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   "source": [
    "# Cross-Encoder for Quora Duplicate Questions Detection\n",
    "This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3be9ab9",
   "metadata": {},
   "source": [
    "## Training Data\n",
    "This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2d2712",
   "metadata": {},
   "source": [
    "## Usage and Performance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0127bf3d",
   "metadata": {},
   "source": [
    "Pre-trained models can be used like this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6968e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade paddlenlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39e99053",
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import AutoModel\n",
    "\n",
    "model = AutoModel.from_pretrained(\"cross-encoder/stsb-TinyBERT-L-4\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35446f31",
   "metadata": {},
   "source": [
    "You can use this model also without sentence_transformers and by just using ``AutoModel`` class\n",
    "> The model introduction and model weights originate from [https://huggingface.co/cross-encoder/stsb-TinyBERT-L-4](https://huggingface.co/cross-encoder/stsb-TinyBERT-L-4) and were converted to PaddlePaddle format for ease of use in PaddleNLP.\n"
   ]
  }
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