Snowpark_HuggingFace.ipynb

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    "# Snowpark Hugging Face\n",
    "Let's try out Hugging Face libraries\n",
    "\n",
    "The process has improved for Transformers and Sentence Transformers, there is native support for Snowpark. \n"
   ]
  },
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   "source": [
    "### 1. Setup Environment"
   ]
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   "source": [
    "# Snowflake connector\n",
    "from snowflake import connector\n",
    "#from snowflake.ml.utils import connection_params\n",
    "\n",
    "# Snowpark for Python\n",
    "from snowflake.snowpark.session import Session\n",
    "from snowflake.snowpark.types import Variant\n",
    "from snowflake.snowpark.version import VERSION\n",
    "\n",
    "# Misc\n",
    "import json\n",
    "import logging \n",
    "logger = logging.getLogger(\"snowflake.snowpark.session\")\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "953f6906",
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   "source": [
    "with open('creds.json') as f:\n",
    "    data = json.load(f)\n",
    "    USERNAME = data['user']\n",
    "    PASSWORD = data['password']\n",
    "    SF_ACCOUNT = data['account']\n",
    "    SF_WH = data['warehouse']\n",
    "\n",
    "CONNECTION_PARAMETERS = {\n",
    "   \"account\": SF_ACCOUNT,\n",
    "   \"user\": USERNAME,\n",
    "   \"password\": PASSWORD,\n",
    "}\n",
    "\n",
    "session = Session.builder.configs(CONNECTION_PARAMETERS).create()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65109d11",
   "metadata": {},
   "source": [
    "Let's grab the FinBERT text classification model, use it, save it to the registry, and then run prediction jobs. You can also add the parameter `token` to add your Hugging Face token for models that are restricted."
   ]
  },
  {
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   "execution_count": 3,
   "id": "da3e7472",
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     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/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": [
    "import transformers\n",
    "\n",
    "finbert_model = transformers.pipeline(\n",
    "    task=\"text-classification\",\n",
    "    model=\"ProsusAI/finbert\",\n",
    "    top_k=2,\n",
    ")"
   ]
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   "id": "c8aa01e6",
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    {
     "data": {
      "text/plain": [
       "[[{'label': 'neutral', 'score': 0.7302333116531372},\n",
       "  {'label': 'positive', 'score': 0.18303203582763672}]]"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "finbert_model.predict(\"I am bullish on $AAPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce71f7f2",
   "metadata": {},
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   "source": [
    "from snowflake.ml.registry import Registry\n",
    "\n",
    "reg = Registry(session=session, database_name=\"RAJIV\", schema_name=\"PUBLIC\")\n",
    "\n",
    "# Log the model\n",
    "mv = reg.log_model(\n",
    "    finbert_model,\n",
    "    model_name=\"finbert\",\n",
    "    version_name=\"v1\",\n",
    "    conda_dependencies=[\"transformers==4.32.1\",\"tokenizers==0.13.3\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce14a415",
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   "source": [
    "mv = reg.get_model(\"finbert\").version(\"v1\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "id": "c349a55e",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outputs</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[{\"label\": \"negative\", \"score\": 0.810623705387...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "                                             outputs\n",
       "0  [{\"label\": \"negative\", \"score\": 0.810623705387...\n",
       "1  [{\"label\": \"neutral\", \"score\": 0.9263970851898..."
      ]
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    "import pandas as pd \n",
    "# Use the model\n",
    "mv.run(pd.DataFrame(\n",
    "        [\n",
    "            [\"I have a problem with my Snowflake that needs to be resolved asap!!\", \"\"],\n",
    "            [\"I would like to have udon for today's dinner.\", \"\"],\n",
    "        ]\n",
    "    )\n",
    ")\n"
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