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OPENNLP-1753: Switch to pre-trained Model binaries v1.3
- provides and adapts PR #799 for OpenNLP 2.x maintenance branch - adjusts version strings - adjusts index.html template to latest v1.3 copy for DownloadParserTest - adjusts examples in Dev manual
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‎opennlp-docs/src/docbkx/lemmatizer.xml‎

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@@ -41,7 +41,7 @@
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<para>
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<screen>
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<![CDATA[
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$ opennlp LemmatizerME opennlp-en-ud-ewt-lemmas-1.2-2.5.0.bin < sentences]]>
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$ opennlp LemmatizerME opennlp-en-ud-ewt-lemmas-1.3-2.5.4.bin < sentences]]>
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</screen>
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The Lemmatizer now reads a pos tagged sentence(s) per line from
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standard input. For example, you can copy this sentence to the
@@ -89,7 +89,7 @@ signed VERB sign
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<programlisting language="java">
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<![CDATA[
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LemmatizerModel model = null;
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try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-lemmas-1.2-2.5.0.bin"))) {
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try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-lemmas-1.3-2.5.4.bin"))) {
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model = new LemmatizerModel(modelIn);
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}
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]]>

‎opennlp-docs/src/docbkx/postagger.xml‎

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@@ -41,7 +41,7 @@ under the License.
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Download the English maxent pos model and start the POS Tagger Tool with this command:
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<screen>
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<![CDATA[
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$ opennlp POSTagger opennlp-en-ud-ewt-pos-1.2-2.5.0.bin]]>
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$ opennlp POSTagger opennlp-en-ud-ewt-pos-1.3-2.5.4.bin]]>
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</screen>
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The POS Tagger now reads a tokenized sentence per line from stdin.
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Copy these two sentences to the console:
@@ -69,7 +69,7 @@ Mr._PROPN Vinken_PROPN is_AUX chairman_NOUN of_ADP Elsevier_ADJ N.V._PROPN ,_PUN
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In the sample below it is loaded from disk.
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<programlisting language="java">
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<![CDATA[
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try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-pos-1.2-2.5.0.bin"){
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try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-pos-1.3-2.5.4.bin"){
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POSModel model = new POSModel(modelIn);
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}]]>
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</programlisting>
@@ -343,7 +343,7 @@ Arrays.stream(tags).forEach(k -> System.out.print(k + " "));]]>
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POS Tags using the custom model (en-custom-pos-maxent.bin): PROPN OTHER PRON ADJ NOUN PUNCT
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Output with the default model
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POS Tags using the default model (opennlp-en-ud-ewt-pos-1.2-2.5.0.bin): NOUN AUX PRON ADJ NOUN PUNCT
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POS Tags using the default model (opennlp-en-ud-ewt-pos-1.3-2.5.4.bin): NOUN AUX PRON ADJ NOUN PUNCT
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</literallayout>
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</para>
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</section>

‎opennlp-docs/src/docbkx/sentdetect.xml‎

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@@ -1,7 +1,7 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE chapter PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
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"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
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]>
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"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd"[
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]>
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<!--
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Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
@@ -57,31 +57,31 @@ Rudolph Agnew, 55 years old and former chairman of Consolidated Gold Fields PLC,
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</para>
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<section id="tools.sentdetect.detection.cmdline">
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<title>Sentence Detection Tool</title>
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<para>
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The easiest way to try out the Sentence Detector is the command line tool. The tool is only intended for demonstration and testing.
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Download the english sentence detector model and start the Sentence Detector Tool with this command:
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<screen>
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<![CDATA[
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$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.2-2.5.0.bin]]>
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</screen>
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Just copy the sample text from above to the console. The Sentence Detector will read it and echo one sentence per line to the console.
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Usually the input is read from a file and the output is redirected to another file. This can be achieved with the following command.
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<screen>
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<![CDATA[
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$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.2-2.5.0.bin < input.txt > output.txt]]>
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</screen>
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For the english sentence model from the website the input text should not be tokenized.
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</para>
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<title>Sentence Detection Tool</title>
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<para>
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The easiest way to try out the Sentence Detector is the command line tool. The tool is only intended for demonstration and testing.
63+
Download the english sentence detector model and start the Sentence Detector Tool with this command:
64+
<screen>
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<![CDATA[
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$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin]]>
67+
</screen>
68+
Just copy the sample text from above to the console. The Sentence Detector will read it and echo one sentence per line to the console.
69+
Usually the input is read from a file and the output is redirected to another file. This can be achieved with the following command.
70+
<screen>
71+
<![CDATA[
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$ opennlp SentenceDetector opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin < input.txt > output.txt]]>
73+
</screen>
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For the english sentence model from the website the input text should not be tokenized.
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</para>
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</section>
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<section id="tools.sentdetect.detection.api">
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<title>Sentence Detection API</title>
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<para>
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The Sentence Detector can be easily integrated into an application via its API.
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To instantiate the Sentence Detector the sentence model must be loaded first.
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<programlisting language="java">
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<![CDATA[
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try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-sentence-1.2-2.5.0.bin")) {
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<title>Sentence Detection API</title>
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<para>
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The Sentence Detector can be easily integrated into an application via its API.
81+
To instantiate the Sentence Detector the sentence model must be loaded first.
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<programlisting language="java">
83+
<![CDATA[
84+
try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-sentence-1.3-2.5.4.bin")) {
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SentenceModel model = new SentenceModel(modelIn);
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}]]>
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</programlisting>

‎opennlp-docs/src/docbkx/tokenizer.xml‎

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@@ -97,7 +97,7 @@ $ opennlp SimpleTokenizer]]>
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our website.
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<screen>
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<![CDATA[
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$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.2-2.5.0.bin]]>
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$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin]]>
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</screen>
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To test the tokenizer copy the sample from above to the console. The
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whitespace separated tokens will be written back to the
@@ -107,7 +107,7 @@ $ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.2-2.5.0.bin]]>
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Usually the input is read from a file and written to a file.
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<screen>
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<![CDATA[
110-
$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.2-2.5.0.bin < article.txt > article-tokenized.txt]]>
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$ opennlp TokenizerME opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin < article.txt > article-tokenized.txt]]>
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</screen>
112112
It can be done in the same way for the Simple Tokenizer.
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</para>
@@ -151,7 +151,7 @@ London share prices were bolstered largely by continued gains on Wall Street and
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can be loaded.
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<programlisting language="java">
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<![CDATA[
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try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-tokens-1.2-2.5.0.bin")) {
154+
try (InputStream modelIn = new FileInputStream("opennlp-en-ud-ewt-tokens-1.3-2.5.4.bin")) {
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TokenizerModel model = new TokenizerModel(modelIn);
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}]]>
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</programlisting>

‎opennlp-tools/src/main/java/opennlp/tools/monitoring/StopCriteria.java‎

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@@ -19,18 +19,14 @@
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import java.util.function.Predicate;
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import opennlp.tools.ml.model.AbstractModel;
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/**
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* Stop criteria for model training. If the predicate is met, then the training is aborted.
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*
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* @see Predicate
29-
* @see AbstractModel
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*/
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public interface StopCriteria<T extends Number> extends Predicate<T> {
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33-
String FINISHED = "Training Finished after completing %s Iterations successfully.";
29+
String FINISHED = "Training finished after completing %s iterations successfully.";
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/**
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* @return A detailed message captured upon hitting the {@link StopCriteria} during model training.

‎opennlp-tools/src/main/java/opennlp/tools/util/DownloadUtil.java‎

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@@ -56,9 +56,9 @@ public class DownloadUtil {
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private static final Logger logger = LoggerFactory.getLogger(DownloadUtil.class);
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private static final String BASE_URL =
59-
System.getProperty("OPENNLP_DOWNLOAD_BASE_URL", "https://dlcdn.apache.org/opennlp/");
59+
System.getProperty("OPENNLP_DOWNLOAD_BASE_URL", "https://dlcdn.apache.org/opennlp/");
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private static final String MODEL_URI_PATH =
61-
System.getProperty("OPENNLP_DOWNLOAD_MODEL_PATH", "models/ud-models-1.2/");
61+
System.getProperty("OPENNLP_DOWNLOAD_MODEL_PATH", "models/ud-models-1.3/");
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private static final String OPENNLP_DOWNLOAD_HOME = "OPENNLP_DOWNLOAD_HOME";
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6464
private static Map<String, Map<ModelType, String>> availableModels;
@@ -202,7 +202,7 @@ private static void validateModel(URL sha512, Path downloadedModel) throws IOExc
202202
final String actualChecksum = calculateSHA512(downloadedModel);
203203
if (!actualChecksum.equalsIgnoreCase(expectedChecksum)) {
204204
throw new IOException("SHA512 checksum validation failed for " + downloadedModel.getFileName() +
205-
". Expected: " + expectedChecksum + ", but got: " + actualChecksum);
205+
". Expected: " + expectedChecksum + ", but got: " + actualChecksum);
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}
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}
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@@ -353,7 +353,7 @@ private void addModel(String locale, String link, Map<String, Map<ModelType, Str
353353
private String fetchPageIndex() {
354354
final StringBuilder html = new StringBuilder();
355355
try (BufferedReader br = new BufferedReader(
356-
new InputStreamReader(indexUrl.openStream(), StandardCharsets.UTF_8))) {
356+
new InputStreamReader(indexUrl.openStream(), StandardCharsets.UTF_8))) {
357357
String line;
358358
while ((line = br.readLine()) != null) {
359359
html.append(line);

‎opennlp-tools/src/test/java/opennlp/tools/AbstractModelLoaderTest.java‎

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@@ -38,7 +38,7 @@ public abstract class AbstractModelLoaderTest {
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private static final String BASE_URL_MODELS_V183 = "https://dlcdn.apache.org/opennlp/models/langdetect/1.8.3/";
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protected static final Path OPENNLP_DIR = Paths.get(System.getProperty("OPENNLP_DOWNLOAD_HOME",
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System.getProperty("user.home"))).resolve(".opennlp");
41-
protected static final String VER = "1.2-2.5.0";
41+
protected static final String VER = "1.3-2.5.4";
4242
protected static final String BIN = ".bin";
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protected static List<String> SUPPORTED_LANG_CODES = List.of(
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"en", "fr", "de", "it", "nl", "bg", "ca", "cs", "da", "el",

‎opennlp-tools/src/test/java/opennlp/tools/cmdline/lemmatizer/LemmatizerModelLoaderIT.java‎

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@@ -56,7 +56,7 @@ public void setup() {
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@ParameterizedTest(name = "Verify \"{0}\" tokenizer model loading")
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@ValueSource(strings = {"en-ud-ewt", "fr-ud-gsd", "de-ud-gsd", "it-ud-vit", "nl-ud-alpino",
59-
"bg-ud-btb", "ca-ud-ancora", "cs-ud-pdt", "da-ud-ddt", "el-ud-gdt", "es-ud-gsd", "et-ud-edt",
59+
"bg-ud-btb", "ca-ud-ancora", "cs-ud-pdtc", "da-ud-ddt", "el-ud-gdt", "es-ud-gsd", "et-ud-edt",
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"eu-ud-bdt", "fi-ud-tdt", "hr-ud-set", "hy-ud-bsut", "is-ud-icepahc", "ka-ud-glc", "kk-ud-ktb",
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"ko-ud-kaist", "lv-ud-lvtb", "no-ud-bokmaal", "pl-ud-pdb", "pt-ud-gsd", "ro-ud-rrt", "ru-ud-gsd",
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"sr-ud-set", "sk-ud-snk", "sl-ud-ssj", "sv-ud-talbanken", "tr-ud-boun", "uk-ud-iu"})

‎opennlp-tools/src/test/java/opennlp/tools/cmdline/postag/POSModelLoaderIT.java‎

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@@ -56,7 +56,7 @@ public void setup() {
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@ParameterizedTest(name = "Verify \"{0}\" POS model loading")
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@ValueSource(strings = {"en-ud-ewt", "fr-ud-gsd", "de-ud-gsd", "it-ud-vit", "nl-ud-alpino",
59-
"bg-ud-btb", "ca-ud-ancora", "cs-ud-pdt", "da-ud-ddt", "el-ud-gdt", "es-ud-gsd", "et-ud-edt",
59+
"bg-ud-btb", "ca-ud-ancora", "cs-ud-pdtc", "da-ud-ddt", "el-ud-gdt", "es-ud-gsd", "et-ud-edt",
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"eu-ud-bdt", "fi-ud-tdt", "hr-ud-set", "hy-ud-bsut", "is-ud-icepahc", "ka-ud-glc", "kk-ud-ktb",
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"ko-ud-kaist", "lv-ud-lvtb", "no-ud-bokmaal", "pl-ud-pdb", "pt-ud-gsd", "ro-ud-rrt", "ru-ud-gsd",
6262
"sr-ud-set", "sk-ud-snk", "sl-ud-ssj", "sv-ud-talbanken", "tr-ud-boun", "uk-ud-iu"})

‎opennlp-tools/src/test/java/opennlp/tools/cmdline/sentdetect/SentenceModelLoaderIT.java‎

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@@ -56,7 +56,7 @@ public void setup() {
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@ParameterizedTest(name = "Verify \"{0}\" sentence model loading")
5858
@ValueSource(strings = {"en-ud-ewt", "fr-ud-gsd", "de-ud-gsd", "it-ud-vit", "nl-ud-alpino",
59-
"bg-ud-btb", "ca-ud-ancora", "cs-ud-pdt", "da-ud-ddt", "el-ud-gdt", "es-ud-gsd", "et-ud-edt",
59+
"bg-ud-btb", "ca-ud-ancora", "cs-ud-pdtc", "da-ud-ddt", "el-ud-gdt", "es-ud-gsd", "et-ud-edt",
6060
"eu-ud-bdt", "fi-ud-tdt", "hr-ud-set", "hy-ud-bsut", "is-ud-icepahc", "ka-ud-glc", "kk-ud-ktb",
6161
"ko-ud-kaist", "lv-ud-lvtb", "no-ud-bokmaal", "pl-ud-pdb", "pt-ud-gsd", "ro-ud-rrt", "ru-ud-gsd",
6262
"sr-ud-set", "sk-ud-snk", "sl-ud-ssj", "sv-ud-talbanken", "tr-ud-boun", "uk-ud-iu"})

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