1.Traditional_Vector_Embeddings/1.Text/embedding_models/infersent.ipynb

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    "The notebook is made to run on kaggle\n",
    "- Needs glove-840b-300d and infersent "
   ]
  },
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   "source": [
    "Adapted From : https://www.kaggle.com/code/jacksoncrow/infersent-demo"
   ]
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   "source": [
    "## Environment Setup"
   ]
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   "source": [
    "!pip install --upgrade packaging"
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   "source": [
    "!pip install datasets --upgrade"
   ]
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   "source": [
    "%matplotlib inline\n",
    "\n",
    "from random import randint\n",
    "import numpy as np\n",
    "import torch\n",
    "import shutil\n",
    "import string\n",
    "import nltk.data\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)"
   ]
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   "source": [
    "# here we need to restructure working directory, so that script imports working properly\n",
    "shutil.copytree(\"/kaggle/input/infersent/\", \"/kaggle/working/infersent\")\n",
    "! mv /kaggle/working/infersent/* /kaggle/working/"
   ]
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     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
      "100  146M  100  146M    0     0  28.0M      0  0:00:05  0:00:05 --:--:-- 34.0M\r\n",
      "CPU times: user 83.5 ms, sys: 33.9 ms, total: 117 ms\n",
      "Wall time: 7.12 s\n"
     ]
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   ],
   "source": [
    "%%time\n",
    "\n",
    "! mkdir encoder\n",
    "! curl -Lo encoder/infersent1.pkl https://dl.fbaipublicfiles.com/infersent/infersent1.pkl"
   ]
  },
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   "metadata": {},
   "source": [
    "## Load Model"
   ]
  },
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    "model_version = 1\n",
    "MODEL_PATH = \"encoder/infersent%s.pkl\" % model_version\n",
    "W2V_PATH = '/kaggle/input/glove-840b-300d/glove.840B.300d.txt' # change this to your local\n",
    "VOCAB_SIZE = 1e5  # Load embeddings of VOCAB_SIZE most frequent words\n",
    "USE_CUDA = True  # Keep it on CPU if False, otherwise will put it on GPU"
   ]
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    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
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   "source": [
    "from models import InferSent\n",
    "params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,\n",
    "                'pool_type': 'max', 'dpout_model': 0.0, 'version': model_version}\n",
    "model = InferSent(params_model)\n",
    "model.load_state_dict(torch.load(MODEL_PATH))"
   ]
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   "outputs": [
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     "text": [
      "Vocab size : 100000.0\n",
      "CPU times: user 13.4 s, sys: 1.73 s, total: 15.1 s\n",
      "Wall time: 26.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = model.cuda() if USE_CUDA else model\n",
    "\n",
    "model.set_w2v_path(W2V_PATH)\n",
    "\n",
    "model.build_vocab_k_words(K=VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"recastai/coyo-75k-augmented-captions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encode Sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to specify input sentences as a list, where each punctuation sign is padded with a space from both sides"
   ]
  },
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   "outputs": [],
   "source": [
    "sentences = dataset['train']['short_caption']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "source": [
    "### converting to a list of strings\n",
    "sentences = [sentence[0] for sentence in sentences]\n",
    "sentences_ = [str(sentence) for sentence in sentences]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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   "source": [
    "#use this to add space in the list as appropriate : here the captions have no punctuations\n",
    "# def add_spaces_around_punctuation(sentences):\n",
    "#     def add_spaces(text):\n",
    "#         pattern = r\"(?<!\\s)([!\\\"#$%&'()*+,-./:;<=>?@\\[\\\\\\]^_`{|}~])\"\n",
    "#         new_text = re.sub(pattern, r\" \\1\", text)\n",
    "        \n",
    "#         pattern = r\"([!\\\"#$%&'()*+,-./:;<=>?@\\[\\\\\\]^_`{|}~])(?!\\s)\"\n",
    "#         new_text = re.sub(pattern, r\"\\1 \", new_text)\n",
    "        \n",
    "#         return new_text\n",
    "    \n",
    "#     return [add_spaces(sentence) for sentence in sentences]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying the model embedings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nb words kept : 858700/864110 (99.4%)\n",
      "Speed : 5545.3 sentences/s (gpu mode, bsize=128)\n"
     ]
    }
   ],
   "source": [
    "embeddings = model.encode(sentences_, bsize=128, tokenize=False, verbose=True)\n",
    "df = pd.DataFrame(embeddings)\n",
    "torch.cuda.empty_cache()"
   ]
  },
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   "outputs": [],
   "source": [
    "df['prompt'] = sentences \n",
    "df.to_csv(\"infersent_embeddings.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the output using kaggle api\n",
    "\n",
    "```kaggle datasets download -d anantjain1223/infersent-cyoyo-75k```\n",
    "\n",
    "or see the kaggle-dataset here\\\n",
    "https://www.kaggle.com/datasets/anantjain1223/infersent-cyoyo-75k/data"
   ]
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