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# ERNIE 3.0 模型Python部署示例
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English | [简体中文](README_CN.md)
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在部署前，需确认以下两个步骤
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# Example of ERNIE 3.0 Models Python Deployment
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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Before deployment, two steps require confirmation.
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本目录下提供`seq_cls_infer.py`快速完成在CPU/GPU的文本分类任务的部署示例。
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- 1. Environment of software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl package should be installed. Please refer to [FastDeploy Python Installation](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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## 依赖安装
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This directory provides deployment examples that seq_cls_inferve.py fast finish text classification tasks on CPU/GPU.
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本项目提供的Python版本的预测器Predictor基于PaddleNLP提供的AutoTokenizer进行分词，并利用fast_tokenizer加速分词, 执行以下命令进行安装。
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## Dependency Installation
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The Python Predictor in this project uses AutoTokenizer provided by PaddleNLP to conduct word segmentation and fast_tokenizer to speed up word segmentation. Run the following command to install it.
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```bash
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pip install -r requirements.txt
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```
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## 文本分类任务
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## Text Classification Tasks
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### 快速开始
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### A Quick Start
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以下示例展示如何基于FastDeploy库完成ERNIE 3.0 Medium模型在CLUE Benchmark 的[AFQMC数据集](https://bj.bcebos.com/paddlenlp/datasets/afqmc_public.zip)上进行文本分类任务的Python预测部署。
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The following example shows how to employ FastDeploy library to complete Python predictive deployment of ERNIE 3.0 Medium model on [AFQMC Dataset](https://bj.bcebos.com/paddlenlp/datasets/afqmc_public.zip)of CLUE Benchmark for text classification tasks.
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```bash
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# 下载部署示例代码
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# Download the deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/text/ernie-3.0/python
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# 下载AFQMC数据集的微调后的ERNIE 3.0模型
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# Download fine-tuned ERNIE 3.0 models for AFQMC dataset
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wget https://bj.bcebos.com/fastdeploy/models/ernie-3.0/ernie-3.0-medium-zh-afqmc.tgz
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tar xvfz ernie-3.0-medium-zh-afqmc.tgz
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# CPU 推理
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# CPU Inference
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python seq_cls_infer.py --device cpu --model_dir ernie-3.0-medium-zh-afqmc
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# GPU 推理
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# GPU Inference
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python seq_cls_infer.py --device gpu --model_dir ernie-3.0-medium-zh-afqmc
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```
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The result returned after running is as follows:
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运行完成后返回的结果如下：
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```bash
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[INFO] fastdeploy/runtime.cc(469)::Init Runtime initialized with Backend::ORT in Device::CPU.
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@@ -48,24 +50,24 @@ Batch id:0, example id:0, sentence1:花呗收款额度限制, sentence2:收钱
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Batch id:1, example id:0, sentence1:花呗支持高铁票支付吗, sentence2:为什么友付宝不支持花呗付款, label:0, similarity:0.9979
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```
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### 参数说明
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### Parameter Description
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`seq_cls_infer.py` 除了以上示例的命令行参数，还支持更多命令行参数的设置。以下为各命令行参数的说明。
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`seq_cls_infer.py`supports more command-line arguments in addition to the preceding example. The following is a description of every command-line argument
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| 参数 |参数说明 |
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| Parameter | Parameter Description |
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|----------|--------------|
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|--model_dir | 指定部署模型的目录， |
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|--batch_size |最大可测的 batch size，默认为 1|
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|--max_length |最大序列长度，默认为 128|
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|--device | 运行的设备，可选范围: ['cpu', 'gpu']，默认为'cpu' |
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|--backend | 支持的推理后端，可选范围: ['onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']，默认为'onnx_runtime' |
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|--use_fp16 | 是否使用FP16模式进行推理。使用tensorrt和paddle_tensorrt后端时可开启，默认为False |
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|--use_fast| 是否使用FastTokenizer加速分词阶段。默认为True|
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|--model_dir | Specify the directory where the model is to be deployed |
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|--batch_size |Maximum measurable batch size, default 1|
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|--max_length |Maximum sequence length, default 128|
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|--device | Running devices, optional range: ['cpu', 'gpu'], default 'cpu' |
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|--backend | Supported inference backend, optional range: ['onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']，default 'onnx_runtime' |
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|--use_fp16 | Whether to use FP16 mode for inference.Enabled when using tensorrt and paddle tensorrt backend, and default is False |
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|--use_fast| Whether to use FastTokenizer to speed up the tokenization, and default is True|
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## 相关文档
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## Related Documents
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[ERNIE 3.0模型详细介绍](https://github.com/PaddlePaddle/PaddleNLP/tree/release/2.4/model_zoo/ernie-3.0)
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[ERNIE 3.0 Model detailed introduction](https://github.com/PaddlePaddle/PaddleNLP/tree/release/2.4/model_zoo/ernie-3.0)
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[ERNIE 3.0模型导出方法](https://github.com/PaddlePaddle/PaddleNLP/tree/release/2.4/model_zoo/ernie-3.0)
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[ERNIE 3.0 Model Export Method](https://github.com/PaddlePaddle/PaddleNLP/tree/release/2.4/model_zoo/ernie-3.0)
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[ERNIE 3.0模型C++部署方法](../cpp/README.md)
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[ERNIE 3.0 Model C++ Deployment Method](../cpp/README.md)
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73
examples/text/ernie-3.0/python/README_CN.md
Normal file
73
examples/text/ernie-3.0/python/README_CN.md
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@@ -0,0 +1,73 @@
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[English](README.md) | 简体中文
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# ERNIE 3.0 模型Python部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`seq_cls_infer.py`快速完成在CPU/GPU的文本分类任务的部署示例。
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## 依赖安装
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本项目提供的Python版本的预测器Predictor基于PaddleNLP提供的AutoTokenizer进行分词，并利用fast_tokenizer加速分词, 执行以下命令进行安装。
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```bash
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pip install -r requirements.txt
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```
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## 文本分类任务
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### 快速开始
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以下示例展示如何基于FastDeploy库完成ERNIE 3.0 Medium模型在CLUE Benchmark 的[AFQMC数据集](https://bj.bcebos.com/paddlenlp/datasets/afqmc_public.zip)上进行文本分类任务的Python预测部署。
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/text/ernie-3.0/python
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# 下载AFQMC数据集的微调后的ERNIE 3.0模型
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wget https://bj.bcebos.com/fastdeploy/models/ernie-3.0/ernie-3.0-medium-zh-afqmc.tgz
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tar xvfz ernie-3.0-medium-zh-afqmc.tgz
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# CPU 推理
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python seq_cls_infer.py --device cpu --model_dir ernie-3.0-medium-zh-afqmc
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# GPU 推理
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python seq_cls_infer.py --device gpu --model_dir ernie-3.0-medium-zh-afqmc
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```
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运行完成后返回的结果如下：
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```bash
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[INFO] fastdeploy/runtime.cc(469)::Init Runtime initialized with Backend::ORT in Device::CPU.
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Batch id:0, example id:0, sentence1:花呗收款额度限制, sentence2:收钱码，对花呗支付的金额有限制吗, label:1, similarity:0.5819
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Batch id:1, example id:0, sentence1:花呗支持高铁票支付吗, sentence2:为什么友付宝不支持花呗付款, label:0, similarity:0.9979
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```
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### 参数说明
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`seq_cls_infer.py` 除了以上示例的命令行参数，还支持更多命令行参数的设置。以下为各命令行参数的说明。
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| 参数 |参数说明 |
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|----------|--------------|
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|--model_dir | 指定部署模型的目录， |
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|--batch_size |最大可测的 batch size，默认为 1|
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|--max_length |最大序列长度，默认为 128|
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|--device | 运行的设备，可选范围: ['cpu', 'gpu']，默认为'cpu' |
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|--backend | 支持的推理后端，可选范围: ['onnx_runtime', 'paddle', 'openvino', 'tensorrt', 'paddle_tensorrt']，默认为'onnx_runtime' |
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|--use_fp16 | 是否使用FP16模式进行推理。使用tensorrt和paddle_tensorrt后端时可开启，默认为False |
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|--use_fast| 是否使用FastTokenizer加速分词阶段。默认为True|
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## 相关文档
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[ERNIE 3.0模型详细介绍](https://github.com/PaddlePaddle/PaddleNLP/tree/release/2.4/model_zoo/ernie-3.0)
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[ERNIE 3.0模型导出方法](https://github.com/PaddlePaddle/PaddleNLP/tree/release/2.4/model_zoo/ernie-3.0)
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[ERNIE 3.0模型C++部署方法](../cpp/README.md)
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