⚡ Quickstart

✨ Create the task

You can make a prediction directly indicating the model and calling the method predict().

from promptmeteo import DocumentClassifier

clf = DocumentClassifier(
        language            = 'en',
        model_provider_name = 'hf_pipeline',
        model_name          = 'google/flan-t5-small',
        prompt_labels       = ['positive', 'neutral', 'negative']
    )

clf.predict(['so cool!!'])

[['positive']]

✨ Train the task

You can also include examples to improve the results by calling the method train().

clf = clf.train(
    examples    = ['i am happy', 'doesnt matter', 'I hate it'],
    annotations = ['positive', 'neutral', 'negative'],
)

clf.predict(['so cool!!'])

[['positive']]

✨ Save a trained task

Once the model is trained it can be saved locally…

clf.save_model("hello_world.meteo")

✨ Load a trained task

… and loaded again to make new predictions.

from promptmeteo import DocumentClassifier

clf = DocumentClassifier(
        language            = 'en',
        model_provider_name = 'hf_pipeline',
        model_name          = 'google/flan-t5-small',
    ).load_model("hello_world.meteo")

clf.predict(['so cool!!'])

[['positive']]

Models can also be loaded without instantiating the class by using .load_model() as a function instead of a method:

from promptmeteo import DocumentClassifier

clf = DocumentClassifier.load_model("hello_world.meteo")