introduction_en.ipynb

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  {
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   "metadata": {},
   "source": [
    "# Model Card for DistilRoBERTa base\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d39a086",
   "metadata": {},
   "source": [
    "## Model Description\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2043d14",
   "metadata": {},
   "source": [
    "This model is a distilled version of the [RoBERTa-base model](https://huggingface.co/roberta-base). It follows the same training procedure as [DistilBERT](https://huggingface.co/distilbert-base-uncased).\n",
    "The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/master/examples/distillation).\n",
    "This model is case-sensitive: it makes a difference between english and English.\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base).\n",
    "On average DistilRoBERTa is twice as fast as Roberta-base.\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "We encourage users of this model card to check out the [RoBERTa-base model card](https://huggingface.co/roberta-base) to learn more about usage, limitations and potential biases.\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "- **Developed by:** Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf (Hugging Face)\n",
    "- **Model type:** Transformer-based language model\n",
    "- **Language(s) (NLP):** English\n",
    "- **License:** Apache 2.0\n",
    "- **Related Models:** [RoBERTa-base model card](https://huggingface.co/roberta-base)\n",
    "- **Resources for more information:**\n",
    "- [GitHub Repository](https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md)\n",
    "- [Associated Paper](https://arxiv.org/abs/1910.01108)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## How to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad9b1a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade paddlenlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94e4d093",
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "from paddlenlp.transformers import AutoModel\n",
    "\n",
    "model = AutoModel.from_pretrained(\"distilroberta-base\")\n",
    "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
    "print(model(input_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e258a20c",
   "metadata": {},
   "source": [
    "<a href=\"https://huggingface.co/exbert/?model=distilroberta-base\">\n",
    "<img width=\"300px\" src=\"https://cdn-media.huggingface.co/exbert/button.png\">\n",
    "</a>\n",
    "\n",
    "> The model introduction and model weights originate from [https://huggingface.co/distilroberta-base](https://huggingface.co/distilroberta-base) and were converted to PaddlePaddle format for ease of use in PaddleNLP.\n"
   ]
  }
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