id: cheatsheet
title: Cheatsheet
Word representation learning
In order to learn word vectors do:
$ ./fasttext skipgram -input data.txt -output model
Obtaining word vectors
Print word vectors for a text file queries.txt containing words.
$ ./fasttext print-word-vectors model.bin < queries.txt
Text classification
In order to train a text classifier do:
$ ./fasttext supervised -input train.txt -output model
Once the model was trained, you can evaluate it by computing the precision and recall at k (P@k and R@k) on a test set using:
$ ./fasttext test model.bin test.txt 1
In order to obtain the k most likely labels for a piece of text, use:
$ ./fasttext predict model.bin test.txt k
In order to obtain the k most likely labels and their associated probabilities for a piece of text, use:
$ ./fasttext predict-prob model.bin test.txt k
If you want to compute vector representations of sentences or paragraphs, please use:
$ ./fasttext print-sentence-vectors model.bin < text.txt
Quantization
In order to create a .ftz file with a smaller memory footprint do:
$ ./fasttext quantize -output model
All other commands such as test also work with this model
$ ./fasttext test model.ftz test.txt
Autotune
Activate hyperparameter optimization with -autotune-validation argument:
$ ./fasttext supervised -input train.txt -output model -autotune-validation valid.txt
Set timeout (in seconds):
$ ./fasttext supervised -input train.txt -output model -autotune-validation valid.txt -autotune-duration 600
Constrain the final model size:
$ ./fasttext supervised -input train.txt -output model -autotune-validation valid.txt -autotune-modelsize 2M
来源：CSDN
作者：分子美食家
链接：https://blog.csdn.net/weixin_40083227/article/details/103611809