ai.djl.modality.nlp.bert.BertTokenizer Maven / Gradle / Ivy
/*
* Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
* OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package ai.djl.modality.nlp.bert;
import ai.djl.modality.nlp.preprocess.SimpleTokenizer;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.LinkedList;
import java.util.List;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.stream.Collectors;
/** BertTokenizer is a class to help you encode question and paragraph sentence. */
public class BertTokenizer extends SimpleTokenizer {
private static final Pattern PATTERN = Pattern.compile("(\\S+?)([.,?!])?(\\s+|$)");
/** {@inheritDoc} */
@Override
public List tokenize(String input) {
List ret = new LinkedList<>();
Matcher m = PATTERN.matcher(input);
while (m.find()) {
ret.add(m.group(1));
String token = m.group(2);
if (token != null) {
ret.add(token);
}
}
return ret;
}
/**
* Returns a string presentation of the tokens.
*
* @param tokens a list of tokens
* @return a string presentation of the tokens
*/
public String tokenToString(List tokens) {
return String.join(" ", tokens);
}
/**
* Pads the tokens to the required length.
*
* @param the type of the List
* @param tokens the input tokens
* @param padItem the things to pad at the end
* @param num the total length after padding
* @return a list of padded tokens
*/
public List pad(List tokens, E padItem, int num) {
if (tokens.size() >= num) {
return tokens;
}
List padded = new ArrayList<>(num);
padded.addAll(tokens);
for (int i = tokens.size(); i < num; ++i) {
padded.add(padItem);
}
return padded;
}
/**
* Encodes questions and paragraph sentences.
*
* @param question the input question
* @param paragraph the input paragraph
* @return BertToken
*/
public BertToken encode(String question, String paragraph) {
List qToken = tokenize(question);
List pToken = tokenize(paragraph);
int validLength = qToken.size() + pToken.size();
qToken.add(0, "[CLS]");
qToken.add("[SEP]");
pToken.add("[SEP]");
List tokens = new ArrayList<>(qToken);
tokens.addAll(pToken);
int tokenTypeStartIdx = qToken.size();
long[] tokenTypeArr = new long[tokens.size()];
Arrays.fill(tokenTypeArr, tokenTypeStartIdx, tokenTypeArr.length, 1);
long[] attentionMaskArr = new long[tokens.size()];
Arrays.fill(attentionMaskArr, 1);
return new BertToken(
tokens,
Arrays.stream(tokenTypeArr).boxed().collect(Collectors.toList()),
Arrays.stream(attentionMaskArr).boxed().collect(Collectors.toList()),
validLength);
}
/**
* Encodes questions and paragraph sentences with max length.
*
* @param question the input question
* @param paragraph the input paragraph
* @param maxLength the maxLength
* @return BertToken
*/
public BertToken encode(String question, String paragraph, int maxLength) {
BertToken bertToken = encode(question, paragraph);
return new BertToken(
pad(bertToken.getTokens(), "[PAD]", maxLength),
pad(bertToken.getTokenTypes(), 0L, maxLength),
pad(bertToken.getAttentionMask(), 0L, maxLength),
bertToken.getValidLength());
}
}