最新JAVA的NLP工具DJL
零、其他：NLP工具包
LingPipe
是alias公司开发的一款自然语言处理软件包。
主题分类（Top Classification）
命名实体识别（Named Entity Recognition）
词性标注（Part-of Speech Tagging）
句题检测（Sentence Detection）
查询拼写检查（Query Spell Checking）
兴趣短语检测（Interseting Phrase Detection）
聚类（Clustering）
字符语言建模（Character Language Modeling）
医学文献下载/解析/索引（MEDLINE Download, Parsing and Indexing）
数据库文本挖掘（Database Text Mining）
中文分词（Chinese Word Segmentation）
情感分析（Sentiment Analysis）
语言辨别（Language Identification）
HanLP
HanLP是一系列模型与算法组成的NLP工具包，目标是普及自然语言处理在生产环境中的应用。
FudanNLP
FNLP主要是为中文自然语言处理而开发的工具包，也包含为实现这些任务的机器学习算法和数据集。 本工具包及其包含数据集使用LGPL3.0许可证。
信息检索： 文本分类 新闻聚类
中文处理： 中文分词 词性标注 实体名识别 关键词抽取 依存句法分析 时间短语识别
结构化学习： 在线学习 层次分类 聚类
Apache OpenNLP
OpenNLP支持最常见的NLP任务:
例如标记化，句子分段，词性标记，命名实体提取，分块，解析，语言检测和共指解析
1
Stanford CoreNLP
斯坦福大学开发的自然语言处理工具套件,包括词性标注、命名实体识别、共指消解等 NLP 任务:
一、简介
开源库以Java构建和部署深度学习、编写一次即可在任何地方运行。使用DJL开发模型并在您选择的引擎上运行。直观的API使用本机Java概念并抽象化了深度学习所涉及的复杂性。引入您自己的模型，或使用我们库中的最新模型在几分钟内进行部署。
二、开源地址：
https://github.com/awslabs/djl
三、例子或者用法
1、Single-shot object detection example
2、Train your first model
3、Image classification example
4、Transfer learning example
5、Train SSD model example
6、Multi-threaded inference example
7、Bert question and answer example
8、Instance segmentation example
9、Pose estimation example
10、Action recognition example
11、Multi-label dataset training example
四、官网地址：
https://djl.ai/
五、代码如下：
1、依赖：
2、问答案例:
package com.xxxx.bigdata.nlputils;
import ai.djl.Application;
import ai.djl.ModelException;
import ai.djl.inference.Predictor;
import ai.djl.mxnet.zoo.nlp.qa.QAInput;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;
import java.io.IOException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* An example of inference using BertQA.
*
*
See:
*
*
- the jupyter
* demo with more information about BERT.
* - the
* for information about running this example.
*
*
*/
public final class BertQaInference {
private static final Logger logger = LoggerFactory.getLogger(BertQaInference.class);
private BertQaInference() {}
public static void main(String[] args) throws IOException, TranslateException, ModelException {
String answer = BertQaInference.predict();
logger.info("Answer: {}", answer);
}
public static String predict() throws IOException, TranslateException, ModelException {
String question = "When did BBC Japan start broadcasting?";
String paragraph =
"BBC Japan was a general entertainment Channel.\n"
+ "Which operated between December 2004 and April 2006.\n"
+ "It ceased operations after its Japanese distributor folded.";
QAInput input = new QAInput(question, paragraph, 384);
logger.info("Paragraph: {}", input.getParagraph());
logger.info("Question: {}", input.getQuestion());
Criteria
Criteria.builder()
.optApplication(Application.NLP.QUESTION_ANSWER)
.setTypes(QAInput.class, String.class)
.optFilter("backbone", "bert")
.optFilter("dataset", "book_corpus_wiki_en_uncased")
.optProgress(new ProgressBar())
.build();
try (ZooModel
try (Predictor
return predictor.predict(input);
}
}
}
}
3、训练模型
package com.xxxx.bigdata.nlputils;
import ai.djl.Device;
import ai.djl.Model;
import ai.djl.basicdataset.CaptchaDataset;
import ai.djl.basicmodelzoo.cv.classification.ResNetV1;
import ai.djl.examples.training.util.Arguments;
import ai.djl.examples.training.util.ExampleTrainingResult;
import ai.djl.examples.training.util.TrainingUtils;
import ai.djl.metric.Metrics;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.types.Shape;
import ai.djl.nn.Block;
import ai.djl.nn.SequentialBlock;
import ai.djl.training.DefaultTrainingConfig;
import ai.djl.training.Trainer;
import ai.djl.training.dataset.Dataset;
import ai.djl.training.dataset.Dataset.Usage;
import ai.djl.training.dataset.RandomAccessDataset;
import ai.djl.training.evaluator.Accuracy;
import ai.djl.training.listener.TrainingListener;
import ai.djl.training.loss.SimpleCompositeLoss;
import ai.djl.training.loss.SoftmaxCrossEntropyLoss;
import ai.djl.training.util.ProgressBar;
import java.io.IOException;
import java.nio.file.Paths;
/**
* An example of training a CAPTCHA solving model.
*
*
See this
* information about this example.
*/
public final class TrainCaptcha {
private TrainCaptcha() {}
public static void main(String[] args) throws Exception{
TrainCaptcha.runExample(args);
}
public static ExampleTrainingResult runExample(String[] args)
throws Exception {
Arguments arguments = Arguments.parseArgs(args);
try (Model model = Model.newInstance()) {
model.setBlock(getBlock());
// get training and validation dataset
RandomAccessDataset trainingSet = getDataset(Usage.TRAIN, arguments);
RandomAccessDataset validateSet = getDataset(Usage.VALIDATION, arguments);
// setup training configuration
DefaultTrainingConfig config = setupTrainingConfig(arguments);
ExampleTrainingResult result;
try (Trainer trainer = model.newTrainer(config)) {
trainer.setMetrics(new Metrics());
Shape inputShape =
new Shape(1, 1, CaptchaDataset.IMAGE_HEIGHT, CaptchaDataset.IMAGE_WIDTH);
// initialize trainer with proper input shape
trainer.initialize(inputShape);
TrainingUtils.fit(
trainer,
arguments.getEpoch(),
trainingSet,
validateSet,
arguments.getOutputDir(),
"captcha");
result = new ExampleTrainingResult(trainer);
}
model.save(Paths.get(arguments.getOutputDir()), "captcha");
return result;
}
}
private static DefaultTrainingConfig setupTrainingConfig(Arguments arguments) {
SimpleCompositeLoss loss = new SimpleCompositeLoss();
for (int i = 0; i < CaptchaDataset.CAPTCHA_LENGTH; i++) {
loss.addLoss(new SoftmaxCrossEntropyLoss("loss_digit_" + i), i);
}
DefaultTrainingConfig config =
new DefaultTrainingConfig(loss)
.optDevices(Device.getDevices(arguments.getMaxGpus()))
.addTrainingListeners(
TrainingListener.Defaults.logging(arguments.getModelDir(),arguments.getBatchSize(),arguments.getEpoch(),arguments.getMaxGpus(),arguments.getOutputDir()));
for (int i = 0; i < CaptchaDataset.CAPTCHA_LENGTH; i++) {
config.addEvaluator(new Accuracy("acc_digit_" + i, i));
}
return config;
}
private static RandomAccessDataset getDataset(Dataset.Usage usage, Arguments arguments)
throws IOException {
CaptchaDataset dataset =
CaptchaDataset.builder()
.optUsage(usage)
.setSampling(arguments.getBatchSize(), true)
.optMaxIteration(arguments.getMaxIterations())
.build();
dataset.prepare(new ProgressBar());
return dataset;
}
private static Block getBlock() {
Block resnet =
ResNetV1.builder()
.setNumLayers(50)
.setImageShape(
new Shape(
1, CaptchaDataset.IMAGE_HEIGHT, CaptchaDataset.IMAGE_WIDTH))
.setOutSize(CaptchaDataset.CAPTCHA_OPTIONS * CaptchaDataset.CAPTCHA_LENGTH)
.build();
return new SequentialBlock()
.add(resnet)
.add(
resnetOutputList -> {
NDArray resnetOutput = resnetOutputList.singletonOrThrow();
NDList splitOutput =
resnetOutput
.reshape(
-1,
CaptchaDataset.CAPTCHA_LENGTH,
CaptchaDataset.CAPTCHA_OPTIONS)
.split(CaptchaDataset.CAPTCHA_LENGTH, 1);
NDList output = new NDList(CaptchaDataset.CAPTCHA_LENGTH);
for (NDArray outputDigit : splitOutput) {
output.add(outputDigit.squeeze(1));
}
return output;
});
}
}