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