introduction_en.ipynb

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   "source": [
    "# Mengzi-BERT base fin model (Chinese)\n",
    "Continue trained mengzi-bert-base with 20G financial news and research reports. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c65092",
   "metadata": {},
   "source": [
    "[Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/abs/2110.06696)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5404c7",
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   "source": [
    "## Usage\n"
   ]
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  {
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   "source": [
    "!pip install --upgrade paddlenlp"
   ]
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   "id": "d2c66056",
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import AutoModel\n",
    "\n",
    "model = AutoModel.from_pretrained(\"Langboat/mengzi-bert-base-fin\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39809dc",
   "metadata": {},
   "source": [
    "```\n",
    "@misc{zhang2021mengzi,\n",
    "title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese},\n",
    "author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},\n",
    "year={2021},\n",
    "eprint={2110.06696},\n",
    "archivePrefix={arXiv},\n",
    "primaryClass={cs.CL}\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25bda96",
   "metadata": {},
   "source": [
    "> The model introduction and model weights originate from https://huggingface.co/Langboat/mengzi-bert-base-fin and were converted to PaddlePaddle format for ease of use in PaddleNLP."
   ]
  }
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