What is TF.Text in TensorFlow 2.0

TF.Text

Introduction

TensorFlow Text provides a collection of text related classes and ops ready to use with TensorFlow 2.0. The library can perform the preprocessing regularly required by text-based models, and includes other features useful for sequence modeling not provided by core TensorFlow.

The benefit of using these ops in your text preprocessing is that they are done in the TensorFlow graph. You do not need to worry about tokenization in training being different than the tokenization at inference, or managing preprocessing scripts.

Eager Execution

TensorFlow Text requires TensorFlow 2.0, and is fully compatible with eager mode and graph mode.


Note: On rare occassions, this import may fail looking for the TF library. Please reset the runtime and rerun the pip install above.In [0]:

!pip install tensorflow-text

In [0]:

import tensorflow as tfimport tensorflow_text as text

Unicode

Most ops expect that the strings are in UTF-8. If you’re using a different encoding, you can use the core tensorflow transcode op to transcode into UTF-8. You can also use the same op to coerce your string to structurally valid UTF-8 if your input could be invalid.In [0]:

docs = tf.constant([u'Everything not saved will be lost.'.encode('UTF-16-BE'), u'Sad☹'.encode('UTF-16-BE')])utf8_docs = tf.strings.unicode_transcode(docs, input_encoding='UTF-16-BE', output_encoding='UTF-8')

Tokenization

Tokenization is the process of breaking up a string into tokens. Commonly, these tokens are words, numbers, and/or punctuation.

The main interfaces are Tokenizer and TokenizerWithOffsets which each have a single method tokenize and tokenize_with_offsets respectively. There are multiple tokenizers available now. Each of these implement TokenizerWithOffsets (which extends Tokenizer) which includes an option for getting byte offsets into the original string. This allows the caller to know the bytes in the original string the token was created from.

All of the tokenizers return RaggedTensors with the inner-most dimension of tokens mapping to the original individual strings. As a result, the resulting shape’s rank is increased by one. Please review the ragged tensor guide if you are unfamiliar with them. https://www.tensorflow.org/guide/ragged_tensors

WhitespaceTokenizer

This is a basic tokenizer that splits UTF-8 strings on ICU defined whitespace characters (eg. space, tab, new line).In [0]:

tokenizer = text.WhitespaceTokenizer()tokens = tokenizer.tokenize(['everything not saved will be lost.', u'Sad☹'.encode('UTF-8')])print(tokens.to_list())

UnicodeScriptTokenizer

This tokenizer splits UTF-8 strings based on Unicode script boundaries. The script codes used correspond to International Components for Unicode (ICU) UScriptCode values. See: http://icu-project.org/apiref/icu4c/uscript_8h.html

In practice, this is similar to the WhitespaceTokenizer with the most apparent difference being that it will split punctuation (USCRIPT_COMMON) from language texts (eg. USCRIPT_LATIN, USCRIPT_CYRILLIC, etc) while also separating language texts from each other.In [0]:

tokenizer = text.UnicodeScriptTokenizer()tokens = tokenizer.tokenize(['everything not saved will be lost.', u'Sad☹'.encode('UTF-8')])print(tokens.to_list())

Unicode split

When tokenizing languages without whitespace to segment words, it is common to just split by character, which can be accomplished using the unicode_split op found in core.In [0]:

tokens = tf.strings.unicode_split([u"仅今年前".encode('UTF-8')], 'UTF-8')print(tokens.to_list())

Offsets

When tokenizing strings, it is often desired to know where in the original string the token originated from. For this reason, each tokenizer which implements TokenizerWithOffsets has a tokenize_with_offsets method that will return the byte offsets along with the tokens. The offset_starts lists the bytes in the original string each token starts at, and the offset_limits lists the bytes where each token ends.In [0]:

tokenizer = text.UnicodeScriptTokenizer()(tokens, offset_starts, offset_limits) = tokenizer.tokenize_with_offsets(['everything not saved will be lost.', u'Sad☹'.encode('UTF-8')])print(tokens.to_list())print(offset_starts.to_list())print(offset_limits.to_list())

TF.Data Example

Tokenizers work as expected with the tf.data API. A simple example is provided below.In [0]:

docs = tf.data.Dataset.from_tensor_slices([['Never tell me the odds.'], ["It's a trap!"]])tokenizer = text.WhitespaceTokenizer()tokenized_docs = docs.map(lambda x: tokenizer.tokenize(x))iterator = iter(tokenized_docs)print(next(iterator).to_list())print(next(iterator).to_list())

Other Text Ops

TF.Text packages other useful preprocessing ops. We will review a couple below.

Wordshape

A common feature used in some natural language understanding models is to see if the text string has a certain property. For example, a sentence breaking model might contain features which check for word capitalization or if a punctuation character is at the end of a string.

Wordshape defines a variety of useful regular expression based helper functions for matching various relevant patterns in your input text. Here are a few examples.In [0]:

tokenizer = text.WhitespaceTokenizer()tokens = tokenizer.tokenize(['Everything not saved will be lost.', u'Sad☹'.encode('UTF-8')])# Is capitalized?f1 = text.wordshape(tokens, text.WordShape.HAS_TITLE_CASE)# Are all letters uppercased?f2 = text.wordshape(tokens, text.WordShape.IS_UPPERCASE)# Does the token contain punctuation?f3 = text.wordshape(tokens, text.WordShape.HAS_SOME_PUNCT_OR_SYMBOL)# Is the token a number?f4 = text.wordshape(tokens, text.WordShape.IS_NUMERIC_VALUE)print(f1.to_list())print(f2.to_list())print(f3.to_list())print(f4.to_list())

N-grams & Sliding Window

N-grams are sequential words given a sliding window size of n. When combining the tokens, there are three reduction mechanisms supported. For text, you would want to use Reduction.STRING_JOIN which appends the strings to each other. The default separator character is a space, but this can be changed with the string_separater argument.

The other two reduction methods are most often used with numerical values, and these are Reduction.SUM and Reduction.MEAN.In [0]:

tokenizer = text.WhitespaceTokenizer()tokens = tokenizer.tokenize(['Everything not saved will be lost.', u'Sad☹'.encode('UTF-8')])# Ngrams, in this case bi-gram (n = 2)bigrams = text.ngrams(tokens, 2, reduction_type=text.Reduction.STRING_JOIN)print(bigrams.to_list())

What is tf.expand_dims?

  1. expand_dims will not add or reduce elements in a tensor, it just changes the shape by adding 1 to dimensions.
  2. For example, a vector with 10 elements could be treated as a 10×1 matrix.
  3. The situation I have met to use expand_dims is when I tried to build a ConvNet to classify grayscale images. The grayscale images will be loaded as matrix of size [320, 320]. However, tf.nn.conv2d require input to be [batch, in_height, in_width, in_channels], where the in_channels dimension is missing in my data which in this case should be 1. So I used expand_dims to add one more dimension

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channels_first vs channels_last

Channel last: The final image data of the channel is represented in a three-dimensional array where the last channel color represents the channels, e.g. [Rows] [columns] [channels].

Channel First: Image data is represented in a three-dimensional array where the first channel represents coloured channels, e.g. [Channel] [rows] [columns].

It shows the order of channels in your input tensor. Putting data_format into channel_first, you say that the size of your tensor for each layer would be as follows: (batch, channel, height, width), but for Channel_last you have (batch, height, width, channel).

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