QA_models_comparison.ipynb

{
 "cells": [
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     "elapsed": 10,
     "status": "ok",
     "timestamp": 1716964108852,
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     "text": [
      "Wed May 29 06:28:29 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\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   45C    P8              10W /  70W |      0MiB / 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"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c-vhpACIAr2b"
   },
   "source": [
    "# Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 18185,
     "status": "ok",
     "timestamp": 1717158859190,
     "user": {
      "displayName": "Аккаунт Хитрый",
      "userId": "03630083244114494333"
     },
     "user_tz": -180
    },
    "id": "_dREbyNwWkgR"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForQuestionAnswering\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import pipeline\n",
    "from datasets import load_dataset\n",
    "import torch\n",
    "from google.colab import drive\n",
    "import string, re\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1717158859191,
     "user": {
      "displayName": "Аккаунт Хитрый",
      "userId": "03630083244114494333"
     },
     "user_tz": -180
    },
    "id": "q3EqDSHaBSF5",
    "outputId": "5a281c40-e2a2-43d6-ee8b-a940e9fdaad0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "DEVICE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подключение Google Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24084,
     "status": "ok",
     "timestamp": 1716964240415,
     "user": {
      "displayName": "Аккаунт Хитрый",
      "userId": "03630083244114494333"
     },
     "user_tz": -180
    },
    "id": "jGixp-OutSmz",
    "outputId": "5afba9ce-4f48-4187-cac0-0c7146c7cdee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Загрузка моделей для сравнения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19243,
     "status": "ok",
     "timestamp": 1716964259651,
     "user": {
      "displayName": "Аккаунт Хитрый",
      "userId": "03630083244114494333"
     },
     "user_tz": -180
    },
    "id": "6cduPaSZYooW",
    "outputId": "80da1381-426c-4e74-9bbd-6f322f4258ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.1 s, sys: 1.94 s, total: 3.04 s\n",
      "Wall time: 19.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# кортежи (название модели, реализована на TensorFlow)\n",
    "model_names = [\n",
    "    ('/content/drive/MyDrive/PBOA_ruBERT_QA_1_epoch',    False),\n",
    "    ('/content/drive/MyDrive/PBOA_ruBERT_QA_2_epoch',    False),\n",
    "    ('/content/drive/MyDrive/PBOA_ruELECTRA_QA_1_epoch', False),\n",
    "    ('/content/drive/MyDrive/PBOA_ruELECTRA_QA_2_epoch', False),\n",
    "    ('/content/drive/MyDrive/ruBERT_QA_1_epoch',         False),\n",
    "    ('/content/drive/MyDrive/ruBERT_QA_2_epoch',         False),\n",
    "    ('/content/drive/MyDrive/ruBERT_QA_3_epoch',         False),\n",
    "    ('/content/drive/MyDrive/ruT5_QA_1_epoch',           False),\n",
    "    ('/content/drive/MyDrive/ruT5_QA_2_epoch',           False),\n",
    "    ('/content/drive/MyDrive/ruELECTRA_QA_1_epoch',      False),\n",
    "    ('/content/drive/MyDrive/ruELECTRA_QA_2_epoch',      False),\n",
    "    ('/content/drive/MyDrive/ruELECTRA_QA_3_epoch',      False),\n",
    "    ('/content/drive/MyDrive/ruELECTRA_QA_4_epoch',      False),\n",
    "    ('/content/drive/MyDrive/ruELECTRA_QA_5_epoch',      False),\n",
    "    ('AndrewChar/model-QA-5-epoch-RU',                   True ),\n",
    "    ('KirrAno93/rubert-base-cased-finetuned-squad',      False),\n",
    "    ('IooHooI/my_awesome_qa_model',                      False),\n",
    "    ('Silxxor/qa_model',                                 False)\n",
    "]\n",
    "\n",
    "tokenizers = []\n",
    "models = []\n",
    "pipelines = []\n",
    "\n",
    "# Загрузка всех моделей из списка\n",
    "for model_name, from_tf in model_names:\n",
    "    print(model_name)\n",
    "    tokenizers.append(AutoTokenizer.from_pretrained(model_name))\n",
    "    models.append(AutoModelForQuestionAnswering.from_pretrained(model_name, from_tf=from_tf).to(DEVICE))\n",
    "    pipelines.append(pipeline('question-answering', model=models[-1], tokenizer=tokenizers[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Загрузка набора данных и метрик"
   ]
  },
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       "model_id": "eb1ea65c66014189a522346f34b3de97",
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      },
      "text/plain": [
       "Generating train split:   0%|          | 0/45328 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    {
     "data": {
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      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/5036 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
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    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/23936 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 45328\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 5036\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 23936\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sberquad = load_dataset('sberquad')\n",
    "sberquad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "wRzEISLUJxso"
   },
   "outputs": [],
   "source": [
    "# Нормализация текста: убрать лишние пробелы и знаки пунктуации\n",
    "def normalize_text(s):\n",
    "    def white_space_fix(text):\n",
    "        return ' '.join(text.split())\n",
    "\n",
    "    def remove_punc(text):\n",
    "        for exclude in string.punctuation:\n",
    "            text = text.replace(exclude, ' ')\n",
    "        return text\n",
    "\n",
    "    return white_space_fix(remove_punc(s.lower()))\n",
    "\n",
    "# Метрика EM для текста\n",
    "def compute_exact_match(prediction, truth):\n",
    "    return int(normalize_text(prediction) == normalize_text(truth))\n",
    "\n",
    "# Метрика F1 для текста\n",
    "def compute_f1(prediction, truth):\n",
    "    pred_tokens = normalize_text(prediction).split()\n",
    "    truth_tokens = normalize_text(truth).split()\n",
    "\n",
    "    if len(pred_tokens) == 0 or len(truth_tokens) == 0:\n",
    "        return int(pred_tokens == truth_tokens)\n",
    "\n",
    "    common_tokens = set(pred_tokens) & set(truth_tokens)\n",
    "\n",
    "    if len(common_tokens) == 0:\n",
    "        return 0\n",
    "\n",
    "    precision = len(common_tokens) / len(pred_tokens)\n",
    "    recall = len(common_tokens) / len(truth_tokens)\n",
    "\n",
    "    return 2 * (precision * recall) / (precision + recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1716964275114,
     "user": {
      "displayName": "Аккаунт Хитрый",
      "userId": "03630083244114494333"
     },
     "user_tz": -180
    },
    "id": "PllHajtxoAak",
    "outputId": "fb349ba0-4efb-44a1-d301-cf1e933fc3b2"
   },
   "outputs": [],
   "source": [
    "# Данные из SberQuAD для валидации\n",
    "validation_questions = sberquad['validation']['question']\n",
    "validation_contexts = sberquad['validation']['context']\n",
    "validation_answers = [ans['text'][0] for ans\n",
    "                      in sberquad['validation']['answers']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Сравнение моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1716964428628,
     "user": {
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      "userId": "03630083244114494333"
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     "user_tz": -180
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    "id": "ZBYzgiuOsQ7K",
    "outputId": "6c9c796f-e1bb-41a2-94e6-80475aceef7a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 30s, sys: 360 ms, total: 2min 30s\n",
      "Wall time: 2min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Предсказания моделей\n",
    "predicted_answers = [qa_pipeline(question=validation_questions,\n",
    "                                 context=validation_contexts)\n",
    "                     for qa_pipeline in pipelines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "0hUKN1msvIy0"
   },
   "outputs": [],
   "source": [
    "# Значения метрики EM у всех моделей\n",
    "em_results = [[compute_exact_match(pred['answer'], truth)\n",
    "               for pred, truth in zip(predicted, validation_answers)]\n",
    "              for predicted in predicted_answers]\n",
    "\n",
    "# Значения метрики F1 у всех моделей\n",
    "f1_results = [[compute_f1(pred['answer'], truth)\n",
    "               for pred, truth in zip(predicted, validation_answers)]\n",
    "              for predicted in predicted_answers]"
   ]
  },
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   "cell_type": "code",
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     "elapsed": 3,
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    },
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     "text": [
      "PBOA_ruBERT_QA_1_epoch              | 0.6340349483717236 | 0.8279802219062723\n",
      "PBOA_ruBERT_QA_2_epoch              | 0.6221207307386815 | 0.8192004605987167\n",
      "PBOA_ruELECTRA_QA_1_epoch           | 0.0017871326449563 | 0.06057979002981966\n",
      "PBOA_ruELECTRA_QA_2_epoch           | 0.0023828435266084 | 0.07666543694697722\n",
      "ruBERT_QA_1_epoch                   | 0.5992851469420175 | 0.7966346399312619\n",
      "ruBERT_QA_2_epoch                   | 0.5806195393169182 | 0.7876980827327227\n",
      "ruBERT_QA_3_epoch                   | 0.5773709293089754 | 0.7866356772708364\n",
      "ruT5_QA_1_epoch                     | 0.5716838760921367 | 0.7837050514319427\n",
      "ruT5_QA_2_epoch                     | 0.6084193804606831 | 0.8138671724431853\n",
      "ruELECTRA_QA_1_epoch                | 0.2960683081810961 | 0.5101738295607546\n",
      "ruELECTRA_QA_2_epoch                | 0.3234710087370929 | 0.541623642076742\n",
      "ruELECTRA_QA_3_epoch                | 0.3262509928514694 | 0.5470222396760291\n",
      "ruELECTRA_QA_4_epoch                | 0.3210881652104845 | 0.5450094416888817\n",
      "ruELECTRA_QA_5_epoch                | 0.3073868149324861 | 0.533159341653915\n",
      "model-QA-5-epoch-RU                 | 0.5415011914217633 | 0.7500355089564685\n",
      "rubert-base-cased-finetuned-squad   | 0.5901509134233519 | 0.7893769839501348\n",
      "my_awesome_qa_model                 | 0.5146942017474185 | 0.7202804507513528\n",
      "qa_model                            | 0.0107227958697379 | 0.08101478517141324\n"
     ]
    }
   ],
   "source": [
    "for (name, _), em, f1 in zip(model_names, em_results, f1_results):\n",
    "    only_name = name.split('/')[-1]\n",
    "    r_em = np.mean(em)\n",
    "    r_f1 = np.mean(f1)\n",
    "    print(f'{only_name:<35} | {r_em:<5} | {r_f1:<5}')"
   ]
  }
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