introduction_cn.ipynb

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   "source": [
    "# Cross-Encoder for Natural Language Inference\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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   "source": [
    "## Training Data\n",
    "The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.\n"
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   "source": [
    "## Performance\n",
    "For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).\n"
   ]
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   "source": [
    "## Usage\n"
   ]
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   "source": [
    "Pre-trained models can be used like this:\n"
   ]
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  {
   "cell_type": "code",
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   "id": "d2c99a51",
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   "source": [
    "!pip install --upgrade paddlenlp"
   ]
  },
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   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import AutoModel\n",
    "\n",
    "model = AutoModel.from_pretrained(\"cross-encoder/nli-MiniLM2-L6-H768\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
  {
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   "source": [
    "> 此模型介绍及权重来源于[https://huggingface.co/cross-encoder/nli-MiniLM2-L6-H768](https://huggingface.co/cross-encoder/nli-MiniLM2-L6-H768)，并转换为飞桨模型格式。\n"
   ]
  }
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