create_mbedding-vecotor.ipynb

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   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
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     "name": "stderr",
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     "text": [
      "/home/swastikm/work/senetence-transformer-in-action-main/semantic_search_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "from datasets import load_from_disk\n",
    "import os"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = os.getcwd()\n",
    "dataset_folder = \"conala\"\n",
    "path_to_dataset = os.path.join(base_path,dataset_folder)\n",
    "dataset = load_from_disk(path_to_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create vector embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/swastikm/work/senetence-transformer-in-action-main/semantic_search_env/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_embeddings(examples):\n",
    "    vectors = {}\n",
    "    model_input = examples['intent']\n",
    "    out =  model.encode(model_input)\n",
    "    vectors['embedding'] = out\n",
    "    return vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████| 593891/593891 [20:14<00:00, 488.98 examples/s]\n"
     ]
    }
   ],
   "source": [
    "vectorized_dataset = dataset.map(get_embeddings,batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'id', 'embedding'],\n",
       "    num_rows: 593891\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorized_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving the dataset (3/3 shards): 100%|██████████| 593891/593891 [00:08<00:00, 69697.72 examples/s] \n"
     ]
    }
   ],
   "source": [
    "vectorized_dataset.save_to_disk('vectorized_dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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