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情绪分析

模型基本信息

对话情绪识别（Emotion Detection，简称 EmoTect）专注于识别智能对话场景中用户的情绪，针对智能对话场景中的用户文本，自动判断该文本的情绪类别并给出相应的置信度，情绪类型分为积极、消极、中性。该模型基于TextCNN（多卷积核 CNN 模型），能够更好地捕捉句子局部相关性。

参考：https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.2/modules/text/sentiment_analysis/emotion_detection_textcnn

样本结果示例

["今天天气真好", "湿纸巾是干垃圾", "别来吵我"]

[
    {
        "text": "今天天气真好",
        "emotion_label": 2,
        "emotion_key": "positive",
        "positive_probs": 0.9267,
        "negative_probs": 0.0019,
        "neutral_probs": 0.0714
    },
    {
        "text": "湿纸巾是干垃圾",
        "emotion_label": 1,
        "emotion_key": "neutral",
        "positive_probs": 0.0062,
        "negative_probs": 0.0042,
        "neutral_probs": 0.9896
    },
    {
        "text": "别来吵我",
        "emotion_label": 0,
        "emotion_key": "negative",
        "positive_probs": 0.0732,
        "negative_probs": 0.7791,
        "neutral_probs": 0.1477
    }
]

现在就来试试吧

先决条件

1、环境依赖

请访问 依赖项

2、emotion_detection_textcnn 依赖

  • paddlepaddle >= 1.8.0

  • paddlehub >= 1.8.0

3、下载模型

hub install emotion_detection_textcnn

服务模型

安装 Pinferencia

首先，让我们安装 Pinferencia。

pip install "pinferencia[streamlit]"

创建 app.py

让我们将我们的预测函数保存到一个文件 app.py 中并添加一些行来注册它。

app.py
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import paddlehub as hub

from pinferencia import Server, task

emotion_detection_textcnn = hub.Module(name="emotion_detection_textcnn")


def predict(text: list) -> list:
    return emotion_detection_textcnn.emotion_classify(texts=text)


service = Server()
service.register(
    model_name="emotion_detection_textcnn",
    model=predict,
    metadata={"task": task.TEXT_TO_TEXT},
)

运行服务，等待它加载模型并启动服务器：

$ uvicorn app:service --reload
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [xxxxx] using statreload
INFO:     Started server process [xxxxx]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

$ pinfer app:service --reload

Pinferencia: Frontend component streamlit is starting...
Pinferencia: Backend component uvicorn is starting...

测试服务

打开http://127.0.0.1:8501，模板 Raw Request 会自动选中。

png

请求

curl --location --request POST \
    'http://127.0.0.1:8000/v1/models/emotion_detection_textcnn/predict' \
    --header 'Content-Type: application/json' \
    --data-raw '{
        "data": ["今天天气真好", "湿纸巾是干垃圾", "别来吵我"]
    }'

响应

{
    "model_name": "emotion_detection_textcnn",
    "data": [
        {
            "text": "今天天气真好",
            "emotion_label": 2,
            "emotion_key": "positive",
            "positive_probs": 0.9267,
            "negative_probs": 0.0019,
            "neutral_probs": 0.0714
        },
        {
            "text": "湿纸巾是干垃圾",
            "emotion_label": 1,
            "emotion_key": "neutral",
            "positive_probs": 0.0062,
            "negative_probs": 0.0042,
            "neutral_probs": 0.9896
        },
        {
            "text": "别来吵我",
            "emotion_label": 0,
            "emotion_key": "negative",
            "positive_probs": 0.0732,
            "negative_probs": 0.7791,
            "neutral_probs": 0.1477
        }
    ]
}

创建test.py。

test.py
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import requests


response = requests.post(
    url="http://localhost:8000/v1/models/emotion_detection_textcnn/predict",
    json={"data": ["今天天气真好", "湿纸巾是干垃圾", "别来吵我"]}
)
print(response.json())
运行脚本并检查结果。

$ python test.py
{
    "model_name": "emotion_detection_textcnn",
    "data": [
        {
            "text": "今天天气真好",
            "emotion_label": 2,
            "emotion_key": "positive",
            "positive_probs": 0.9267,
            "negative_probs": 0.0019,
            "neutral_probs": 0.0714
        },
        {
            "text": "湿纸巾是干垃圾",
            "emotion_label": 1,
            "emotion_key": "neutral",
            "positive_probs": 0.0062,
            "negative_probs": 0.0042,
            "neutral_probs": 0.9896
        },
        {
            "text": "别来吵我",
            "emotion_label": 0,
            "emotion_key": "negative",
            "positive_probs": 0.0732,
            "negative_probs": 0.7791,
            "neutral_probs": 0.1477
        }
    ]
}

更酷的是，访问 http://127.0.0.1:8501，您将拥有一个交互式UI。

您可以在那里发送预测请求！