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    "# 🤗 + 📚 dbmdz German BERT models\n",
    "\n",
    "In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State\n",
    "Library open sources another German BERT models 🎉\n",
    "\n",
    "# German BERT\n",
    "\n",
    "## Stats\n",
    "\n",
    "In addition to the recently released [German BERT](https://deepset.ai/german-bert)\n",
    "model by [deepset](https://deepset.ai/) we provide another German-language model.\n",
    "\n",
    "The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus,\n",
    "Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with\n",
    "a size of 16GB and 2,350,234,427 tokens.\n",
    "\n",
    "For sentence splitting, we use [spacy](https://spacy.io/). Our preprocessing steps\n",
    "(sentence piece model for vocab generation) follow those used for training\n",
    "[SciBERT](https://github.com/allenai/scibert). The model is trained with an initial\n",
    "sequence length of 512 subwords and was performed for 1.5M steps."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589fadf4",
   "metadata": {},
   "source": [
    "## How to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646e12d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade paddlenlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5935d3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import AutoModel\n",
    "\n",
    "model = AutoModel.from_pretrained(\"dbmdz/bert-base-german-cased\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05add24",
   "metadata": {},
   "source": [
    "# Reference\n",
    "\n",
    "> 此模型介绍及权重来源于[https://huggingface.co/dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased)，并转换为飞桨模型格式。\n"
   ]
  }
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