FastText is a library for text classification and representation. It transforms text into continuous vectors that can later be used on any language related task. A few tutorials are available.
fastText uses a hashtable for either word or character ngrams. The size of the hashtable directly impacts the size of a model. To reduce the size of the model, it is possible to reduce the size of this table with the option '-hash'. For example a good value is 20000. Another option that greatly impacts the size of a model is the size of the vectors (-dim). This dimension can be reduced to save space but this can significantly impact performance. If that still produce a model that is too big, one can further reduce the size of a trained model with the quantization option.
## What would be the best way to represent word phrases rather than words?
## 表示单词短语而不是单词的最佳方法是什么?
Currently the best approach to represent word phrases or sentence is to take a bag of words of word vectors. Additionally, for phrases like “New York”, preprocessing the data so that it becomes a single token “New_York” can greatly help.
## Why does fastText produce vectors even for unknown words?
## 为什么 fastText 对未知词也产生向量?
One of the key features of fastText word representation is its ability to produce vectors for any words, even made-up ones.
Indeed, fastText word vectors are built from vectors of substrings of characters contained in it.
This allows to build vectors even for misspelled words or concatenation of words.
FastText 词表示的一个关键特征就是它能对任何词产生词向量, 即使是自制词.
事实上, fastText 词向量是由包含在其中的字符字串构成的.
这甚至允许为拼写错误的单词或拼接单词创建词向量.
## Why is the hierarchical softmax slightly worse in performance than the full softmax?
## 为什么分层 softmax 的效果比完全 softmax 效果要略差一些?
The hierachical softmax is an approximation of the full softmax loss that allows to train on large number of class efficiently. This is often at the cost of a few percent of accuracy.
Note also that this loss is thought for classes that are unbalanced, that is some classes are more frequent than others. If your dataset has a balanced number of examples per class, it is worth trying the negative sampling loss (-loss ns -neg 100).
However, negative sampling will still be very slow at test time, since the full softmax will be computed.
## Can I use fastText with python? Or other languages?
## 我能用 python 语言使用 fastText 吗? 或者其他语言?
There are few unofficial wrappers for python or lua available on github.
Github 上几乎没有非官方的 python 或者 lua 包装器.
## Can I use fastText with continuous data?
## 我能用 fastText 处理连续数据吗?
FastText works on discrete tokens and thus cannot be directly used on continuous tokens. However, one can discretize continuous tokens to use fastText on them, for example by rounding values to a specific digit ("12.3" becomes "12").
## There are misspellings in the dictionary. Should we improve text normalization?
## 词典中一些错误拼写的词. 我们应该提升文本规范化吗?
If the words are infrequent, there is no need to worry.
如果这些词出现频率不高, 无须理会.
## I'm encountering a NaN, why could this be?
## 我遇到了 NaN, 为什么会这样呢?
You'll likely see this behavior because your learning rate is too high. Try reducing it until you don't see this error anymore.
你出现这个情况可能是因为学习率太高. 尝试减小学习率直到看不到这个错误.
## My compiler / architecture can't build fastText. What should I do?
Try a newer version of your compiler. We try to maintain compatibility with older versions of gcc and many platforms, however sometimes maintaining backwards compatibility becomes very hard. In general, compilers and tool chains that ship with LTS versions of major linux distributions should be fair game. In any case, create an issue with your compiler version and architecture and we'll try to implement compatibility.
