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Add unified sentiment analysis based on UIE. #3694
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本功能在预测时需要传入测试集文件路径,可将测试集文件命名为`test.txt`, 然后放入 `./data` 目录下。需要注意的是,测试集文件每行均为一个待预测的语句,如下所示。 | ||
#### 2.2.1 属性/观点分析 |
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这里说的属性是指情感维度吗? 看看要不要整体统一一下
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这里的情感维度是属性,一般叫属性说法的会比较多一点
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如果你希望自己尝试进行属性级情感分类模型训练,可使用4.1节中提供的 `cls_data` Demo 数据,或自己业务的标注数据重新训练模型,本项目已将属性级情感分类模型的相关训练和测试代码放入 `classification` 目录下,请到该目录下执行模型训练即可,更多的实现细节和使用方式,请参考[这里](classification/README.md)。 | ||
#### 2.2.3 属性+情感分析 |
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属性 + 情感极性分析?
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已改为:属性 + 情感极性分析
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## 6. 引用 | ||
- 👉 [通用情感分析抽取](./unified_sentiment_extraction/README) |
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这里建议 更多的进行一些引导,因为上面的方案介绍更像是介绍统一信息抽取的方案
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后续整体ready后,会继续完善这块
<a name="2.1"></a> | ||
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### 2.1 运行环境 | ||
- python >= 3.6 |
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python的版本建议升级3.7+ 后续Paddle的框架和套件不再支持python 3.6
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已改为:python >= 3.7
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**安装PaddlePaddle**: | ||
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环境中paddlepaddle-gpu或paddlepaddle版本应大于或等于2.3, 请参见[飞桨快速安装](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)根据自己需求选择合适的PaddlePaddle下载命令。 |
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这里也可以建议增加conda的安装方式
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已修改为:
环境中paddlepaddle-gpu或paddlepaddle版本应大于或等于2.3, 具体可以参见飞桨快速安装根据自己需求选择合适的PaddlePaddle下载命令。如下命令可以安装linux系统,CUDA版本为10.2环境下的paddlepaddle,具体版本号为支持GPU的2.3.2版本。
conda install paddlepaddle-gpu==2.3.2 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
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# visualization for aspect | ||
save_path = os.path.join(args.save_dir, "aspect_wc.png") | ||
vs.plot_aspect_with_frequency(sr.aspect_frequency, save_path, image_type="wordcloud") |
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这里的sr 定义的地方没有放出code
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已添加:
define SentimentResult to process the result of sentiment result.
sr = SentimentResult(args.file_path, sentiment_name=args.sentiment_name)
``` | ||
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<div align="center"> | ||
<img src="https://user-images.githubusercontent.com/35913314/200213998-e646c422-7ab5-48ae-9e28-d6068cdf7b8f.png"/> |
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后续可以看看和pipelines的联动
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已支持pipeline
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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霖捷本周会把UIE放到 transformers/ernie/modeling.py里面,后续可以直接调用
同时可能要看看支持UIE-M模型,这个模型结构稍有区别
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已修改
from utils import load_txt, write_json_file | ||
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def parse_args(): |
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这里的yapf disable一下
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这些代码后续会删掉,统一用taskflow
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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这个有个疑问,后续也是提供uie-base的微调能力? 还是我们提供定制化的base medium nano mini相关版本
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这里计划是提供uie-base的微调能力
Codecov Report
@@ Coverage Diff @@
## develop #3694 +/- ##
===========================================
- Coverage 33.11% 32.98% -0.13%
===========================================
Files 400 400
Lines 56139 56396 +257
===========================================
+ Hits 18588 18604 +16
- Misses 37551 37792 +241
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LGTM
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Description
Add unified sentiment analysis based on UIE.