sequences into multiple inputs. The offsets will help us \n",
    "  # determine the original answer text \n",
    "  batch = tk.batch_encode_plus(\n",
    "        [[q,c] for q,c in zip(samples['question'], samples['context'])], \n",
    "        padding='max_length', \n",
    "        truncation='only_second',\n",
    "        stride=128,\n",
    "        return_overflowing_tokens=True,\n",
    "        return_offsets_mapping=True\n",
    "    )\n",
    "  \n",
    "  # We'll store the ID of the samples to calculate squad score\n",
    "  batch['example_id'] = []\n",
    "  # The overflow sample map tells us which input each sample corresponds to\n",
    "  sample_map = batch.pop('overflow_to_sample_mapping')\n",
    "\n",
    "  for i in range(len(batch['input_ids'])):\n",
    "    # The sample index tells us which of the values in \"samples\" these features belong to\n",
    "    sample_idx = sample_map[i]\n",
    "    sequence_ids = batch.sequence_ids(i)\n",
    "\n",
    "    # Add the ID to map these features back to the correct sample\n",
    "    batch['example_id'].append(samples['id'][sample_idx])\n",
    "\n",
    "    #Set offsets for non-context words to be None for ease of processing\n",
    "    batch['offset_mapping'][i] = [o if sequence_ids[k] == 1 else None for k,o in enumerate(batch['offset_mapping'][i])]\n",
    "\n",
    "  return batch  \n",
    "\n",
    "def val_collate_fn(inputs):\n",
    "  input_ids = torch.tensor([i['input_ids'] for i in inputs])\n",
    "  attention_mask = torch.tensor([i['attention_mask'] for i in inputs])\n",
    "\n",
    "  # Truncate to max length\n",
    "  max_len = max(attention_mask.sum(-1))\n",
    "  input_ids = input_ids[:,:max_len]\n",
    "  attention_mask = attention_mask[:,:max_len]\n",
    "  \n",
    "  return {'input_ids': input_ids, 'attention_mask': attention_mask}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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      "424737ffbc734801bd19a39b4e2ae3c5"
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    "id": "oHKF-5m5xr-O",
    "outputId": "c89a34e9-188e-4b16-82ad-a1bcafe3eccc"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25b4921546cc4f85b5efee9f5888f9d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_dataset = mlqa['validation'].map(partial(get_validation_features, tk), batched=True, remove_columns=mlqa['validation'].column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cB53z9Kmx7LC"
   },
   "outputs": [],
   "source": [
    "def predict(model: nn.Module, valid_dl: DataLoader):\n",
    "  \"\"\"\n",
    "  Evaluates the model on the given dataset\n",
    "  :param model: The model under evaluation\n",
    "  :param valid_dl: A `DataLoader` reading validation data\n",
    "  :return: The accuracy of the model on the dataset\n",
    "  \"\"\"\n",
    "  # VERY IMPORTANT: Put your model in \"eval\" mode -- this disables things like \n",
    "  # layer normalization and dropout\n",
    "  model.eval()\n",
    "  start_logits_all = []\n",
    "  end_logits_all = []\n",
    "\n",
    "  # ALSO IMPORTANT: Don't accumulate gradients during this process\n",
    "  with torch.no_grad():\n",
    "    for batch in tqdm(valid_dl, desc='Evaluation'):\n",
    "      batch = {b: batch[b].to(device) for b in batch}\n",
    "\n",
    "      # Pass the inputs through the model, get the current loss and logits\n",
    "      outputs = model(\n",
    "          input_ids=batch['input_ids'],\n",
    "          attention_mask=batch['attention_mask']\n",
    "      )\n",
    "      # Store the \"start\" class logits and \"end\" class logits for every token in the input\n",
    "      start_logits_all.extend(list(outputs['start_logits'].detach().cpu().numpy()))\n",
    "      end_logits_all.extend(list(outputs['end_logits'].detach().cpu().numpy()))\n",
    "\n",
    "\n",
    "    return start_logits_all,end_logits_all\n",
    "\n",
    "def post_process_predictions(examples, dataset, logits, num_possible_answers = 20, max_answer_length = 30):\n",
    "  all_start_logits, all_end_logits = logits\n",
    "  # Build a map from example to its corresponding features. This will allow us to index from\n",
    "  # sample ID to all of the features for that sample (in case they were split up due to long input)\n",
    "  example_id_to_index = {k: i for i, k in enumerate(examples[\"id\"])}\n",
    "  features_per_example = defaultdict(list)\n",
    "  for i, feature in enumerate(dataset):\n",
    "      features_per_example[example_id_to_index[feature[\"example_id\"]]].append(i)\n",
    "\n",
    "  # Create somewhere to store our predictions\n",
    "  predictions = OrderedDict()\n",
    "\n",
    "  # Iterate through each sample in the dataset\n",
    "  for j, sample in enumerate(tqdm(examples)):\n",
    "\n",
    "    # Get the feature indices (all of the features split across the batch)\n",
    "    feature_indices = features_per_example[j]\n",
    "    # Get the original context which predumably has the answer text\n",
    "    context = sample['context']\n",
    "\n",
    "    preds = []\n",
    "    # Iterate through all of the features\n",
    "    for ft_idx in feature_indices:\n",
    "\n",
    "      # Get the start and end answer logits for this input\n",
    "      start_logits = all_start_logits[ft_idx]\n",
    "      end_logits = all_end_logits[ft_idx]\n",
    "\n",
    "      # Get the offsets to map token indices to character indices\n",
    "      offset_mapping = dataset[ft_idx]['offset_mapping']\n",
    "\n",
    "      # Sort the logits and take the top N\n",
    "      start_indices = np.argsort(start_logits)[::-1][:num_possible_answers]\n",
    "      end_indices = np.argsort(end_logits)[::-1][:num_possible_answers]\n",
    "\n",
    "      # Iterate through start and end indices\n",
    "      for start_index in start_indices:\n",
    "        for end_index in end_indices:\n",
    "          \n",
    "          # Ignore this combination if either the indices are not in the context\n",
    "          if start_index >= len(offset_mapping) or end_index >= len(offset_mapping) or offset_mapping[start_index] is None or offset_mapping[end_index] is None:\n",
    "            continue\n",
    "\n",
    "          # Also ignore if the start index is greater than the end index of the number of tokens\n",
    "          # is greater than some specified threshold\n",
    "          if start_index > end_index or end_index - start_index + 1 > max_answer_length:\n",
    "            continue\n",
    "\n",
    "          ans_text = context[offset_mapping[start_index][0]:offset_mapping[end_index][1]]\n",
    "          preds.append({\n",
    "              'score': start_logits[start_index] + end_logits[end_index],\n",
    "              'text': ans_text\n",
    "          })\n",
    "\n",
    "    if len(preds) > 0:\n",
    "      # Sort by score to get the top answer\n",
    "      answer = sorted(preds, key=lambda x: x['score'], reverse=True)[0]\n",
    "    else:\n",
    "      answer = {'score': 0.0, 'text': \"\"}\n",
    "          \n",
    "    predictions[sample['id']] = answer['text']\n",
    "  return predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0upXUFTGkaxb"
   },
   "source": [
    "Create the DataLoader and run prediction!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qsGK-W8hz2Yt"
   },
   "outputs": [],
   "source": [
    "val_dl = DataLoader(validation_dataset, collate_fn=val_collate_fn, batch_size=32)\n",
    "logits = predict(model, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hY_Rf0BAz479"
   },
   "outputs": [],
   "source": [
    "predictions = post_process_predictions(mlqa['validation'], validation_dataset, logits)\n",
    "formatted_predictions = [{'id': k, 'prediction_text': v} for k,v in predictions.items()]\n",
    "gold = [{'id': example['id'], 'answers': example['answers']} for example in mlqa['validation']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y9sZFzHrkek-"
   },
   "source": [
    "We're using the official SQuAD evaluation metric which measure exact span match as well as token-level F1 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fa7xO83d9Du1",
    "outputId": "aac00fca-9f8b-4a61-84df-8990906a847e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'exact_match': 57.055749128919864, 'f1': 72.38330713792288}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "{'exact_match': 57.055749128919864, 'f1': 72.38330713792288}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_squad(references=gold, predictions=formatted_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilingual transformers\n",
    " \n",
    "While usually Transformer models are trained on a dataset made of a single language (e.g. the now classic BERT model), it is just as simple and easy to train them on a dataset that contains texts in more than one. For example, XLM-RoBERTa was trained on texts in more than 100 languages!\n",
    "\n",
    "Are those models any good?\n",
    "\n",
    "**Question:** What are the possible advantages of using a multilimgual model?\n",
    "\n",
    "<span style=\"color:white\">(generalization, shared representation, cross-lingual training and inference, stronger performance on low resource languages.)\n",
    "</span>.\n",
    "\n",
    "\n",
    "Are those models any good, though?\n",
    "Let's test one of those models - XLM-RoBERTa that was later fine-tuned on an **English only** QA dataset.\n",
    "\n",
    "It can correctly answer questions in English, nothing remarkable here.\n",
    "\n",
    "![example1](../../img/eng-eng.jpeg)\n",
    "\n",
    "\n",
    "It can also answer questions in Danish! Not bad.\n",
    "\n",
    "![example2](../../img/eng-eng.jpeg)\n",
    "\n",
    "What about a question asked in English with a Danish context?\n",
    "\n",
    "![example3](../../img/en-dk.jpeg)\n",
    "\n",
    "Easy peasy. Let's take it to the extreme, what about this chimeric monstrosity?\n",
    "\n",
    "![example4](../../img/chimera.jpeg)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Quite remarkable!\n",
    "\n",
    "![example4](../../img/chimera-ans.jpeg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yvLqvMH5koyw"
   },
   "source": [
    "## Training on other languages\n",
    "\n",
    "Lets test those capabilities ourselves using the MLQA datast. Let's see how a model trained on German performs on English"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 135,
     "referenced_widgets": [
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    "id": "7RP9ZOK5qnSQ",
    "outputId": "1d5ea027-d19c-4572-c8ff-51bb2a6f551a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset mlqa/mlqa-translate-train.de (download: 60.43 MiB, generated: 84.23 MiB, post-processed: Unknown size, total: 144.66 MiB) to /root/.cache/huggingface/datasets/mlqa/mlqa-translate-train.de/1.0.0/1a1ae267d8d9e8e9ff25bd8811a27c5f8752ee58c5d75cf6c6451cbaba777c87...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "592da8b21ea14f879a6695dfbed4efcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/63.4M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8053fd98666d4a7780e5235db42a744b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ab0dbb45a574dfa81141ea3e537ab44",
       "version_major": 2,
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      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset mlqa downloaded and prepared to /root/.cache/huggingface/datasets/mlqa/mlqa-translate-train.de/1.0.0/1a1ae267d8d9e8e9ff25bd8811a27c5f8752ee58c5d75cf6c6451cbaba777c87. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
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       "model_id": "68d09241839149d2ae4da1a32ae412b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "german_dataset = load_dataset('mlqa', 'mlqa-translate-train.de')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 154,
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    "id": "nWPWRL4Sq7jt",
    "outputId": "d395c37b-f70f-4293-ad92-8f75c76aacd6"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "904f7b72ef914de0ac0db781c71c625c",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/81 [00:00<?, ?ba/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForQuestionAnswering: ['lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight']\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": [
    "tokenized_dataset = german_dataset['train'].map(partial(get_train_features, tk), batched=True, remove_columns=german_dataset['train'].column_names)\n",
    "german_model = AutoModelForQuestionAnswering.from_pretrained(MODEL_NAME).to(device)\n",
    "#train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Krsc6BhM0965"
   },
   "outputs": [],
   "source": [
    "samples = random.sample(list(range(len(tokenized_dataset))), 4000)\n",
    "tokenized_dataset = tokenized_dataset.select(samples)\n",
    "train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ta-NBB8sq8wj",
    "outputId": "5c7eb18b-e283-4899-b57a-37754b99c11b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [10:06<00:00,  1.65it/s]\n",
      "100%|██████████| 1000/1000 [10:11<00:00,  1.64it/s]\n",
      "100%|██████████| 1000/1000 [10:09<00:00,  1.64it/s]\n"
     ]
    }
   ],
   "source": [
    "# Create the optimizer\n",
    "lr=2e-5\n",
    "n_epochs = 3\n",
    "weight_decay = 0.01\n",
    "warmup_steps = 200\n",
    "\n",
    "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "optimizer_grouped_parameters = [\n",
    "    {'params': [p for n, p in german_model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "      'weight_decay': weight_decay},\n",
    "    {'params': [p for n, p in german_model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "]\n",
    "# optimizer = Adam(optimizer_grouped_parameters, lr=1e-3)\n",
    "# scheduler = None\n",
    "optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    warmup_steps,\n",
    "    n_epochs * len(train_dl)\n",
    ")\n",
    "\n",
    "losses = train(\n",
    "    german_model, \n",
    "    train_dl,\n",
    "    optimizer, \n",
    "    scheduler,\n",
    "    n_epochs, \n",
    "    device\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rSbQU74vtHra",
    "outputId": "d717e85b-8d22-4c4e-96fb-f71996d2e1c7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 41/41 [01:29<00:00,  2.18s/it]\n"
     ]
    }
   ],
   "source": [
    "logits = predict(german_model, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KDpQXthc8omK",
    "outputId": "97fc94cb-1b7f-4ea4-8345-9c8d72fdfdc4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1148/1148 [00:03<00:00, 372.72it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'exact_match': 47.47386759581882, 'f1': 63.08390012502795}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = post_process_predictions(mlqa['validation'], validation_dataset, logits)\n",
    "formatted_predictions = [{'id': k, 'prediction_text': v} for k,v in predictions.items()]\n",
    "gold = [{'id': example['id'], 'answers': example['answers']} for example in mlqa['validation']]\n",
    "compute_squad(references=gold, predictions=formatted_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ytcsX-DVpP66"
   },
   "source": [
    "We can also try Chinese to English"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
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    "outputId": "45322494-1eec-493b-a80b-49175a3ad6ff"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset mlqa (/root/.cache/huggingface/datasets/mlqa/mlqa-translate-train.zh/1.0.0/1a1ae267d8d9e8e9ff25bd8811a27c5f8752ee58c5d75cf6c6451cbaba777c87)\n"
     ]
    },
    {
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zh_dataset = load_dataset('mlqa', 'mlqa-translate-train.zh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 154,
     "referenced_widgets": [
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c02f105b7cc84757ab3ea7a32430e8b4",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/77 [00:00<?, ?ba/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForQuestionAnswering: ['lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight']\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": [
    "tokenized_dataset = zh_dataset['train'].map(partial(get_train_features, tk), batched=True, remove_columns=zh_dataset['train'].column_names)\n",
    "german_model.to('cpu')\n",
    "zh_model = AutoModelForQuestionAnswering.from_pretrained(MODEL_NAME).to(device)\n",
    "#train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HKXnIvtDpsDx"
   },
   "outputs": [],
   "source": [
    "samples = random.sample(list(range(len(tokenized_dataset))), 4000)\n",
    "tokenized_dataset = tokenized_dataset.select(samples)\n",
    "train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z6vwoLakpu1Y",
    "outputId": "fc532856-d127-43bb-f6a7-ac597d098b20"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [57:17<00:00,  3.44s/it]\n",
      "100%|██████████| 1000/1000 [55:57<00:00,  3.36s/it]\n",
      "100%|██████████| 1000/1000 [56:41<00:00,  3.40s/it]\n"
     ]
    }
   ],
   "source": [
    "# Create the optimizer\n",
    "lr=2e-5\n",
    "n_epochs = 3\n",
    "weight_decay = 0.01\n",
    "warmup_steps = 200\n",
    "\n",
    "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "optimizer_grouped_parameters = [\n",
    "    {'params': [p for n, p in german_model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "      'weight_decay': weight_decay},\n",
    "    {'params': [p for n, p in german_model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "]\n",
    "# optimizer = Adam(optimizer_grouped_parameters, lr=1e-3)\n",
    "# scheduler = None\n",
    "optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    warmup_steps,\n",
    "    n_epochs * len(train_dl)\n",
    ")\n",
    "\n",
    "losses = train(\n",
    "    zh_model, \n",
    "    train_dl,\n",
    "    optimizer, \n",
    "    scheduler,\n",
    "    n_epochs, \n",
    "    device\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OhWbulgYp0Uw",
    "outputId": "217bdcf1-a81c-4644-bbd3-caed808e0bb6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 41/41 [01:28<00:00,  2.15s/it]\n"
     ]
    }
   ],
   "source": [
    "logits = predict(zh_model, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9uXAz98Ap2tn",
    "outputId": "e308ea3f-b739-4e35-fbe7-2db206493358"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1148/1148 [00:02<00:00, 458.57it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'exact_match': 0.17421602787456447, 'f1': 3.9269257222658576}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = post_process_predictions(mlqa['validation'], validation_dataset, logits)\n",
    "formatted_predictions = [{'id': k, 'prediction_text': v} for k,v in predictions.items()]\n",
    "gold = [{'id': example['id'], 'answers': example['answers']} for example in mlqa['validation']]\n",
    "compute_squad(references=gold, predictions=formatted_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1IDFEGrUqIGA"
   },
   "source": [
    "Finally, let's try German to English again but using a model pre-trained **only** on English. What are some obvious issues you can think of coming up?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "text": [
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForQuestionAnswering: ['lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight']\n",
      "- This IS expected if you are initializing RobertaForQuestionAnswering 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 RobertaForQuestionAnswering 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 RobertaForQuestionAnswering were not initialized from the model checkpoint at 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": [
    "tokenized_dataset = german_dataset['train'].map(partial(get_train_features, tk), batched=True, remove_columns=german_dataset['train'].column_names)\n",
    "german_model = AutoModelForQuestionAnswering.from_pretrained(MODEL_NAME).to(device)\n",
    "#train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FPBVbYSfqk0A"
   },
   "outputs": [],
   "source": [
    "samples = random.sample(list(range(len(tokenized_dataset))), 4000)\n",
    "tokenized_dataset = tokenized_dataset.select(samples)\n",
    "train_dl = DataLoader(tokenized_dataset, collate_fn=collate_fn, shuffle=True, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "69bi5xV2qn0D",
    "outputId": "2fc1b552-f5e8-4238-8b13-f18960f29485"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [13:52<00:00,  1.20it/s]\n",
      "100%|██████████| 1000/1000 [13:53<00:00,  1.20it/s]\n",
      "100%|██████████| 1000/1000 [13:49<00:00,  1.21it/s]\n"
     ]
    }
   ],
   "source": [
    "# Create the optimizer\n",
    "lr=2e-5\n",
    "n_epochs = 3\n",
    "weight_decay = 0.01\n",
    "warmup_steps = 200\n",
    "\n",
    "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "optimizer_grouped_parameters = [\n",
    "    {'params': [p for n, p in german_model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "      'weight_decay': weight_decay},\n",
    "    {'params': [p for n, p in german_model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "]\n",
    "# optimizer = Adam(optimizer_grouped_parameters, lr=1e-3)\n",
    "# scheduler = None\n",
    "optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    warmup_steps,\n",
    "    n_epochs * len(train_dl)\n",
    ")\n",
    "\n",
    "losses = train(\n",
    "    german_model, \n",
    "    train_dl,\n",
    "    optimizer, \n",
    "    scheduler,\n",
    "    n_epochs, \n",
    "    device\n",
    ")"
   ]
  },
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     "text": [
      "Evaluation: 100%|██████████| 40/40 [01:24<00:00,  2.12s/it]\n"
     ]
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   ],
   "source": [
    "validation_dataset = mlqa['validation'].map(partial(get_validation_features, tk), batched=True, remove_columns=mlqa['validation'].column_names)\n",
    "val_dl = DataLoader(validation_dataset, collate_fn=val_collate_fn, batch_size=32)\n",
    "logits = predict(german_model, val_dl)"
   ]
  },
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "100%|██████████| 1148/1148 [00:02<00:00, 431.26it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'exact_match': 10.365853658536585, 'f1': 21.88637627312505}"
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   ],
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    "predictions = post_process_predictions(mlqa['validation'], validation_dataset, logits)\n",
    "formatted_predictions = [{'id': k, 'prediction_text': v} for k,v in predictions.items()]\n",
    "gold = [{'id': example['id'], 'answers': example['answers']} for example in mlqa['validation']]\n",
    "compute_squad(references=gold, predictions=formatted_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I3je-hhlRt4-"
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   "source": [
    "!!"
   ]
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