week05_transfer/homework.ipynb

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      "source": [
        "### Homework 5: Question search engine\n",
        "\n",
        "Remeber week01 where you used GloVe embeddings to find related questions? That was.. cute, but far from state of the art. It's time to really solve this task using context-aware embeddings.\n",
        "\n",
        "__Warning:__ this task assumes you have seen `seminar.ipynb`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HYffoHiI8du5"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade transformers datasets accelerate deepspeed\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import transformers\n",
        "import datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfSHyQlT-fVF"
      },
      "source": [
        "### Load data and model"
      ]
    },
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          "text": [
            "WARNING:datasets.builder:Using custom data configuration SetFit--qqp-4b14bf13c8e13797\n"
          ]
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            "Downloading and preparing dataset json/SetFit--qqp to /root/.cache/huggingface/datasets/SetFit___json/SetFit--qqp-4b14bf13c8e13797/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...\n"
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              "0 tables [00:00, ? tables/s]"
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          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/SetFit___json/SetFit--qqp-4b14bf13c8e13797/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.\n"
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        {
          "name": "stdout",
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          "text": [
            "\n",
            "\n",
            "Sample[0]: {'text1': 'How is the life of a math student? Could you describe your own experiences?', 'text2': 'Which level of prepration is enough for the exam jlpt5?', 'label': 0, 'idx': 0, 'label_text': 'not duplicate'}\n",
            "Sample[3]: {'text1': 'What can one do after MBBS?', 'text2': 'What do i do after my MBBS ?', 'label': 1, 'idx': 3, 'label_text': 'duplicate'}\n"
          ]
        }
      ],
      "source": [
        "qqp = datasets.load_dataset('SetFit/qqp')\n",
        "print('\\n')\n",
        "print(\"Sample[0]:\", qqp['train'][0])\n",
        "print(\"Sample[3]:\", qqp['train'][3])"
      ]
    },
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      "outputs": [
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      "source": [
        "model_name = \"gchhablani/bert-base-cased-finetuned-qqp\"\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\n",
        "model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hM3ZujeZ-Z7E"
      },
      "source": [
        "### Tokenize the data"
      ]
    },
    {
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        "MAX_LENGTH = 128\n",
        "def preprocess_function(examples):\n",
        "    result = tokenizer(\n",
        "        examples['text1'], examples['text2'],\n",
        "        padding='max_length', max_length=MAX_LENGTH, truncation=True\n",
        "    )\n",
        "    result['label'] = examples['label']\n",
        "    return result\n",
        "\n",
        "qqp_preprocessed = qqp.map(preprocess_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ObMcFN59_Ll2",
        "outputId": "174e7cdd-de86-45ac-9a58-4cbb9c6ca45d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[101, 1731, 1110, 1103, 1297, 1104, 170, 12523, 2377, 136, 7426, 1128, 5594, 1240, 1319, 5758, 136,  ...\n"
          ]
        }
      ],
      "source": [
        "print(repr(qqp_preprocessed['train'][0]['input_ids'])[:100], \"...\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PyQ1ZbzGAUF2"
      },
      "source": [
        "### Task 1: evaluation (1 point)\n",
        "\n",
        "We randomly chose a model trained on QQP - but is it any good?\n",
        "\n",
        "One way to measure this is with validation accuracy - which is what you will implement next.\n",
        "\n",
        "Here's the interface to help you do that:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M5ueSoieAbBg"
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      "outputs": [],
      "source": [
        "val_set = qqp_preprocessed['validation']\n",
        "val_loader = torch.utils.data.DataLoader(\n",
        "    val_set, batch_size=1, shuffle=False, collate_fn=transformers.default_data_collator\n",
        ")"
      ]
    },
    {
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      "execution_count": null,
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        "id": "SsPwXXx-At-i",
        "outputId": "d149d172-650e-4807-f8ee-99e224bacf0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sample batch: {'labels': tensor([0]), 'idx': tensor([0]), 'input_ids': tensor([[  101,  2009,  1132,  2170,   118,  4038,  1177,  2712,   136,   102,\n",
            "          2009,  1132,  1117, 10224,  4724,  1177,  2712,   136,   102,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "         0, 0, 0, 0, 0, 0, 0, 0]])}\n",
            "\n",
            "Prediction (probs): [[9.998927e-01 1.072810e-04]]\n"
          ]
        }
      ],
      "source": [
        "for batch in val_loader:\n",
        "     break  # here be your training code\n",
        "print(\"Sample batch:\", batch)\n",
        "\n",
        "with torch.no_grad():\n",
        "  predicted = model(\n",
        "      input_ids=batch['input_ids'],\n",
        "      attention_mask=batch['attention_mask'],\n",
        "      token_type_ids=batch['token_type_ids']\n",
        "  )\n",
        "\n",
        "print('\\nPrediction (probs):', torch.softmax(predicted.logits, dim=1).data.numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RoxHzxn0DQqO"
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      "source": [
        "__Your task__ is to measure the validation accuracy of your model.\n",
        "Doing so naively may take several hours. Please make sure you use the following optimizations:\n",
        "\n",
        "- run the model on GPU with no_grad\n",
        "- using batch size larger than 1\n",
        "- use optimize data loader with num_workers > 1\n",
        "- (optional) use [mixed precision](https://pytorch.org/docs/stable/notes/amp_examples.html)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "9k5EK7-KA5F2"
      },
      "outputs": [],
      "source": [
        "\n",
        "<A whole lot of YOUR CODE HERE>\n",
        "...\n",
        "\n",
        "\n",
        "accuracy = <Validation accuracy, between 0 and 1>\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert 0.9 < accuracy < 0.91"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0R2z_-FZU3qy",
        "outputId": "bdccca39-2fc9-462c-b3a7-30b5fc9e86ad"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9083848627256987"
            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KONQ1E0J-y6B"
      },
      "source": [
        "### Task 2: train the model (4 points)\n",
        "\n",
        "For this task, you have two options:\n",
        "\n",
        "__Option A:__ fine-tune your own model. You are free to choose any model __except for the original BERT.__ We recommend [DeBERTa-v3](https://huggingface.co/microsoft/deberta-v3-base). Better yet, choose the best model based on public benchmarks (e.g. [GLUE](https://gluebenchmark.com/)).\n",
        "\n",
        "You can write the training code manually or use transformers.Trainer (see [this example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification)). Please make sure that your model's accuracy is at least __comparable__ with the above example for BERT.\n",
        "\n",
        "\n",
        "__Option B:__ compare at least 3 pre-finetuned models (in addition to the above BERT model). For each model, report (1) its accuracy, (2) its speed, measured in samples per second in your hardware setup and (3) its size in megabytes. Please take care to compare models in equal setting, e.g. same CPU / GPU. Compile your results into a table and write a short (~half-page on top of a table) report, summarizing your findings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T0ZkZTkl_yMU"
      },
      "outputs": [],
      "source": [
        "<A whole lot of your code goes here>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XpC8Uf82Lqxd"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQD0IV44LrSs"
      },
      "source": [
        "### Task 3: try the full pipeline (1 point)\n",
        "\n",
        "Finally, it is time to use your model to find duplicate questions.\n",
        "Please implement a function that takes a question and finds top-5 potential duplicates in the training set. For now, it is fine if your function is slow, as long as it yields correct results.\n",
        "\n",
        "Showcase how your function works with at least 5 examples."
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zLSjmsKaUyQb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "__Bonus:__ for bonus points, try to find a way to run the function faster than just passing over all questions in a loop. For isntance, you can form a short-list of potential candidates using a cheaper method, and then run your tranformer on that short list. If you opted for this solution, please keep both the original implementation and the optimized one - and explain briefly what is the difference there."
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