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Emotion Detection Textcnn

Model basic information

Emotion Detection (EmoTect for short) focuses on identifying the emotions of users in intelligent dialogue scenes. For user texts in intelligent dialogue scenes, it automatically determines the emotional category of the text and gives the corresponding confidence. The emotional type is divided into positive , Negative, Neutral. The model is based on TextCNN (Multiple Convolutional Kernel CNN model), which can better capture sentence local correlation.

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

Sample result example

["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
    }
]

Let's try it out now

Prerequisite

1、environment dependent

Please visit dependencies

2、emotion_detection_textcnn dependent

  • paddlepaddle >= 1.8.0

  • paddlehub >= 1.8.0

3、Download the model

hub install emotion_detection_textcnn

Serve the Model

Install Pinferencia

First, let's install Pinferencia.

pip install "pinferencia[streamlit]"

Create app.py

Let's save our predict function into a file app.py and add some lines to register it.

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},
)

Run the service, and wait for it to load the model and start the server:

$ 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...

Test the service

Open http://127.0.0.1:8501, and the template Raw Request will be selected automatically.

png

Request

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"]
    }'

Response

{
    "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
        }
    ]
}

Create the 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())
Run the script and check the result.

$ 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
        }
    ]
}

Even cooler, go to http://127.0.0.1:8000, and you will have a full documentation of your APIs.

You can also send predict requests just there!