fast-tfidf 0.1.2

A fast implementation of tf-idf

Fast TF-IDF

A blazingly fast TF-IDF implementation for Python that uses multiprocessing to parallelise document processing.

Key Features:

• orders of magnitude faster than TensorFlow's TextVectorization (see benchmarks below)
• tensorflow-compatible vocabulary and idf weights can be used with TensorFlow's TextVectorization, it just computes them much quicker
• it's much more memory efficient too, consuming less than half the memory required by TextVectorization for the same dataset in my tests

Installation

# Using pip
pip install fast-tfidf

# Or install from source
git clone https://github.com/npil/fast-tfidf.git
cd fast-tfidf
pip install -e .

Usage

This package provides vocabulary extraction and IDF weight calculation that can be used standalone or integrated with other libraries.

Basic Example

from fast_tfidf import get_vocabulary_and_idf_weights

# Your text documents
documents = [
    "machine learning is amazing",
    "deep learning and machine learning",
    "artificial intelligence and deep learning"
]

# Extract vocabulary and IDF weights
# Get top 1000 terms (unigrams only)
vocabulary, idf_weights = get_vocabulary_and_idf_weights(
    documents,
    n_features=1000,
    remove_stopwords=False,
    use_bigrams=False
)

print(f"Vocabulary size: {len(vocabulary)}")
print(f"Top terms: {vocabulary[:5]}")
print(f"IDF weights: {idf_weights[:5]}")

With Bigrams

# Include bigrams for richer representation
vocabulary, idf_weights = get_vocabulary_and_idf_weights(
    documents,
    n_features=2000,
    use_bigrams=True  # Enable bigrams
)

With Stopword Removal

# Remove common English stopwords
vocabulary, idf_weights = get_vocabulary_and_idf_weights(
    documents,
    n_features=1000,
    remove_stopwords=True  # Filter out stopwords
)

Integration with TensorFlow

from tensorflow.keras.layers import TextVectorization
from fast_tfidf import get_vocabulary_and_idf_weights

vocabulary, idf_weights = get_vocabulary_and_idf_weights(documents, n_features=2000)

vectoriser = TextVectorization(
    vocabulary=vocabulary,
    idf_weights=idf_weights,
    output_mode='tf_idf'
)

# No adapt() needed - use pre-computed weights! 🚀
# Your model can now use 'vectoriser' for TF-IDF transformation

Note: Although fast-tf-idf uses a different IDF formula than TensorFlow's default log((1 + n_docs) / (1 + df)) + 1 vs TensorFlow's internal formula), this only affects absolute values while preserving relative term importance.

Performance

Benchmark results comparing against TensorFlow's TextVectorization:

Documents	TensorFlow	fast-tfidf	Speedup
100	204 ms	29 ms	7.1x ⚡
1,000	1.06 s	28 ms	38.3x ⚡⚡
5,000	3.00 s	43 ms	70.4x ⚡⚡⚡
10,000	7.51 s	77 ms	97.0x ⚡⚡⚡⚡

See benchmarks/ directory for detailed results and comparison scripts.

Development

Dependencies are managed using poetry.

There is a vscode dev container that can be used for development, or otherwise you can run make install in the environment of your choice to install the package and all dependencies (requires poetry and make).

We use the following tools to ensure code quality:

1. ruff
2. mypy
3. pytest

You can run all formatting / linting / type checks using make lint (you can use make fix to automatically fix some formatting errors).

You can run unittests using make test.

You can run linting and unittests using make lint-test.