notebooks/6 Обучение новой модели трансформера на новом датасете.ipynb

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     "text": [
      "Collecting sentence_transformers\r\n",
      "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.6.0 in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (4.20.1)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (4.64.0)\r\n",
      "Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (1.11.0)\r\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (0.12.0)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (1.21.6)\r\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (1.0.2)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (1.7.3)\r\n",
      "Requirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (3.7)\r\n",
      "Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (0.1.97)\r\n",
      "Requirement already satisfied: huggingface-hub>=0.4.0 in /opt/conda/lib/python3.7/site-packages (from sentence_transformers) (0.10.1)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (4.1.1)\r\n",
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      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub>=0.4.0->sentence_transformers) (6.0)\r\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (0.12.1)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2021.11.10)\r\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from nltk->sentence_transformers) (8.0.4)\r\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from nltk->sentence_transformers) (1.0.1)\r\n",
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      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision->sentence_transformers) (9.1.1)\r\n",
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      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (1.26.12)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->huggingface-hub>=0.4.0->sentence_transformers) (2022.9.24)\r\n",
      "Building wheels for collected packages: sentence_transformers\r\n",
      "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=60a34f90273e219a989349fd356195168bd214e3fe599d3c7883d73427e44d86\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9\r\n",
      "Successfully built sentence_transformers\r\n",
      "Installing collected packages: sentence_transformers\r\n",
      "Successfully installed sentence_transformers-2.2.2\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "try:\n",
    "    from sentence_transformers import SentenceTransformer, InputExample, losses, util\n",
    "except:\n",
    "    !pip install sentence_transformers\n",
    "    from sentence_transformers import SentenceTransformer, InputExample, losses, util\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score, fbeta_score, hamming_loss, jaccard_score, average_precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7de755d5",
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   "outputs": [],
   "source": [
    "BASE = '../input/pretrained-transformers-for-fine-tuning/models'\n",
    "path_models = os.listdir(BASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edffcd6f",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "df = pd.read_csv('../input/new-preprocessing-nst/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9fb2d38",
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   "outputs": [],
   "source": [
    "train, test = train_test_split(df,test_size=0.2, \n",
    "                                random_state=128,\n",
    "                                stratify=df['is_dup'])"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    46612\n",
       "0    46612\n",
       "Name: is_dup, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test.is_dup.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2276f149",
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   "outputs": [],
   "source": [
    "mod_path = os.path.join(BASE, 'sentence-transformers_distiluse-base-multilingual-cased-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9079fb70",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 372895/372895 [00:01<00:00, 197780.06it/s]\n"
     ]
    }
   ],
   "source": [
    "ls = train.values.tolist()\n",
    "train_examples = []\n",
    "for x in tqdm(range(len(train))):\n",
    "    train_examples.append(InputExample(texts=[ls[x][0], ls[x][1]], label=float(ls[x][2])))"
   ]
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   "cell_type": "code",
   "execution_count": 8,
   "id": "76f0ec3a",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f0d5852",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [],
   "source": [
    "model = SentenceTransformer(mod_path, device = 'cuda')\n",
    "train_loss = losses.CosineSimilarityLoss(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
    {
     "data": {
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       "model_id": "efe5c33904e3494ab98044206d19e884",
       "version_major": 2,
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      },
      "text/plain": [
       "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
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   ],
   "source": [
    "model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1, warmup_steps=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59abeed06e8e4f5589bb79632b1e0cfb",
       "version_major": 2,
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      },
      "text/plain": [
       "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
      ]
     },
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    },
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     "data": {
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     "metadata": {},
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   ],
   "source": [
    "name_1 = [x[0] for x in test[['name_1']].values.tolist()[:10000]]\n",
    "name_2 = [x[0] for x in test[['name_2']].values.tolist()[:10000]]\n",
    "y_test = test.is_dup.values.tolist()[:10000]\n",
    "\n",
    "embeddings1 = model.encode(name_1, convert_to_tensor=True)\n",
    "embeddings2 = model.encode(name_2, convert_to_tensor=True)\n",
    "\n",
    "cosine_scores = util.cos_sim(embeddings1, embeddings2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d3c06af9",
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   "outputs": [],
   "source": [
    "y_pred = []\n",
    "for i in range(len(cosine_scores)):\n",
    "    y_pred.append(float(cosine_scores[i][i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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   "source": [
    "def evaluate(y_test, y_pred):\n",
    "    y_test = np.asarray(y_test, dtype='float32')\n",
    "    y_predf = [1 if x > 0.88 else 0 for x in y_pred]\n",
    "    y_predf = np.asarray(y_predf, dtype='float32')\n",
    "    score = f1_score(y_test, y_predf)\n",
    "    rec = recall_score(y_test, y_predf)\n",
    "    pre = precision_score(y_test, y_predf)\n",
    "    ac = accuracy_score(y_test, y_predf)\n",
    "    fb = fbeta_score(y_test, y_predf, beta=0.5)\n",
    "    hm = hamming_loss(y_test, y_predf)\n",
    "    js = jaccard_score(y_test, y_predf)\n",
    "    ap = average_precision_score(y_test, y_predf)\n",
    "    return ac, pre, rec, score, fb, hm, js, ap"
   ]
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       "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
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   "source": [
    "name_1 = ['Gazprom', 'Зенит Санкт-Петербург', 'Tesla Texas']\n",
    "name_2 = ['Rosneft', 'Спартак', 'Tesla']\n",
    "y_test = [0, 0, 1]\n",
    "\n",
    "embeddings1 = model.encode(name_1, convert_to_tensor=True)\n",
    "embeddings2 = model.encode(name_2, convert_to_tensor=True)\n",
    "\n",
    "cosine_scores = util.cos_sim(embeddings1, embeddings2)"
   ]
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    "y_pred = []\n",
    "for i in range(len(cosine_scores)):\n",
    "    y_pred.append(float(cosine_scores[i][i]))"
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   "source": [
    "model.save('1.0_db-multilingual-cased-v2')"
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  },
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    {
     "data": {
      "text/plain": [
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