fastText的使用

Learning a text classifier using fastText

Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.

What is text classification?

The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc…) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

As an example, we build a classifier which automatically classifies stackexchange questions about cooking into one of several possible tags, such as pot, bowl or baking.

Installing fastText

The first step of this tutorial is to install and build fastText. It only requires a c++ compiler with good support of c++11.

Let us start by cloning the fastText repository:

>> git clone git@github.com/facebookresearch/fastText.git

Move to the fastText directory and build it:

>> cd fastText && make

Running the binary without any argument will print the high level documentation, showing the different usecases supported by fastText:

>> ./fasttext usage: fasttext <command> <args>  The commands supported by fasttext are:  supervised     train a supervised classifier test           evaluate a supervised classifier predict        predict most likely labels predict-prob   predict most likely labels with probabilities skipgram       train a skipgram model cbow           train a cbow model print-vectors  print vectors given a trained model

In this tutorial, we mainly use the supervised, test and predict subcommands, which corresponds to learning (and using) text classifier. For an introduction to the other functionalities of fastText, please see the tutorial about learning word vectors.

Getting and preparing the data

As mentioned in the introduction, we need labeled data to train our supervised classifier. In this tutorial, we are interested in building a classifier to automatically recognize the topic of a stackexchange question about cooking. Let’s download examples of questions from the cooking section of Stackexchange, and their associated tags:

>> wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/cooking.stackexchange.tar.gz && tar xvzf cooking.stackexchange.tar.gz >> head cooking.stackexchange.txt

Each line of the text file contains a list of labels, followed by the corresponding document. All the labels start by the __label__ prefix, which is how fastText recognize what is a label or what is a word. The model is then trained to predict the labels given the word in the document.

Before training our first classifier, we need to split the data into train and validation. We will use the validation set to evaluate how good the learned classifier is on new data.

>> wc cooking.stackexchange.txt     15404  169582 1401900 cooking.stackexchange.txt

Our full dataset contains 15404 examples. Let’s split it into a training set of 12404 examples and a validation set of 3000 examples:

>> head -n 12404 cooking.stackexchange.txt > cooking.train >> tail -n 3000 cooking.stackexchange.txt > cooking.valid

Our first classifier

We are now ready to train our first classifier:

>> ./fasttext supervised -input cooking.train -output model_cooking Read 0M words Number of words:  14598 Number of labels: 734 Progress: 100.0%  words/sec/thread: 75109  lr: 0.000000  loss: 5.708354  eta: 0h0m 

The -input command line option indicates the file containing the training examples, while the -output option indicates where to save the model. At the end of training, a file model_cooking.bin, containing the trained classifier, is created in the current directory.

It is possible to directly test our classifier interactively, by running the command:

>> ./fasttext predict model_cooking.bin -

and then typing a sentence. Let’s first try the sentence:

Which baking dish is best to bake a banana bread ?

The predicted tag is baking which fits well to this question. Let us now try a second example:

Why not put knives in the dishwasher?

The label predicted by the model is food-safety, which is not relevant. Somehow, the model seems to fail on simple examples. To get a better sense of its quality, let’s test it on the validation data by running:

>> ./fasttext test model_cooking.bin cooking.valid                  N  3000 P@1  0.124 R@1  0.0541 Number of examples: 3000

The output of fastText are the precision at one (P@1) and the recall at one (R@1). We can also compute the precision at five and recall at five with:

>> ./fasttext test model_cooking.bin cooking.valid 5                N  3000 P@5  0.0668 R@5  0.146 Number of examples: 3000

Advanced reader: precision and recall

The precision is the number of correct labels among the labels predicted by fastText. The recall is the number of labels that successfully were predicted, among all the real labels. Let’s take an example to make this more clear:

Why not put knives in the dishwasher?

On Stack Exchange, this sentence is labeled with three tags: equipment, cleaning and knives. The top five labels predicted by the model can be obtained with:

>> ./fasttext predict model_cooking.bin - 5

are food-safety, baking, equipment, substitutions and bread.

Thus, one out of five labels predicted by the model is correct, giving a precision of 0.20. Out of the three real labels, only one is predicted by the model, giving a recall of 0.33.

For more details, see the related Wikipedia page.

Making the model better

The model obtained by running fastText with the default arguments is pretty bad at classifying new questions. Let’s try to improve the performance, by changing the default parameters.

preprocessing the data

Looking at the data, we observe that some words contain uppercase letter or punctuation. One of the first step to improve the performance of our model is to apply some simple pre-processing. A crude normalization can be obtained using command line tools such as sed and tr:

>> cat cooking.stackexchange.txt | sed -e "s/([.!?,'/()])/ 1 /g" | tr "[:upper:]" "[:lower:]" > cooking.preprocessed.txt >> head -n 12404 cooking.preprocessed.txt > cooking.train >> tail -n 3000 cooking.preprocessed.txt > cooking.valid 

Let’s train a new model on the pre-processed data:

>> ./fasttext supervised -input cooking.train -output model_cooking Read 0M words Number of words:  9012 Number of labels: 734 Progress: 100.0%  words/sec/thread: 82041  lr: 0.000000  loss: 5.671649  eta: 0h0m h-14m   >> ./fasttext test model_cooking.bin cooking.valid  N  3000 P@1  0.164 R@1  0.0717 Number of examples: 3000

We observe that thanks to the pre-processing, the vocabulary is smaller (from 14k words to 9k). The precision is also starting to go up by 4%!

more epochs and larger learning rate

By default, fastText sees each training example only five times during training, which is pretty small, given that our training set only have 12k training examples. The number of times each examples is seen (also known as the number of epochs), can be increased using the -epoch option:

>> ./fasttext supervised -input cooking.train -output model_cooking -epoch 25  Read 0M words Number of words:  9012 Number of labels: 734 Progress: 100.0%  words/sec/thread: 77633  lr: 0.000000  loss: 7.147976  eta: 0h0m

Let’s test the new model:

>> ./fasttext test model_cooking.bin cooking.valid                                         N  3000 P@1  0.501 R@1  0.218 Number of examples: 3000

This is much better! Another way to change the learning speed of our model is to increase (or decrease) the learning rate of the algorithm. This corresponds to how much the model changes after processing each example. A learning rate of 0 would means that the model does not change at all, and thus, does not learn anything. Good values of the learning rate are in the range 0.1 - 1.0.

>> ./fasttext supervised -input cooking.train -output model_cooking -lr 1.0   Read 0M words Number of words:  9012 Number of labels: 734 Progress: 100.0%  words/sec/thread: 81469  lr: 0.000000  loss: 6.405640  eta: 0h0m  >> ./fasttext test model_cooking.bin cooking.valid                          N  3000 P@1  0.563 R@1  0.245 Number of examples: 3000

Even better! Let’s try both together:

>> ./fasttext supervised -input cooking.train -output model_cooking -lr 1.0 -epoch 25 Read 0M words Number of words:  9012 Number of labels: 734 Progress: 100.0%  words/sec/thread: 76394  lr: 0.000000  loss: 4.350277  eta: 0h0m  >> ./fasttext test model_cooking.bin cooking.valid                                    N  3000 P@1  0.585 R@1  0.255 Number of examples: 3000

Let us now add a few more features to improve even further our performance!

word n-grams

Finally, we can improve the performance of a model by using word bigrams, instead of just unigrams. This is especially important for classification problems where word order is important, such as sentiment analysis.

>> ./fasttext supervised -input cooking.train -output model_cooking -lr 1.0 -epoch 25 -wordNgrams 2 Read 0M words Number of words:  9012 Number of labels: 734 Progress: 100.0%  words/sec/thread: 75366  lr: 0.000000  loss: 3.226064  eta: 0h0m   >> ./fasttext test model_cooking.bin cooking.valid                                                  N  3000 P@1  0.599 R@1  0.261 Number of examples: 3000

With a few steps, we were able to go from a precision at one of 12.4% to 59.9%. Important steps included:

• preprocessing the data ;
• changing the number of epochs (using the option -epoch, standard range [5 - 50]) ;
• changing the learning rate (using the option -lr, standard range [0.1 - 1.0]) ;
• using word n-grams (using the option -wordNgrams, standard range [1 - 5]).

Advanced readers: What is a Bigram?

A ‘unigram’ refers to a single undividing unit, or token, usually used as an input to a model. For example a unigram can a word or a letter depending on the model. In fastText, we work at the word level and thus unigrams are words.

Similarly we denote by ‘bigram’ the concatenation of 2 consecutive tokens or words. Similarly we often talk about n-gram to refer to the concatenation any n consecutive tokens.

For example, in the sentence, ‘Last donut of the night’, the unigrams are ‘last’, ‘donut’, ‘of’, ‘the’ and ‘night’. The bigrams are: ‘Last donut’, ‘donut of’, ‘of the’ and ‘the night’.

Bigrams are particularly interesting because, for most sentences, you can reconstruct the order of the words just by looking at a bag of n-grams.

Let us illustrate this by a simple exercise, given the following bigrams, try to reconstruct the original sentence: ‘all out’, ‘I am’, ‘of bubblegum’, ‘out of’ and ‘am all’.
It is common to refer to a word as a unigram.

Scaling things up

Since we are training our model on a few thousands of examples, the training only takes a few seconds. But training models on larger datasets, with more labels can start to be too slow. A potential solution to make the training faster is to use the hierarchical softmax, instead of the regular softmax [Add a quick explanation of the hierarchical softmax]. This can be done with the option -loss hs:

>> ./fasttext supervised -input cooking.train -output model_cooking -lr 1.0 -epoch 25 -wordNgrams 2 -bucket 200000 -dim 50 -loss hs Read 0M words Number of words:  9012 Number of labels: 734 Progress: 100.0%  words/sec/thread: 2199406  lr: 0.000000  loss: 1.718807  eta: 0h0m 

Training should now take less than a second.

Conclusion

In this tutorial, we gave a brief overview of how to use fastText to train powerful text classifiers. We had a light overview of some of the most important options to tune.

文章来源: fastText的使用