transforms/language/doc_quality/doc_quality.ipynb

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    "##### **** These pip installs need to be adapted to use the appropriate release level. Alternatively, The venv running the jupyter lab could be pre-configured with a requirement file that includes the right release. Example for transform developers working from git clone:\n",
    "```\n",
    "make venv \n",
    "source venv/bin/activate \n",
    "pip install jupyterlab\n",
    "```"
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    "%%capture\n",
    "## This is here as a reference only\n",
    "# Users and application developers must use the right tag for the latest from pypi\n",
    "%pip install \"data-prep-toolkit-transforms[doc_quality]==1.0.0a4\""
   ]
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    "##### **** Configure the transform parameters. The set of dictionary keys holding DocQualityTransform configuration for values are as follows: \n",
    "* text_lang - specifies language used in the text content. By default, \"en\" is used.\n",
    "* doc_content_column - specifies column name that contains document text. By default, \"contents\" is used.\n",
    "* bad_word_filepath - specifies a path to bad word file: local folder (file or directory) that points to bad word file. You don't have to set this parameter if you don't need to set bad words.\n",
    "#####"
   ]
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   "cell_type": "markdown",
   "id": "ebf1f782-0e61-485c-8670-81066beb734c",
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   "source": [
    "##### ***** Import required classes and modules"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2a12abc-9460-4e45-8961-873b48a9ab19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dpk_doc_quality.transform_python import DocQuality"
   ]
  },
  {
   "cell_type": "markdown",
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    "##### ***** Setup runtime parameters and invoke the transform"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "95737436",
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   "outputs": [],
   "source": [
    "%%capture\n",
    "DocQuality(input_folder='test-data/input',\n",
    "            output_folder= 'output',\n",
    "            docq_text_lang = \"en\",\n",
    "            docq_doc_content_column =\"contents\").transform()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3df5adf-4717-4a03-864d-9151cd3f134b",
   "metadata": {},
   "source": [
    "##### **** The specified folder will include the transformed parquet files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7276fe84-6512-4605-ab65-747351e13a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "glob.glob(\"output/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "845a75cf-f4a9-467d-87fa-ccbac1c9beb8",
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