underthesea-core 3.3.1

Underthesea Core

Underthesea Core

Underthesea Core is a powerful extension of the popular natural language processing library Underthesea, which includes a range of efficient data preprocessing tools and machine learning models for training. Built with Rust for optimal performance, Underthesea Core offers fast processing speeds and is easy to implement, with Python bindings for seamless integration into existing projects. This extension is an essential tool for developers looking to build high-performance NLP systems that deliver accurate and reliable results.

Installation

pip install underthesea-core

Version

Current version: 2.0.0

What's New in 2.0.0

• L-BFGS optimizer with OWL-QN for L1 regularization
• 10x faster feature lookup with flat data structure
• 1.24x faster than python-crfsuite for word segmentation
• Loop unrolling and unsafe bounds-check elimination for performance

Usage

CRFTrainer

Train a CRF model with L-BFGS optimization:

from underthesea_core import CRFTrainer, CRFTagger

# Prepare training data
# X: list of sequences, each sequence is a list of feature lists (one per token)
# y: list of label sequences
X_train = [
    [["word=Tôi", "is_upper=False"], ["word=yêu", "is_upper=False"], ["word=Việt", "is_upper=True"], ["word=Nam", "is_upper=True"]],
    [["word=Hà", "is_upper=True"], ["word=Nội", "is_upper=True"], ["word=đẹp", "is_upper=False"]],
]
y_train = [
    ["O", "O", "B-LOC", "I-LOC"],
    ["B-LOC", "I-LOC", "O"],
]

# Create trainer with L-BFGS optimizer
trainer = CRFTrainer(
    loss_function="lbfgs",  # L-BFGS with OWL-QN (recommended)
    l1_penalty=1.0,         # L1 regularization
    l2_penalty=0.001,       # L2 regularization
    max_iterations=100,
    verbose=1
)

# Train and get model
model = trainer.train(X_train, y_train)
print(f"Labels: {model.get_labels()}")
print(f"Features: {model.num_state_features()}")

# Save model
model.save("ner_model.bin")

CRFTagger

Load a trained model and make predictions:

from underthesea_core import CRFTagger, CRFModel

# Load model and create tagger
model = CRFModel.load("ner_model.bin")
tagger = CRFTagger.from_model(model)

# Or load directly
tagger = CRFTagger()
tagger.load("ner_model.bin")

# Predict labels for a sequence
features = [
    ["word=Tôi", "is_upper=False"],
    ["word=sống", "is_upper=False"],
    ["word=ở", "is_upper=False"],
    ["word=Hà", "is_upper=True"],
    ["word=Nội", "is_upper=True"],
]
labels = tagger.tag(features)
print(labels)  # ['O', 'O', 'O', 'B-LOC', 'I-LOC']

# Get labels with score
labels, score = tagger.tag_with_score(features)
print(f"Labels: {labels}, Score: {score}")

# Get marginal probabilities
marginals = tagger.marginals(features)
print(f"Marginals shape: {len(marginals)}x{len(marginals[0])}")

CRFFeaturizer

Extract features from tokenized sentences:

from underthesea_core import CRFFeaturizer

features = ["T[-1]", "T[0]", "T[1]"]
dictionary = set(["sinh viên"])
featurizer = CRFFeaturizer(features, dictionary)
sentences = [[["sinh", "X"], ["viên", "X"], ["đi", "X"], ["học", "X"]]]
featurizer.process(sentences)
# [[['T[-1]=BOS', 'T[0]=sinh', 'T[1]=viên'],
#   ['T[-1]=sinh', 'T[0]=viên', 'T[1]=đi'],
#   ['T[-1]=viên', 'T[0]=đi', 'T[1]=học'],
#   ['T[-1]=đi', 'T[0]=học', 'T[1]=EOS']]]

API Reference

CRFTrainer

Parameter	Type	Default	Description
loss_function	str	"lbfgs"	"lbfgs" (recommended) or "perceptron"
l1_penalty	float	0.0	L1 regularization coefficient
l2_penalty	float	0.01	L2 regularization coefficient
max_iterations	int	100	Maximum training iterations
learning_rate	float	0.1	Learning rate (perceptron only)
averaging	bool	True	Use averaged perceptron
verbose	int	1	Verbosity (0=quiet, 1=progress, 2=detailed)

CRFTagger

Method	Description
tag(features)	Predict labels for a sequence
tag_with_score(features)	Predict labels with sequence score
marginals(features)	Get marginal probabilities
labels()	Get all label names
num_labels()	Get number of labels

CRFModel

Method	Description
save(path)	Save model to file
load(path)	Load model from file
get_labels()	Get all label names
num_state_features()	Get number of state features
num_transition_features()	Get number of transition features