MultiTask_Transformer_for_Token_Classification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/napsternxg/ipython-notebooks/blob/master/MultiTask_Transformer_for_Token_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ca9XNgJcXjh",
        "outputId": "459e1aad-fe64-405b-aa42-7c6e5223f4d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (2.0.0+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.28.1)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.11.0)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch) (1.11.1)\n",
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            "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch) (16.0.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.18.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.9/dist-packages (from datasets) (3.2.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.9/dist-packages (from datasets) (0.70.14)\n",
            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (2023.4.0)\n",
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            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install torch transformers datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prompt\n",
        "\n",
        "> Write the python code for a multi task Data Loader using HuggingFace datasets. It should suport token classification datasets. Remember to include the support for mapping token labels to their corresponding subword labels.\n",
        "\n",
        "\n",
        "Bing\n",
        "\n",
        "> Here’s a sample Python code for a multi-task data loader using HuggingFace datasets that supports token classification datasets and includes support for mapping token labels to their corresponding subword labels:\n",
        "\n",
        "> You can use this class by initializing it with the desired datasets and tokenizer name. For example:\n",
        "\n",
        "> This will load the `conll2003` dataset for the ner task and tokenize it using the `bert-base-cased` tokenizer. The resulting data will be a dictionary with keys corresponding to the tasks and values being the tokenized and aligned datasets. Is there anything else you would like to know?"
      ],
      "metadata": {
        "id": "_Vd_51ypcetS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer\n",
        "from functools import partial\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torch.utils.data import Sampler\n",
        "import itertools\n",
        "from transformers import DataCollatorForTokenClassification\n",
        "from transformers import AutoModel\n",
        "from torch import nn\n",
        "from transformers.modeling_outputs import TokenClassifierOutput\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "from torch.optim import Adam\n",
        "from tqdm.auto import tqdm\n",
        "import torch"
      ],
      "metadata": {
        "id": "_Oz3HxYDccP2"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MultiTaskDataSets:\n",
        "    def __init__(self, datasets, tokenizer_name):\n",
        "        self.datasets = datasets\n",
        "        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n",
        "\n",
        "    def tokenize_and_align_labels(self, examples, task=None):\n",
        "        tokenized_inputs = self.tokenizer(examples['tokens'], truncation=True, is_split_into_words=True)\n",
        "\n",
        "        labels = []\n",
        "        dataset_name, label_key = self.datasets[task]\n",
        "        for i, label in enumerate(examples[label_key]):            \n",
        "            word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "            # print(f\"{i=}, {word_ids=}, {label}\")\n",
        "            previous_word_idx = None\n",
        "            label_ids = []\n",
        "            for word_idx in word_ids:\n",
        "                if word_idx is None:\n",
        "                    label_ids.append(-100)\n",
        "                elif word_idx != previous_word_idx:\n",
        "                    label_ids.append(label[word_idx])\n",
        "                else:\n",
        "                    label_ids.append(-100)\n",
        "                previous_word_idx = word_idx\n",
        "            labels.append(label_ids)\n",
        "\n",
        "        tokenized_inputs['labels'] = labels\n",
        "        return tokenized_inputs\n",
        "\n",
        "    def load(self):\n",
        "        data = {}\n",
        "        self.label_to_id = {}\n",
        "        self.task_tokenize_and_align_labels_fns = {}\n",
        "        for task in self.datasets:\n",
        "            dataset_name, label_key = self.datasets[task]\n",
        "            dataset = load_dataset(*dataset_name)\n",
        "            first_split_key = list(dataset.keys())[0]\n",
        "            label_list = dataset[first_split_key].features[label_key].feature.names\n",
        "            self.label_to_id[task] = {l: i for i, l in enumerate(label_list)}\n",
        "            print(self.label_to_id[task])\n",
        "            self.task_tokenize_and_align_labels_fns[task] = partial(self.tokenize_and_align_labels, task=task)\n",
        "            data[task] = dataset.map(self.task_tokenize_and_align_labels_fns[task], batched=True)\n",
        "            data[task].set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])\n",
        "        return data\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "class RoundRobinSampler(Sampler):\n",
        "    def __init__(self, data_source):\n",
        "        self.data_source = data_source\n",
        "        self.task_indices = {task: list(range(len(data))) for task, data in data_source.items()}\n",
        "        self.task_iterators = {task: iter(indices) for task, indices in self.task_indices.items()}\n",
        "\n",
        "    def __iter__(self):\n",
        "        while True:\n",
        "            for task in self.task_indices:\n",
        "                try:\n",
        "                    yield next(self.task_iterators[task])\n",
        "                except StopIteration:\n",
        "                    self.task_iterators[task] = iter(self.task_indices[task])\n",
        "\n",
        "    def __len__(self):\n",
        "        return sum(len(indices) for indices in self.task_indices.values())\n",
        "\n",
        "\n",
        "class MultiTaskTokenClassificationModel(nn.Module):\n",
        "    def __init__(self, model_name, task_info):\n",
        "        super().__init__()\n",
        "        self.shared = AutoModel.from_pretrained(model_name, return_dict=True)\n",
        "        self.num_labels = {\n",
        "            task: info[\"num_labels\"]\n",
        "            for task, info in task_info.items()\n",
        "        }\n",
        "        self.classifiers = nn.ModuleDict({\n",
        "            task: nn.Linear(self.shared.config.hidden_size, info[\"num_labels\"])\n",
        "            for task, info in task_info.items()\n",
        "        })\n",
        "\n",
        "    def forward(self, task, input_ids, attention_mask=None, labels=None, return_dict=True):\n",
        "        outputs = self.shared(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        sequence_output = outputs.last_hidden_state\n",
        "        logits = self.classifiers[task](sequence_output)\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            loss_fct = CrossEntropyLoss()\n",
        "            loss = loss_fct(logits.view(-1, self.num_labels[task]), labels.view(-1))\n",
        "\n",
        "        if not return_dict:\n",
        "            output = (logits,) + outputs[2:]\n",
        "            return ((loss,) + output) if loss is not None else output\n",
        "\n",
        "        return TokenClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n",
        "\n",
        "class RoundRobinIterator:\n",
        "  def __init__(self, data_loaders):\n",
        "    self.data_loaders = data_loaders\n",
        "\n",
        "  def __iter__(self):\n",
        "    iters = {task: iter(data_loader) for task, data_loader in self.data_loaders.items()}\n",
        "    finished_tasks = set()\n",
        "    while len(finished_tasks) < len(iters):\n",
        "      for task, data_iter in iters.items():\n",
        "        if task in finished_tasks:\n",
        "          continue\n",
        "        try:\n",
        "          batch = next(data_iter)\n",
        "          yield task, batch\n",
        "        except StopIteration:\n",
        "          finished_tasks.add(task)\n",
        "          # iters[task] = iter(self.data_loaders[task])\n",
        "\n",
        "  def __len__(self):\n",
        "    return sum(len(data_loader) for data_loader in self.data_loaders.values())\n",
        "\n",
        "\n",
        "\n",
        "    "
      ],
      "metadata": {
        "id": "ze0jQUu8cmHq"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = {\n",
        "    'conll2003': (('conll2003',), 'ner_tags'),\n",
        "    'wnut_17': (('wnut_17',), 'ner_tags'),\n",
        "    # 'twitter_pos_foster': (('strombergnlp/twitter_pos', \"foster\"), 'pos_tags'),\n",
        "    'twitter_pos_ritter': (('strombergnlp/twitter_pos', \"ritter\"), 'pos_tags')\n",
        "}\n",
        "tokenizer_name = 'bert-base-cased'\n",
        "multitask_datasets = MultiTaskDataSets(datasets, tokenizer_name)\n",
        "data = multitask_datasets.load()\n",
        "data"
      ],
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            "a11a5e5269154e739d5cc6689e38a62c",
            "7472fcfe38b444588e642161ea05362b",
            "1b61565007c5433e83db6093393fc265",
            "4564c48443fa4cc0b8d77aa8c1b25ce9",
            "8a363db3a172441cacf796c35e274ff3",
            "9b1eae4e468c4455addcce271acba202",
            "94f19d7628b3403badd311f94732b16e",
            "fdc4c3520cf8493aacd52704cfe8be9c",
            "3c3a2261b054463395788ddc9f2e54fa",
            "dd1c952cd58d4d929f8da43c916fbf6a",
            "86533f4d8b644b589bf18da8b027f4e3",
            "29ccfa1d58f04c359fc019ce67de2b9a"
          ]
        },
        "id": "x7R7MpETcsen",
        "outputId": "c88a7d09-980b-49ee-fd6d-7989876bbdb2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset conll2003 (/root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98)\n"
          ]
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b74b0050d5834b3f9fbf0260bb39993c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98/cache-a513acb7d624a55e.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}\n"
          ]
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18b43fb449f4424f8ac3d9e6525c4cd0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98/cache-677260ba1d786166.arrow\n",
            "WARNING:datasets.builder:Found cached dataset wnut_17 (/root/.cache/huggingface/datasets/wnut_17/wnut_17/1.0.0/077c7f08b8dbc800692e8c9186cdf3606d5849ab0e7be662e6135bb10eba54f9)\n"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "89b5ec0393584c2a800ce08573a0f3ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/wnut_17/wnut_17/1.0.0/077c7f08b8dbc800692e8c9186cdf3606d5849ab0e7be662e6135bb10eba54f9/cache-f18b8855013eeed3.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/wnut_17/wnut_17/1.0.0/077c7f08b8dbc800692e8c9186cdf3606d5849ab0e7be662e6135bb10eba54f9/cache-4af4d4d524198dc1.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/wnut_17/wnut_17/1.0.0/077c7f08b8dbc800692e8c9186cdf3606d5849ab0e7be662e6135bb10eba54f9/cache-1c2219f6ba7c562c.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'O': 0, 'B-corporation': 1, 'I-corporation': 2, 'B-creative-work': 3, 'I-creative-work': 4, 'B-group': 5, 'I-group': 6, 'B-location': 7, 'I-location': 8, 'B-person': 9, 'I-person': 10, 'B-product': 11, 'I-product': 12}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset twitter_pos (/root/.cache/huggingface/datasets/strombergnlp___twitter_pos/ritter/0.0.0/f39a432da22d943ddc3ef140ab9cc12ecaa94631e3d740bbce2215e1a6cd53f5)\n"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/strombergnlp___twitter_pos/ritter/0.0.0/f39a432da22d943ddc3ef140ab9cc12ecaa94631e3d740bbce2215e1a6cd53f5/cache-e6e58f1e82e75f5c.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/strombergnlp___twitter_pos/ritter/0.0.0/f39a432da22d943ddc3ef140ab9cc12ecaa94631e3d740bbce2215e1a6cd53f5/cache-aa40942bad0dd294.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/strombergnlp___twitter_pos/ritter/0.0.0/f39a432da22d943ddc3ef140ab9cc12ecaa94631e3d740bbce2215e1a6cd53f5/cache-0adbc18e83ae9cf2.arrow\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'\"': 0, \"''\": 1, '#': 2, '%': 3, '$': 4, '(': 5, ')': 6, ',': 7, '.': 8, ':': 9, '``': 10, 'CC': 11, 'CD': 12, 'DT': 13, 'EX': 14, 'FW': 15, 'IN': 16, 'JJ': 17, 'JJR': 18, 'JJS': 19, 'LS': 20, 'MD': 21, 'NN': 22, 'NNP': 23, 'NNPS': 24, 'NNS': 25, 'NN|SYM': 26, 'PDT': 27, 'POS': 28, 'PRP': 29, 'PRP$': 30, 'RB': 31, 'RBR': 32, 'RBS': 33, 'RP': 34, 'SYM': 35, 'TO': 36, 'UH': 37, 'VB': 38, 'VBD': 39, 'VBG': 40, 'VBN': 41, 'VBP': 42, 'VBZ': 43, 'WDT': 44, 'WP': 45, 'WP$': 46, 'WRB': 47, 'RT': 48, 'HT': 49, 'USR': 50, 'URL': 51}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'conll2003': DatasetDict({\n",
              "     train: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 14041\n",
              "     })\n",
              "     validation: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 3250\n",
              "     })\n",
              "     test: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 3453\n",
              "     })\n",
              " }),\n",
              " 'wnut_17': DatasetDict({\n",
              "     train: Dataset({\n",
              "         features: ['id', 'tokens', 'ner_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 3394\n",
              "     })\n",
              "     validation: Dataset({\n",
              "         features: ['id', 'tokens', 'ner_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 1009\n",
              "     })\n",
              "     test: Dataset({\n",
              "         features: ['id', 'tokens', 'ner_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 1287\n",
              "     })\n",
              " }),\n",
              " 'twitter_pos_ritter': DatasetDict({\n",
              "     validation: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 118\n",
              "     })\n",
              "     test: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 118\n",
              "     })\n",
              "     train: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 551\n",
              "     })\n",
              " })}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prompt\n",
        "\n",
        "> Can you write a data sampler function which can make batches from this dataset. Make sure each batch is homogenous i.e. each batch should only contain a single task examples. Also make sure that the batches from each task are extracted in a round robin fashion.\n",
        "\n",
        "\n",
        "Bing\n",
        "> Sure! Here’s a sample Python code for a data sampler that can create batches from the multi-task dataset generated by the MultiTaskDataLoader class. This sampler ensures that each batch is homogenous (i.e., contains examples from only a single task) and that batches from each task are extracted in a round-robin fashion:\n",
        "\n"
      ],
      "metadata": {
        "id": "yCtJgUPrjvxM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wRrme4H8fGXw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rRmoI06HrPqq"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "afQcH_U4rYK1"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4\n",
        "collate_fn = DataCollatorForTokenClassification(multitask_datasets.tokenizer)\n",
        "\n",
        "# sampler = RoundRobinSampler(data)\n",
        "data_loaders = {task: DataLoader(dataset_dict['train'], batch_size=batch_size, collate_fn=collate_fn) for task, dataset_dict in data.items()}"
      ],
      "metadata": {
        "id": "6bn_pvoilTN7"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for task, data_loader in data_loaders.items():\n",
        "  for batch in data_loader:\n",
        "    print(task)\n",
        "    print({k: v.shape for k,v in batch.items()})\n",
        "    break  \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALDBOXJKmtjd",
        "outputId": "478d7c13-5fb2-4db6-d959-83dc3005ce4b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conll2003\n",
            "{'input_ids': torch.Size([4, 34]), 'attention_mask': torch.Size([4, 34]), 'labels': torch.Size([4, 34])}\n",
            "wnut_17\n",
            "{'input_ids': torch.Size([4, 35]), 'attention_mask': torch.Size([4, 35]), 'labels': torch.Size([4, 35])}\n",
            "twitter_pos_ritter\n",
            "{'input_ids': torch.Size([4, 87]), 'attention_mask': torch.Size([4, 87]), 'labels': torch.Size([4, 87])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PPrFyxbLsPsV"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6ZxPqpVytHp4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "task_info = {\n",
        "    task: dict(num_labels=len(label2ids))\n",
        "    for task, label2ids in multitask_datasets.label_to_id.items()\n",
        "}\n",
        "task_info"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckcA8Jj9sy6b",
        "outputId": "958f8158-b891-4074-c065-d2ec75f75fcf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'conll2003': {'num_labels': 9},\n",
              " 'wnut_17': {'num_labels': 13},\n",
              " 'twitter_pos_ritter': {'num_labels': 52}}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TC40Ip04uqXZ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qOVLgxzasN19"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"distilbert-base-uncased\"\n",
        "multitask_model = MultiTaskTokenClassificationModel(model_name=model_name, task_info=task_info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AeYh3g0HtS5B",
        "outputId": "92b5d9e7-d3a7-4567-efbf-8a937394a1df"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_transform.weight']\n",
            "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FxULKdLYwI7s"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = Adam(multitask_model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "zc_V8u_2wIWN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 2\n",
        "for epoch in range(num_epochs):\n",
        "  running_loss = 0.0\n",
        "  for task, data_loader in data_loaders.items():\n",
        "    for batch in data_loader:\n",
        "      optimizer.zero_grad()\n",
        "      output = multitask_model(task=task, **batch)\n",
        "      print(task)\n",
        "      print({k: v.shape for k,v in batch.items()})\n",
        "      print({k:v.shape for k,v in output.items()})\n",
        "      loss = output.loss\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "      running_loss += loss.item()\n",
        "      break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaP6SwSTt2M2",
        "outputId": "a72a8a74-29bb-4e1b-f66e-62686ecb1568"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conll2003\n",
            "{'input_ids': torch.Size([4, 34]), 'attention_mask': torch.Size([4, 34]), 'labels': torch.Size([4, 34])}\n",
            "{'loss': torch.Size([]), 'logits': torch.Size([4, 34, 9])}\n",
            "wnut_17\n",
            "{'input_ids': torch.Size([4, 35]), 'attention_mask': torch.Size([4, 35]), 'labels': torch.Size([4, 35])}\n",
            "{'loss': torch.Size([]), 'logits': torch.Size([4, 35, 13])}\n",
            "twitter_pos_ritter\n",
            "{'input_ids': torch.Size([4, 87]), 'attention_mask': torch.Size([4, 87]), 'labels': torch.Size([4, 87])}\n",
            "{'loss': torch.Size([]), 'logits': torch.Size([4, 87, 52])}\n",
            "conll2003\n",
            "{'input_ids': torch.Size([4, 34]), 'attention_mask': torch.Size([4, 34]), 'labels': torch.Size([4, 34])}\n",
            "{'loss': torch.Size([]), 'logits': torch.Size([4, 34, 9])}\n",
            "wnut_17\n",
            "{'input_ids': torch.Size([4, 35]), 'attention_mask': torch.Size([4, 35]), 'labels': torch.Size([4, 35])}\n",
            "{'loss': torch.Size([]), 'logits': torch.Size([4, 35, 13])}\n",
            "twitter_pos_ritter\n",
            "{'input_ids': torch.Size([4, 87]), 'attention_mask': torch.Size([4, 87]), 'labels': torch.Size([4, 87])}\n",
            "{'loss': torch.Size([]), 'logits': torch.Size([4, 87, 52])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ORbXw8n1ytzV"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "round_robin_iterator = RoundRobinIterator(data_loaders)\n",
        "len(round_robin_iterator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDHlrPwOz8fC",
        "outputId": "d8af364d-e86d-4db1-b886-f0632ee7252f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4498"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 2\n",
        "for epoch in range(num_epochs):\n",
        "  for task, batch in round_robin_iterator:\n",
        "      print(task)\n",
        "      print({k: v.shape for k,v in batch.items()})\n",
        "      print(batch[\"input_ids\"][:, :10])\n",
        "      # print({k:v.shape for k,v in output.items()})\n",
        "      break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_p30J0Vgt911",
        "outputId": "33a3f082-9f8f-43ff-ff39-2b16c2076069"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conll2003\n",
            "{'input_ids': torch.Size([4, 34]), 'attention_mask': torch.Size([4, 34]), 'labels': torch.Size([4, 34])}\n",
            "tensor([[  101,  7270, 22961,  1528,  1840,  1106, 21423,  1418,  2495, 12913],\n",
            "        [  101,  1943, 14428,   102,     0,     0,     0,     0,     0,     0],\n",
            "        [  101, 26660, 13329, 12649, 15928,  1820,   118,  4775,   118,  1659],\n",
            "        [  101,  1109,  1735,  2827,  1163,  1113,  9170,  1122, 19786,  1114]])\n",
            "conll2003\n",
            "{'input_ids': torch.Size([4, 34]), 'attention_mask': torch.Size([4, 34]), 'labels': torch.Size([4, 34])}\n",
            "tensor([[  101,  7270, 22961,  1528,  1840,  1106, 21423,  1418,  2495, 12913],\n",
            "        [  101,  1943, 14428,   102,     0,     0,     0,     0,     0,     0],\n",
            "        [  101, 26660, 13329, 12649, 15928,  1820,   118,  4775,   118,  1659],\n",
            "        [  101,  1109,  1735,  2827,  1163,  1113,  9170,  1122, 19786,  1114]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0ZvZgop_1N23"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CmB8ispz15H8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "heNTrvgY3kKI",
        "outputId": "fc66c7e2-c7df-485f-8c8e-a108d53f390a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Apr 17 02:00:14 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   39C    P8     9W /  70W |      3MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zQfYgejq38am"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6E7CAuQ35ov",
        "outputId": "fea927a2-86d7-4ae0-8b57-d529314fbc5f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"Twitter/twhin-bert-base\"\n",
        "multitask_model = MultiTaskTokenClassificationModel(model_name=model_name, task_info=task_info)\n",
        "\n",
        "\n",
        "datasets = {\n",
        "    'conll2003': (('conll2003',), 'ner_tags'),\n",
        "    'wnut_17': (('wnut_17',), 'ner_tags'),\n",
        "    # 'twitter_pos_foster': (('strombergnlp/twitter_pos', \"foster\"), 'pos_tags'),\n",
        "    'twitter_pos_ritter': (('strombergnlp/twitter_pos', \"ritter\"), 'pos_tags')\n",
        "}\n",
        "tokenizer_name = model_name\n",
        "multitask_datasets = MultiTaskDataSets(datasets, tokenizer_name)\n",
        "\n",
        "data = multitask_datasets.load()\n",
        "data"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "xm5zW1De7Evt",
        "outputId": "72289bf5-f04b-4977-918d-a9eba7450e4f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/632 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af90779cec5d48889bbdae671e1eada2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f49326df90fd40c6a1dfd519a9a41f71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at Twitter/twhin-bert-base were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/372 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ac3244261c64414f8f35eb39eddd4d55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.json:   0%|          | 0.00/17.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "89a8aeaaf2ab4ec9ae8c6754dadfc739"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64c8a408e6744e99a5f8071138663f55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset conll2003 (/root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98)\n"
          ]
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7caae05e26954c989d50dfb13b559d45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ae6542c376f5402f8713f43ad4f2d70c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9affa30c9f804057b659db5344b80ceb"
            }
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset wnut_17 (/root/.cache/huggingface/datasets/wnut_17/wnut_17/1.0.0/077c7f08b8dbc800692e8c9186cdf3606d5849ab0e7be662e6135bb10eba54f9)\n"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "76086338f9174e8eb51ec3d51f3816e8"
            }
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'O': 0, 'B-corporation': 1, 'I-corporation': 2, 'B-creative-work': 3, 'I-creative-work': 4, 'B-group': 5, 'I-group': 6, 'B-location': 7, 'I-location': 8, 'B-person': 9, 'I-person': 10, 'B-product': 11, 'I-product': 12}\n"
          ]
        },
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset twitter_pos (/root/.cache/huggingface/datasets/strombergnlp___twitter_pos/ritter/0.0.0/f39a432da22d943ddc3ef140ab9cc12ecaa94631e3d740bbce2215e1a6cd53f5)\n"
          ]
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'\"': 0, \"''\": 1, '#': 2, '%': 3, '$': 4, '(': 5, ')': 6, ',': 7, '.': 8, ':': 9, '``': 10, 'CC': 11, 'CD': 12, 'DT': 13, 'EX': 14, 'FW': 15, 'IN': 16, 'JJ': 17, 'JJR': 18, 'JJS': 19, 'LS': 20, 'MD': 21, 'NN': 22, 'NNP': 23, 'NNPS': 24, 'NNS': 25, 'NN|SYM': 26, 'PDT': 27, 'POS': 28, 'PRP': 29, 'PRP$': 30, 'RB': 31, 'RBR': 32, 'RBS': 33, 'RP': 34, 'SYM': 35, 'TO': 36, 'UH': 37, 'VB': 38, 'VBD': 39, 'VBG': 40, 'VBN': 41, 'VBP': 42, 'VBZ': 43, 'WDT': 44, 'WP': 45, 'WP$': 46, 'WRB': 47, 'RT': 48, 'HT': 49, 'USR': 50, 'URL': 51}\n"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'conll2003': DatasetDict({\n",
              "     train: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 14041\n",
              "     })\n",
              "     validation: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 3250\n",
              "     })\n",
              "     test: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 3453\n",
              "     })\n",
              " }),\n",
              " 'wnut_17': DatasetDict({\n",
              "     train: Dataset({\n",
              "         features: ['id', 'tokens', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 3394\n",
              "     })\n",
              "     validation: Dataset({\n",
              "         features: ['id', 'tokens', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 1009\n",
              "     })\n",
              "     test: Dataset({\n",
              "         features: ['id', 'tokens', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 1287\n",
              "     })\n",
              " }),\n",
              " 'twitter_pos_ritter': DatasetDict({\n",
              "     validation: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 118\n",
              "     })\n",
              "     test: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 118\n",
              "     })\n",
              "     train: Dataset({\n",
              "         features: ['id', 'tokens', 'pos_tags', 'input_ids', 'attention_mask', 'labels'],\n",
              "         num_rows: 551\n",
              "     })\n",
              " })}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multitask_model = multitask_model.to(device)\n",
        "optimizer = Adam(multitask_model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "enZFsEwj3hVr"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "# sampler = RoundRobinSampler(data)\n",
        "collate_fn = DataCollatorForTokenClassification(multitask_datasets.tokenizer)\n",
        "data_loaders = {\n",
        "    task: DataLoader(dataset_dict['train'], batch_size=batch_size, collate_fn=collate_fn, shuffle=True)\n",
        "    for task, dataset_dict in data.items()\n",
        "}\n",
        "round_robin_iterator = RoundRobinIterator(data_loaders)\n",
        "len(round_robin_iterator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGykUHre40Ts",
        "outputId": "d5e2ef0e-8887-40f8-c00e-64982ef31a1a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "564"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 2\n",
        "for epoch in tqdm(range(num_epochs)):\n",
        "  task_losses = {task: 0.0 for task in data_loaders}\n",
        "  batch_losses = {task: float(\"inf\") for task in data_loaders}\n",
        "  task_batches = {f\"{task}_batches\": 0 for task in data_loaders}\n",
        "  with tqdm(round_robin_iterator, desc=\"round_robin_iterator\") as pbar:\n",
        "    for task, batch in pbar:\n",
        "        batch = batch.to(device)\n",
        "        # print(task)\n",
        "        # print({k: v.shape for k,v in batch.items()})\n",
        "        # print(batch[\"input_ids\"][:, :10])\n",
        "        optimizer.zero_grad()\n",
        "        output = multitask_model(task=task, **batch)\n",
        "        task_batches[f\"{task}_batches\"] += 1\n",
        "        # print({k:v.shape for k,v in output.items()})\n",
        "        loss = output.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        batch_losses[task] = loss.item()\n",
        "        task_losses[task] += batch_losses[task]\n",
        "        # print(task_losses)\n",
        "        pbar.set_postfix(task=task, **batch_losses, **task_batches)\n",
        "        # break\n",
        "    print(task_losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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            "03d3cfaf1756482cba0f1eb028905874",
            "52f862adca4e4d2ab4f6b79b464937d3"
          ]
        },
        "id": "o_vsLYBLxTF9",
        "outputId": "d18150a4-6fff-41a0-80f2-7a559f66f71a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bea4f1fe730e4cc3b18af288f6e7d0d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "round_robin_iterator:   0%|          | 0/564 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13c3114105b44ee8b9e98d9470261aa7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'conll2003': 346.3214177787304, 'wnut_17': 36.585519790649414, 'twitter_pos_ritter': 59.7191059589386}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "round_robin_iterator:   0%|          | 0/564 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f58e7c98fc264dc594c1e85c165c2d83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'conll2003': 343.96735060214996, 'wnut_17': 34.77236123383045, 'twitter_pos_ritter': 58.08308410644531}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wXL7biWe48X8"
      },
      "execution_count": 18,
      "outputs": []
    }
  ]
}