{
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  {
   "cell_type": "markdown",
   "id": "91c5f94f",
   "metadata": {},
   "source": [
    "# ClinicalBERT - Bio + Clinical BERT Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec471b16",
   "metadata": {},
   "source": [
    "The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab166b8",
   "metadata": {},
   "source": [
    "This model card describes the Bio+Clinical BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on all MIMIC notes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f6ed08",
   "metadata": {},
   "source": [
    "## How to use the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62913fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade paddlenlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b055241",
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import BertForMaskedLM\n",
    "\n",
    "model = BertForMaskedLM.from_pretrained(\"emilyalsentzer/Bio_ClinicalBERT\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0716a06f",
   "metadata": {},
   "source": [
    "> The model introduction and model weights originate from [https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) and were converted to PaddlePaddle format for ease of use in PaddleNLP.\n"
   ]
  }
 ],
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