Quickstart#

Installation#

AutoIntent can be installed using the package manager pip:

git clone https://github.com/voorhs/AutoIntent.git
cd AutoIntent
pip install .

The library is compatible with Python 3.10+.

Data Format#

To work with AutoIntent, you need to format your training data in a specific way. You need to provide a training split containing samples with utterances and labels, as shown below:

{
    "train": [
        {
            "utterance": "Hello!",
            "label": 0
        }
    ]
}

For a multilabel dataset, the label field should be a list of integers representing the corresponding class labels.

To use it with our Python API, you can use our autointent.Dataset object.

from autointent import Dataset

dataset = Dataset.from_dict({"train": [...]})

To load a dataset from the file system into Python, the autointent.Dataset.from_json() method exists:

dataset = Dataset.from_json("/path/to/json")

AutoML goes brrr…#

Once the data is ready, you can start building the optimal classifier from the command line:

autointent data.train_path="path/to/your/data.json"

This command will start the hyperparameter search in the default search space.

As a result, a runs folder will be created in the current working directory, which will save the selected classifier ready for inference.

Similar actions but in a limited mode can be started using the Python API:

from autointent import PipelineOptimizer

pipeline_optimizer = PipelineOptimizer.default(multilabel=False)
pipeline_optimizer.fit(dataset)

Inference#

To apply the built classifier to new data, you can use our Python API:

from autointent import Pipeline

pipeline = Pipeline.load("path/to/run/directory")
utterances = ["123", "hello world"]
prediction = pipeline.predict(utterances)

Modular Approach#

If there is no need to iterate over pipelines and hyperparameters, you can import classification methods directly.

from autointent.modules import KNNScorer

scorer = KNNScorer(embedder_name="sergeyzh/rubert-tiny-turbo", k=1)
train_utterances = [
    "why is there a hold on my american saving bank account",
    "i am not sure why my account is blocked",
    "why is there a hold on my capital one checking account",
]
train_labels = [0, 2, 1]
scorer.fit(train_utterances, train_labels)
test_utterances = [
    "i think my account is blocked but i do not know the reason",
    "can you tell me why is my bank account frozen",
]
scorer.predict(test_utterances)