introduction_en.ipynb

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    "## SPECTER\n",
    "\n",
    "SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning.\n",
    "\n",
    "Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf)\n",
    "\n",
    "Original Repo: [Github](https://github.com/allenai/specter)\n",
    "\n",
    "Evaluation Benchmark: [SciDocs](https://github.com/allenai/scidocs)\n",
    "\n",
    "Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*"
   ]
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   "source": [
    "## How to Use"
   ]
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   "source": [
    "!pip install --upgrade paddlenlp"
   ]
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   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import BertModel\n",
    "\n",
    "model = BertModel.from_pretrained(\"allenai/specter\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
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   "cell_type": "markdown",
   "id": "cd668864",
   "metadata": {},
   "source": [
    "## Reference\n",
    "\n",
    "> The model introduction and model weights originate from [https://huggingface.co/allenai/specter](https://huggingface.co/allenai/specter) and were converted to PaddlePaddle format for ease of use in PaddleNLP."
   ]
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