# FastTextClassifier **`class FastTextClassifier(config)`** Bases: [`Classifier`](squirro.lib.nlp.steps.classifiers.Classifier.md#squirro.lib.nlp.steps.classifiers.Classifier) The fastText [`Classifier`](squirro.lib.nlp.steps.classifiers.Classifier.md#squirro.lib.nlp.steps.classifiers.Classifier) uses the [fastText](https://fasttext.cc/) library for text embedding and classification provided by Facebook’s AI Research lab. **Input** - all input fields need to be of type [`str`](https://docs.python.org/3.11/library/stdtypes.html#str). **Output** - the output field is filled with data of type [`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict) { [`str`](https://docs.python.org/3.11/library/stdtypes.html#str): [`float`](https://docs.python.org/3.11/library/functions.html#float) }. The key of the [`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict) is the predicted class name and the value is the probability/confidence returned by the model. Parameters - `type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – fasttext - `cutoff` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``100000`) – Cutoff for quantization - `learning_rate` ([`float`](https://docs.python.org/3.11/library/functions.html#float)`, ``1.0`) – Learning rate - `min_count` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``1`) – Minimum number of words appearances to be included in dictionary - `min_prob` ([`float`](https://docs.python.org/3.11/library/functions.html#float)`, ``0.0`) – Minimum prediction probability to return - `n_epochs` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``25`) – Number of training epochs - `n_grams` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``2`) – N of N-grams - `n_predictions` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``None`) – Number of label predictions to return. By default this will be the number of unique labels. - `quantize` ([`bool`](https://docs.python.org/3.11/library/functions.html#bool)`, ``False`) – Whether or not to quantize the model **Example** ```json { "step": "classifier", "type": "fasttext", "input_fields": ["extract"], "output_field": "prediction", "label_field": "label", } ``` Methods SummaryMethods Documentation **`clean()`** Clean step **`load()`** Load a step **`process_doc(doc)`** Process a document Parameters `doc` ([`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)) – Document Returns Processed document Return type [Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document) **`save()`** Save a step **`train(docs)`** Train on a step of a set of documents Parameters `docs` (`generator``(`[`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)`)`) – Generator of documents Returns Generator of processed documents Return type generator([Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document))