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[Model] Add text classification task for ernie-3.0 (#430)
* move text_cls to ernie-3.0 * Add main page of ernie-3.0 * rename infer -> seq_cls_infer * Fix the links * Add ernie-3.0 python, cpp readme * Fix some cpp readme * Add fastdeploy::FDERROR * Add python readme for ernie-3.0 * update README.md * Add empty line * update readme * Fix readme * remove the - from ernie 3.0 * ernie-3.0 -> ernie 3.0 * Use AutoTokenizer to tokenize * Ernie -> ERNIE
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examples/text/ernie-3.0/python/README.md
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examples/text/ernie-3.0/python/README.md
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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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