smartloop 1.1.0

Natural language processing framework for text processing

Smartloop NLP framework

Natural language processing framework

Train a bot

Use the sample.json file in the \data folder, you will pass the name of bot as an argument in the next step.

Below is as training JSON sample containing the pattern and name of the intent that wil be resolved for a user input.

{
    "examples": {
        "intents": [
            {
                "text": "about",
                "intent": "about"
            },
            {
                "text": "company",
                "intent": "about"
            },
            {
                "text": "what is smartloop",
                "intent": "about"
            },
            {
                "text": "start",
                "intent": "start"
            },
            {
                "text": "menu",
                "intent": "start"
            },
            {
                "text": "hi",
                "intent": "start"
            }
        ]
    },
    "lang": "en"
}

From the command line type the following to train the bot:

python main.py train -i sample

Testing the bot

To test the type the following command:

python main.py parse -i sample -t "I need a chabot"

This should return the intent name followed by the confidence level

{
    "topIntent": {
        "intent": "i-need-chatbot",
        "confidence": 0.9999436140060425
    },
    "intents": [
        {
            "intent": "i-need-chatbot",
            "confidence": 0.9999436140060425
        },
        {
            "intent": "chatter-good-afternoon",
            "confidence": 4.835660001845099e-05
        },
        {
            "intent": "bizbot-no-way",
            "confidence": 3.6056665067008e-06
        },
        {
            "intent": "about-chatbot",
            "confidence": 1.9573460576793877e-06
        },
        {
            "intent": "contact",
            "confidence": 1.095663265004987e-06
        }
    ]
}

Tunning your model (Advanced)

It is possible to override the default training parameters to create a model that fits your need, override config.yaml to tune your model:

# number of epochs
epochs: 100

# Use tensorboard callback
logs: True

# classifier parameters
embedded_intent_classifier:
    # base neurons, this will be increased based on the intent size
    neurons: 16
    # length of input len("hello how are you") = 4
    input_length: 100
    learning_rate: 1e-2
    flatten: False
    hidden_layers: 2
    # drop rate to avoid overfitting
    drop_rate: 0.2
    # early stop training in case of not improving
    early_stopping: True

This can vary based on model size, can be tuned using the grid search capabablites to find the optimal settings.

Here is a list of basic parameters and their meaning:

• epochs - This is the number of iterations where 1 epoch = 1 complete neural net cycle
• learning_rate - How fast or slow, the model is learning through iterations
• drop_rate - Adjust to prevent overfitting of the data to fine tune your model

Configuration

Install stop words dictionary using following command

python -m nltk.downloader stopwords   

Debugging

Set logs:True in config.yaml to enable debugging using tensorboard. Once you have trained the bot. Type the following command to start tensorboard:

tensorboard serve --logdir logs/nlp_data/<bot_id>/<model_id>

Supported Language

• English (en)
• Spanish (es)
• German (de)

Requirements

• Tensorflow (>=2.12.0)

License

See LICENSE for full details.

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