{ "cells": [ { "cell_type": "markdown", "id": "a4ae7e65", "metadata": {}, "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" ] }, { "cell_type": "markdown", "id": "f2d88a35", "metadata": {}, "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" ] }, { "cell_type": "markdown", "id": "d982bc91", "metadata": {}, "source": [ "## Performance\n", "For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).\n" ] }, { "cell_type": "markdown", "id": "1f3796c9", "metadata": {}, "source": [ "## Usage\n" ] }, { "cell_type": "markdown", "id": "14206f74", "metadata": {}, "source": [ "Pre-trained models can be used like this:\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7e5f7a2f", "metadata": {}, "outputs": [], "source": [ "!pip install --upgrade paddlenlp" ] }, { "cell_type": "code", "execution_count": null, "id": "05497be6", "metadata": {}, "outputs": [], "source": [ "import paddle\n", "from paddlenlp.transformers import AutoModel\n", "\n", "model = AutoModel.from_pretrained(\"cross-encoder/nli-distilroberta-base\")\n", "input_ids = paddle.randint(100, 200, shape=[1, 20])\n", "print(model(input_ids))" ] }, { "cell_type": "markdown", "id": "ea7e434c", "metadata": {}, "source": [ "> The model introduction and model weights originate from [https://huggingface.co/cross-encoder/nli-distilroberta-base](https://huggingface.co/cross-encoder/nli-distilroberta-base) and were converted to PaddlePaddle format for ease of use in PaddleNLP.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }