fastText-turbo
fastText with faster
prediction and two optional training options. It is a drop-in replacement:
same import fasttext, same API, same command line and the same model files,
so models trained with fastText-turbo load in the official fastText and the
other way around.
pip uninstall fasttext # both packages install the `fasttext` module
pip install fasttext-turbo
What is different from fastText
- Faster prediction, same labels. The output layer is computed eight labels at a time (on x86 with SIMD sums), top-k skips the log for labels that cannot enter the top k, and subword hashes are computed incrementally. Labels are the same as fastText's; probabilities are within about 1e-5 on x86 and byte-identical on ARM. On one CPU core, GlotLID v3 (dimension 256) predicts about 4x faster on x86 and 1.7x on Apple silicon. See Fast Inference for fastText for how it works and the full benchmarks.
- Same trained models: training gives byte-identical models to fastText.
- Multi-threaded prediction on the command line:
fasttext predict model.bin input.txt k threshold threads. The output keeps the input order. - Training on short spans (
-cropProb,-cropMin,-cropMax,-cropUnit char|word): with probabilitycropProba training line is replaced by a random span of its text, for robustness on short inputs. - Training on normalized lines (
-normProb): with probabilitynormProba training line is lowercased and stripped of punctuation and digits, as the CommonLID / OpenLID-v2 input cleaner does. - NumPy 1 and 2 are both supported.
Both training options are off by default; then training gives the same model as fastText. They are not stored in the model file.
import fasttext
model = fasttext.train_supervised(
input="train.txt", minn=2, maxn=5, dim=64, epoch=3, lr=0.8,
crop_prob=0.3, crop_min=5, crop_max=20, crop_unit="char", norm_prob=0.3)
labels, probs = model.predict("Das ist ein kleiner Test", k=3)
Everything else, including word vectors (skipgram, cbow), quantization
and autotune, works as documented in
fastText.
Source: https://github.com/CommonGlot/fastText-turbo. MIT license, like fastText.
Metadata
Release files for fasttext-turbo 0.1.0
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Built distributions (wheels)
Total release size: 57.8 MB
Release files / fasttext_turbo-0.1.0.tar.gz
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