introduction_cn.ipynb

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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"
   ]
  },
  {
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   "id": "4fc17643",
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   "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"
   ]
  },
  {
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   "id": "f66fb11e",
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   "source": [
    "## Usage and Performance\n"
   ]
  },
  {
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   "id": "fd12128b",
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   "source": [
    "Pre-trained models can be used like this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0d04e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade paddlenlp"
   ]
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      "/root/miniconda3/envs/paddle/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "\u001b[32m[2022-11-21 02:38:07,127] [    INFO]\u001b[0m - Downloading model_config.json from https://bj.bcebos.com/paddlenlp/models/community/cross-encoder/stsb-TinyBERT-L-4/model_config.json\u001b[0m\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 432/432 [00:00<00:00, 425kB/s]\n",
      "\u001b[32m[2022-11-21 02:38:07,197] [    INFO]\u001b[0m - We are using <class 'paddlenlp.transformers.bert.modeling.BertModel'> to load 'cross-encoder/stsb-TinyBERT-L-4'.\u001b[0m\n",
      "\u001b[32m[2022-11-21 02:38:07,198] [    INFO]\u001b[0m - Downloading https://bj.bcebos.com/paddlenlp/models/community/cross-encoder/stsb-TinyBERT-L-4/model_state.pdparams and saved to /root/.paddlenlp/models/cross-encoder/stsb-TinyBERT-L-4\u001b[0m\n",
      "\u001b[32m[2022-11-21 02:38:07,198] [    INFO]\u001b[0m - Downloading model_state.pdparams from https://bj.bcebos.com/paddlenlp/models/community/cross-encoder/stsb-TinyBERT-L-4/model_state.pdparams\u001b[0m\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 54.8M/54.8M [00:00<00:00, 64.7MB/s]\n",
      "\u001b[32m[2022-11-21 02:38:08,199] [    INFO]\u001b[0m - Already cached /root/.paddlenlp/models/cross-encoder/stsb-TinyBERT-L-4/model_config.json\u001b[0m\n",
      "W1121 02:38:08.202270 64563 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.2, Runtime API Version: 10.2\n",
      "W1121 02:38:08.207437 64563 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.\n",
      "\u001b[32m[2022-11-21 02:38:09,661] [    INFO]\u001b[0m - Weights from pretrained model not used in BertModel: ['classifier.weight', 'classifier.bias']\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
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      "          0.68093145,  0.05896470]]))\n"
     ]
    }
   ],
   "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": "aeccdfe1",
   "metadata": {},
   "source": [
    "> 此模型介绍及权重来源于[https://huggingface.co/cross-encoder/stsb-TinyBERT-L-4](https://huggingface.co/cross-encoder/stsb-TinyBERT-L-4)，并转换为飞桨模型格式。"
   ]
  }
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