DistilbertPerformance.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bhadreshpsavani/UnderstandingNLP/blob/master/DistilbertPerformance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvN3b08IzjTf",
        "outputId": "77d6034e-6ede-455a-de25-18e65a2c529f"
      },
      "source": [
        "!pip install -q transformers\r\n",
        "!pip install -q datasets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.9MB 10.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.2MB 52.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 58.0MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 184kB 7.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 245kB 13.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 15.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 20.7MB 46.5MB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zDDNubuSzkIY",
        "outputId": "a943b9e3-0042-40b4-a7d8-b45df63454e9"
      },
      "source": [
        "from transformers import squad_convert_examples_to_features\r\n",
        "from transformers.data.processors.squad import SquadV2Processor, SquadFeatures\r\n",
        "from tqdm.notebook import tqdm\r\n",
        "import torch\r\n",
        "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\r\n",
        "from datasets import load_metric, load_dataset\r\n",
        "device =  'cuda' if torch.cuda.is_available() else 'cpu'\r\n",
        "device"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cuda'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjxokEqfzmfZ"
      },
      "source": [
        "def get_result(predictions, references, dataset='squad_v2'):\r\n",
        "  squad_metric = load_metric(dataset)\r\n",
        "  score = squad_metric.compute(predictions=predictions, references=references)\r\n",
        "  return score\r\n",
        "\r\n",
        "def get_validation_data(dataset='squad_v2'):\r\n",
        "  datasets = load_dataset(dataset)\r\n",
        "  valid_dataset = datasets['validation']\r\n",
        "  return valid_dataset\r\n",
        "\r\n",
        "def get_infernece(valid_dataset, model, tokenizer, device):\r\n",
        "  predictions=[]\r\n",
        "  references=[]\r\n",
        "  model.to(device)\r\n",
        "  for example in tqdm(valid_dataset):\r\n",
        "    inputs = tokenizer(example['question'], example['context'], return_tensors=\"pt\", truncation=True)\r\n",
        "    inputs.to(device)\r\n",
        "    output = model(**inputs)\r\n",
        "    start_index = torch.argmax(output['start_logits'])\r\n",
        "    end_index = torch.argmax(output['end_logits'])\r\n",
        "    ans_ids = inputs['input_ids'][0][start_index :end_index+1]\r\n",
        "    answer = tokenizer.decode(ans_ids)\r\n",
        "    answer = tokenizer.clean_up_tokenization(answer).strip()\r\n",
        "    no_answer_probability = 1 if len(example['answers']['answer_start'])!=0 else 0\r\n",
        "    \"\"\"\r\n",
        "    predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\r\n",
        "    references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\r\n",
        "    \"\"\"\r\n",
        "    pred = {'prediction_text':answer, 'id': example['id'], 'no_answer_probability' : no_answer_probability}\r\n",
        "    ref = {'answers': example['answers'] , 'id': example['id']}\r\n",
        "    predictions.append(pred)\r\n",
        "    references.append(ref)\r\n",
        "  return predictions, references"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFtLyyOSzpkq"
      },
      "source": [
        "def print_results(model, tokenizer, device, dataset_name='squad_v2'):\r\n",
        "  validation_dataset = get_validation_data(dataset_name)\r\n",
        "  predictions, references = get_infernece(validation_dataset, model, tokenizer, device)\r\n",
        "  score = get_result(predictions, references, dataset_name)\r\n",
        "  print(score)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
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      "metadata": {
        "colab": {
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        "id": "FQ3OXUlPzrWw",
        "outputId": "8710c338-b674-4275-a20d-0c91e73b4241"
      },
      "source": [
        "distilbert_path='twmkn9/distilbert-base-uncased-squad2'\r\n",
        "distilbert_tokenizer = AutoTokenizer.from_pretrained(distilbert_path)\r\n",
        "distilbert_model = AutoModelForQuestionAnswering.from_pretrained(distilbert_path)\r\n",
        "distilbert_model.to(device)"
      ],
      "execution_count": 7,
      "outputs": [
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            "\n"
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForQuestionAnswering(\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (1): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (2): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (3): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (4): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "        (5): TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (qa_outputs): Linear(in_features=768, out_features=2, bias=True)\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
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        "id": "542auQK9zvN7",
        "outputId": "498f2e65-d5ee-4500-bcbc-0a814f366625"
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      "source": [
        "print_results(distilbert_model, distilbert_tokenizer, device)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
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        },
        {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading and preparing dataset squad_v2/squad_v2 (download: 44.34 MiB, generated: 122.57 MiB, post-processed: Unknown size, total: 166.91 MiB) to /root/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/9cac55034b086140f0649ecb5c604d09d7da2f2f5b73a90caa2e2bcc1f5cac09...\n"
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          "name": "stdout"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=800683.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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              "model_id": "33c2e29e9a404683b7e03dc75873addf",
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          },
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        {
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          "text": [
            "\rDataset squad_v2 downloaded and prepared to /root/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/9cac55034b086140f0649ecb5c604d09d7da2f2f5b73a90caa2e2bcc1f5cac09. Subsequent calls will reuse this data.\n"
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        {
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          "name": "stdout"
        },
        {
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          "data": {
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          },
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "{'exact': 31.272635391223783, 'f1': 35.63616173418905, 'total': 11873, 'HasAns_exact': 59.83468286099865, 'HasAns_f1': 68.57424903340527, 'HasAns_total': 5928, 'NoAns_exact': 2.7922624053826746, 'NoAns_f1': 2.7922624053826746, 'NoAns_total': 5945, 'best_exact': 50.07159100480081, 'best_exact_thresh': 0.0, 'best_f1': 50.07159100480081, 'best_f1_thresh': 0.0}\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "qkL9d38t0CmC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}