TextKNNClassifier is a k-nearest neighbors classifier for text data. It uses a compression algorithm to compute the distance between texts and predicts the label of a test entry based on the labels of the k-nearest neighbors in the training data.
You can install TextKNNassifier using pip:
pip install textknnassifierHere's an example of how to use TextKNNClassifier:
from textknnassifier import classifier
training_data = [
"This is a test",
"Another test",
"General Tarkin",
"General Grievous",
]
training_labels = ["test", "test", "star_wars", "star_wars"]
testing_data = [
"This is a test",
"Testing here too!",
"General Kenobi",
"General Skywalker",
]
KNN = classifier.TextKNNClassifier(n_neighbors=2)
KNN.fit(training_data, training_labels)
predicted_labels = KNN.predict(testing_data)
print(predicted_labels)
# Output: ['test', 'test', 'star_wars', 'star_wars']In this example, we create a TextKNNClassifier instance and use it to predict the labels of the test entries. The initialization is given n_neighbors=2, this denotes the number of training datapoints to consider for predicting the testing label. The fit method takes two arguments: the training data, and the training labels. It simply stores these values for later use. The predict method takes the testing data as an argument and returns the predicted labels.
- Jiang, Z., Yang, M., Tsirlin, M., Tang, R., Dai, Y., & Lin, J. (2023, July). “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors. In Findings of the Association for Computational Linguistics: ACL 2023 (pp. 6810-6828).