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@@ -34,3 +34,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 stanford-ner.jar filter=lfs diff=lfs merge=lfs -text
+stanford_model/stanford-ner-4.2.0-javadoc.jar filter=lfs diff=lfs merge=lfs -text
+stanford_model/stanford-ner-4.2.0-sources.jar filter=lfs diff=lfs merge=lfs -text
+stanford_model/stanford-ner-4.2.0.jar filter=lfs diff=lfs merge=lfs -text

stanford_model/LICENSE.txt

@@ -0,0 +1,339 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
+ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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stanford_model/NERDemo.java

@@ -0,0 +1,171 @@
+import edu.stanford.nlp.ie.AbstractSequenceClassifier;
+import edu.stanford.nlp.ie.crf.*;
+import edu.stanford.nlp.io.IOUtils;
+import edu.stanford.nlp.ling.CoreLabel;
+import edu.stanford.nlp.ling.CoreAnnotations;
+import edu.stanford.nlp.sequences.DocumentReaderAndWriter;
+import edu.stanford.nlp.util.Triple;
+
+import java.util.List;
+
+
+/** This is a demo of calling CRFClassifier programmatically.
+ *  <p>
+ *  Usage: {@code java -mx400m -cp "*" NERDemo [serializedClassifier [fileName]] }
+ *  <p>
+ *  If arguments aren't specified, they default to
+ *  classifiers/english.all.3class.distsim.crf.ser.gz and some hardcoded sample text.
+ *  If run with arguments, it shows some of the ways to get k-best labelings and
+ *  probabilities out with CRFClassifier. If run without arguments, it shows some of
+ *  the alternative output formats that you can get.
+ *  <p>
+ *  To use CRFClassifier from the command line:
+ *  </p><blockquote>
+ *  {@code java -mx400m edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier [classifier] -textFile [file] }
+ *  </blockquote><p>
+ *  Or if the file is already tokenized and one word per line, perhaps in
+ *  a tab-separated value format with extra columns for part-of-speech tag,
+ *  etc., use the version below (note the 's' instead of the 'x'):
+ *  </p><blockquote>
+ *  {@code java -mx400m edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier [classifier] -testFile [file] }
+ *  </blockquote>
+ *
+ *  @author Jenny Finkel
+ *  @author Christopher Manning
+ */
+
+public class NERDemo {
+
+  public static void main(String[] args) throws Exception {
+
+    String serializedClassifier = "classifiers/english.all.3class.distsim.crf.ser.gz";
+
+    if (args.length > 0) {
+      serializedClassifier = args[0];
+    }
+
+    AbstractSequenceClassifier<CoreLabel> classifier = CRFClassifier.getClassifier(serializedClassifier);
+
+    /* For either a file to annotate or for the hardcoded text example, this
+       demo file shows several ways to process the input, for teaching purposes.
+    */
+
+    if (args.length > 1) {
+
+      /* For the file, it shows (1) how to run NER on a String, (2) how
+         to get the entities in the String with character offsets, and
+         (3) how to run NER on a whole file (without loading it into a String).
+      */
+
+      String fileContents = IOUtils.slurpFile(args[1]);
+      List<List<CoreLabel>> out = classifier.classify(fileContents);
+      for (List<CoreLabel> sentence : out) {
+        for (CoreLabel word : sentence) {
+          System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
+        }
+        System.out.println();
+      }
+
+      System.out.println("---");
+      out = classifier.classifyFile(args[1]);
+      for (List<CoreLabel> sentence : out) {
+        for (CoreLabel word : sentence) {
+          System.out.print(word.word() + '/' + word.get(CoreAnnotations.AnswerAnnotation.class) + ' ');
+        }
+        System.out.println();
+      }
+
+      System.out.println("---");
+      List<Triple<String, Integer, Integer>> list = classifier.classifyToCharacterOffsets(fileContents);
+      for (Triple<String, Integer, Integer> item : list) {
+        System.out.println(item.first() + ": " + fileContents.substring(item.second(), item.third()));
+      }
+      System.out.println("---");
+      System.out.println("Ten best entity labelings");
+      DocumentReaderAndWriter<CoreLabel> readerAndWriter = classifier.makePlainTextReaderAndWriter();
+      classifier.classifyAndWriteAnswersKBest(args[1], 10, readerAndWriter);
+
+      System.out.println("---");
+      System.out.println("Per-token marginalized probabilities");
+      classifier.printProbs(args[1], readerAndWriter);
+
+      // -- This code prints out the first order (token pair) clique probabilities.
+      // -- But that output is a bit overwhelming, so we leave it commented out by default.
+      // System.out.println("---");
+      // System.out.println("First Order Clique Probabilities");
+      // ((CRFClassifier) classifier).printFirstOrderProbs(args[1], readerAndWriter);
+
+    } else {
+
+      /* For the hard-coded String, it shows how to run it on a single
+         sentence, and how to do this and produce several formats, including
+         slash tags and an inline XML output format. It also shows the full
+         contents of the {@code CoreLabel}s that are constructed by the
+         classifier. And it shows getting out the probabilities of different
+         assignments and an n-best list of classifications with probabilities.
+      */
+
+      String[] example = {"Good afternoon Rajat Raina, how are you today?",
+                          "I go to school at Stanford University, which is located in California." };
+      for (String str : example) {
+        System.out.println(classifier.classifyToString(str));
+      }
+      System.out.println("---");
+
+      for (String str : example) {
+        // This one puts in spaces and newlines between tokens, so just print not println.
+        System.out.print(classifier.classifyToString(str, "slashTags", false));
+      }
+      System.out.println("---");
+
+      for (String str : example) {
+        // This one is best for dealing with the output as a TSV (tab-separated column) file.
+        // The first column gives entities, the second their classes, and the third the remaining text in a document
+        System.out.print(classifier.classifyToString(str, "tabbedEntities", false));
+      }
+      System.out.println("---");
+
+      for (String str : example) {
+        System.out.println(classifier.classifyWithInlineXML(str));
+      }
+      System.out.println("---");
+
+      for (String str : example) {
+        System.out.println(classifier.classifyToString(str, "xml", true));
+      }
+      System.out.println("---");
+
+      for (String str : example) {
+        System.out.print(classifier.classifyToString(str, "tsv", false));
+      }
+      System.out.println("---");
+
+      // This gets out entities with character offsets
+      int j = 0;
+      for (String str : example) {
+        j++;
+        List<Triple<String,Integer,Integer>> triples = classifier.classifyToCharacterOffsets(str);
+        for (Triple<String,Integer,Integer> trip : triples) {
+          System.out.printf("%s over character offsets [%d, %d) in sentence %d.%n",
+                  trip.first(), trip.second(), trip.third, j);
+        }
+      }
+      System.out.println("---");
+
+      // This prints out all the details of what is stored for each token
+      int i=0;
+      for (String str : example) {
+        for (List<CoreLabel> lcl : classifier.classify(str)) {
+          for (CoreLabel cl : lcl) {
+            System.out.print(i++ + ": ");
+            System.out.println(cl.toShorterString());
+          }
+        }
+      }
+
+      System.out.println("---");
+
+    }
+  }
+
+}

stanford_model/README.txt

@@ -0,0 +1,295 @@
+Stanford NER - v4.2.0 - 2020-11-17
+----------------------------------------------
+
+This package provides a high-performance machine learning based named
+entity recognition system, including facilities to train models from
+supervised training data and pre-trained models for English.
+
+(c) 2002-2020.  The Board of Trustees of The Leland
+    Stanford Junior University. All Rights Reserved.
+
+Original CRF code by Jenny Finkel.
+Additional modules, features, internationalization, compaction, and
+support code by Christopher Manning, Dan Klein, Christopher Cox, Huy Nguyen
+Shipra Dingare, Anna Rafferty, and John Bauer.
+This release prepared by Jason Bolton.
+
+LICENSE
+
+The software is licensed under the full GPL v2+.  Please see the file LICENCE.txt
+
+For more information, bug reports, and fixes, contact:
+    Christopher Manning
+    Dept of Computer Science, Gates 2A
+    Stanford CA 94305-9020
+    USA
+    java-nlp-support@lists.stanford.edu
+    https://nlp.stanford.edu/software/CRF-NER.html
+
+CONTACT
+
+For questions about this distribution, please contact Stanford's JavaNLP group
+at java-nlp-user@lists.stanford.edu.  We provide assistance on a best-effort
+basis.
+
+TUTORIAL
+
+Quickstart guidelines, primarily for end users who wish to use the included NER
+models, are below.  For further instructions on training your own NER model,
+go to https://nlp.stanford.edu/software/crf-faq.html.
+
+INCLUDED SERIALIZED MODELS / TRAINING DATA
+
+The basic included serialized model is a 3 class NER tagger that can
+label: PERSON, ORGANIZATION, and LOCATION entities.  It is included as
+english.all.3class.distsim.crf.ser.gz.  It is trained on data from
+CoNLL, MUC6, MUC7, ACE, OntoNotes, and Wikipedia.
+Because this model is trained on both US
+and UK newswire, it is fairly robust across the two domains.
+
+We have also included a 4 class NER tagger trained on the CoNLL 2003
+Shared Task training data that labels for PERSON, ORGANIZATION,
+LOCATION, and MISC.  It is named
+english.conll.4class.distsim.crf.ser.gz .
+
+A third model is trained only on data from MUC and
+distinguishes between 7 different classes:
+english.muc.7class.distsim.crf.ser.gz.
+
+All of the serialized classifiers come in two versions, one trained to
+basically expected standard written English capitalization, and the other
+to ignore capitalization information. The case-insensitive versions
+of the three models available on the Stanford NER webpage.
+These models use a distributional similarity lexicon to improve performance
+(by between 1.5%-3% F-measure).  The distributional similarity features
+make the models perform substantially better, but they require rather
+more memory.  The distsim models are included in the release package.
+The nodistsim versions of the same models may be available on the
+Stanford NER webpage.
+
+Finally, we have models for other languages, including two German models,
+a Chinese model, and a Spanish model.  The files for these models can be
+found at:
+
+http://nlp.stanford.edu/software/CRF-NER.html
+
+
+QUICKSTART INSTRUCTIONS
+
+This NER system requires Java 1.8 or later.
+
+Providing java is on your PATH, you should be able to run an NER GUI
+demonstration by just clicking.  It might work to double-click on the
+stanford-ner.jar archive but this may well fail as the operating system
+does not give Java enough memory for our NER system, so it is safer to
+instead double click on the ner-gui.bat icon (Windows) or ner-gui.sh
+(Linux/Unix/MacOSX).  Then, using the top option from the Classifier
+menu, load a CRF classifier from the classifiers directory of the
+distribution.  You can then `either load a text file or web page from
+the File menu, or decide to use the default text in the window. Finally,
+you can now named entity tag the text by pressing the Run NER button.
+
+From a command line, you need to have java on your PATH and the
+stanford-ner.jar file and the lib directory in your CLASSPATH.  (The way of doing this depends on
+your OS/shell.)  The supplied ner.bat and ner.sh should work to allow
+you to tag a single file.  For example, for Windows:
+
+    ner file
+
+Or on Unix/Linux you should be able to parse the test file in the distribution
+directory with the command:
+
+java -mx600m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers/english.all.3class.distsim.crf.ser.gz -textFile sample.txt
+
+Here's an output option that will print out entities and their class to
+the first two columns of a tab-separated columns output file:
+
+java -mx600m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers/english.all.3class.distsim.crf.ser.gz -outputFormat tabbedEntities -textFile sample.txt > sample.tsv
+
+When run from a jar file, you also have the option of using a serialized
+classifier contained in the jar file.
+
+USING FULL STANFORD CORENLP NER FUNCTIONALITY
+
+This standalone distribution also allows access to the full NER
+capabilities of the Stanford CoreNLP pipeline. These capabilities
+can be accessed via the NERClassifierCombiner class.
+NERClassifierCombiner allows for multiple CRFs to be used together,
+and has options for recognizing numeric sequence patterns and time
+patterns with the rule-based NER of SUTime.
+
+Suppose one combines three CRF's CRF-1,CRF-2, and CRF-3 with the
+NERClassifierCombiner.  When the NERClassiferCombiner runs, it will
+first apply the NER tags of CRF-1 to the text, then it will apply
+CRF-2's NER tags to any tokens not tagged by CRF-1 and so on.  If
+the option ner.combinationMode is set to NORMAL (default), any label
+applied by CRF-1 cannot be applied by subsequent CRF's.  For instance
+if CRF-1 applies the LOCATION tag, no other CRF's LOCATION tag will be
+used.  If ner.combinationMode is set to HIGH_RECALL, this limitation
+will be deactivated.
+
+To use NERClassifierCombiner at the command-line, the jars in lib
+and stanford-ner.jar must be in the CLASSPATH.  Here is an example command:
+
+java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -ner.model \
+classifiers/english.conll.4class.distsim.crf.ser.gz,classifiers/english.muc.7class.distsim.crf.ser.gz \
+-ner.useSUTime false -textFile sample-w-time.txt
+
+Let's break this down a bit.  The flag "-ner.model" should be followed by a
+list of CRF's to be combined by the NERClassifierCombiner.  Some serialized
+CRF's are provided in the classifiers directory.  In this example the CRF's
+trained on the CONLL 4 class data and the MUC 7 class data are being combined.
+
+When the flag "-ner.useSUTime" is followed by "false", SUTime is shut off.  You should
+note that when the "false" is omitted, the text "4 days ago" suddenly is
+tagged with DATE.  These are the kinds of phrases SUTime can identify.
+
+NERClassifierCombiner can be run on different types of input as well.  Here is
+an example which is run on CONLL style input:
+
+java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -ner.model \
+classifiers/english.conll.4class.distsim.crf.ser.gz,classifiers/english.muc.7class.distsim.crf.ser.gz \
+-map word=0,answer=1 -testFile sample-conll-file.txt
+
+It is crucial to include the "-map word=0,answer=1" , which is specifying that
+the input test file has the words in the first column and the answer labels
+in the second column.
+
+It is also possible to serialize and load an NERClassifierCombiner.
+
+This command loads the three sample crfs with combinationMode=HIGH_RECALL
+and SUTime=false, and dumps them to a file named
+test_serialized_ncc.ncc.ser.gz
+
+java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -ner.model \
+classifiers/english.conll.4class.distsim.crf.ser.gz,classifiers/english.muc.7class.distsim.crf.ser.gz,\
+classifiers/english.all.3class.distsim.crf.ser.gz -ner.useSUTime false \
+-ner.combinationMode HIGH_RECALL -serializeTo test.serialized.ncc.ncc.ser.gz
+
+An example serialized NERClassifierCombiner with these settings is supplied in
+the classifiers directory.  Here is an example of loading that classifier and
+running it on the sample CONLL data:
+
+java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -loadClassifier \
+classifiers/example.serialized.ncc.ncc.ser.gz -map word=0,answer=1 \
+-testFile sample-conll-file.txt
+
+For a more exhaustive description of NERClassifierCombiner go to
+http://nlp.stanford.edu/software/ncc-faq.html
+
+PROGRAMMATIC USE
+
+The NERDemo file illustrates a couple of ways of calling the system
+programatically.  You should get the same results from
+
+java -cp stanford-ner.jar:lib/*:. -mx300m NERDemo classifiers/english.all.3class.distsim.crf.ser.gz sample.txt
+
+as from using CRFClassifier.  For more information on API calls, look in
+the enclosed javadoc directory: load index.html in a browser and look
+first at the edu.stanford.nlp.ie.crf package and CRFClassifier class.
+If you wish to train your own NER systems, look also at the
+edu.stanford.nlp.ie package NERFeatureFactory class.
+
+
+SERVER VERSION
+
+The NER code may also be run as a server listening on a socket:
+
+java -mx1000m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.NERServer 1234
+
+You can specify which model to load with flags, either one on disk:
+
+java -mx1000m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.NERServer -loadClassifier classifiers/all.3class.crf.ser.gz 1234
+
+Or if you have put a model inside the jar file, as a resource under, say, models:
+
+java -mx1000m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.NERServer -loadClassifier models/all.3class.crf.ser.gz 1234
+
+
+RUNNING CLASSIFIERS FROM INSIDE A JAR FILE
+
+The software can run any serialized classifier from within a jar file by
+following the -loadClassifier flag by some resource available within a
+jar file on the CLASSPATH.  An end user can make
+their own jar files with the desired NER models contained inside.
+This allows single jar file deployment.
+
+
+PERFORMANCE GUIDELINES
+
+Performance depends on many factors.  Speed and memory use depend on
+hardware, operating system, and JVM.  Accuracy depends on the data
+tested on.  Nevertheless, in the belief that something is better than
+nothing, here are some statistics from one machine on one test set, in
+semi-realistic conditions (where the test data is somewhat varied).
+
+ner-eng-ie.crf-3-all2006-distsim.ser.gz (older version of ner-eng-ie.crf-3-all2008-distsim.ser.gz)
+Memory: 320MB (on a 32 bit machine)
+PERSON	ORGANIZATION	LOCATION
+91.88	82.91		88.21
+
+
+--------------------
+CHANGES
+--------------------
+
+2020-11-17    4.2.0     Update for compatibility 
+
+2020-05-10    4.0.0     Update to UDv2.0 tokenization 
+
+2018-10-16    3.9.2     Update for compatibility 
+
+2018-02-27    3.9.1     KBP ner models for Chinese and Spanish 
+
+2017-06-09    3.8.0     Updated for compatibility
+
+2016-10-31    3.7.0     Improved Chinese NER
+
+2015-12-09    3.6.0     Updated for compatibility
+
+2015-04-20    3.5.2     synch standalone and CoreNLP functionality
+
+2015-01-29    3.5.1     Substantial accuracy improvements
+
+2014-10-26    3.5.0     Upgrade to Java 1.8
+
+2014-08-27    3.4.1     Add Spanish models
+
+2014-06-16      3.4     Fix serialization bug
+
+2014-01-04    3.3.1     Bugfix release
+
+2013-11-12    3.3.0     Update for compatibility
+
+2013-11-12    3.3.0     Update for compatibility
+
+2013-06-19    3.2.0     Improve handling of line-by-line input
+
+2013-04-04    1.2.8     nthreads option
+
+2012-11-11    1.2.7     Improved English 3 class model by including
+                        data from Wikipedia, release Chinese model
+
+2012-07-09    1.2.6     Minor bug fixes
+
+2012-05-22    1.2.5     Fix encoding issue
+
+2012-04-07    1.2.4     Caseless version of English models supported
+
+2012-01-06    1.2.3     Minor bug fixes
+
+2011-09-14    1.2.2     Improved thread safety
+
+2011-06-19    1.2.1     Models reduced in size but on average improved
+                        in accuracy (improved distsim clusters)
+
+2011-05-16      1.2     Normal download includes 3, 4, and 7
+                        class models. Updated for compatibility
+                        with other software releases.
+
+2009-01-16    1.1.1     Minor bug and usability fixes, changed API
+
+2008-05-07      1.1     Additional feature flags, various code updates
+
+2006-09-18      1.0     Initial release
+

stanford_model/build.xml

@@ -0,0 +1,192 @@
+<!-- build.xml file for ant for JavaNLP -->
+
+<!-- Before using this, unjar the sources' jar file into the src/ directory! -->
+
+<!-- A "project" describes a set of targets that may be requested
+     when Ant is executed.  The "default" attribute defines the
+     target which is executed if no specific target is requested,
+     and the "basedir" attribute defines the current working directory
+     from which Ant executes the requested task.  This is normally
+     set to the current working directory.
+-->
+
+<project name="JavaNLP" default="compile" basedir=".">
+
+  <property name="build.home"    value="${basedir}/classes"/>
+  <property name="build.tests"    value="${basedir}/classes"/>
+  <property name="docs.home"     value="${basedir}/docs"/>
+  <property name="src.home"      value="${basedir}/src"/>
+  <property name="javadoc.home"  value="${basedir}/javadoc"/>
+
+
+<!--  ==================== Compilation Control Options ==================== -->
+
+<!--
+
+  These properties control option settings on the Javac compiler when it
+  is invoked using the <javac> task.
+
+  compile.debug        Should compilation include the debug option?
+
+  compile.deprecation  Should compilation include the deprecation option?
+
+  compile.optimize     Should compilation include the optimize option?
+
+  compile.source       Source version compatibility
+
+  compile.target       Target class version compatibility
+
+-->
+
+  <property name="compile.debug"       value="true"/>
+  <property name="compile.deprecation" value="false"/>
+  <property name="compile.optimize"    value="true"/>
+  <property name="compile.source"      value="1.8" />
+  <property name="compile.target"      value="1.8" />
+  <property name="compile.encoding"      value="utf-8" />
+
+
+
+
+<!-- ==================== All Target ====================================== -->
+
+<!--
+
+  The "all" target is a shortcut for running the "clean" target followed
+  by the "compile" target, to force a complete recompile.
+
+-->
+
+  <target name="all" depends="clean,compile"
+   description="Clean build and dist directories, then compile"/>
+
+
+
+<!-- ==================== Clean Target ==================================== -->
+
+<!--
+
+  The "clean" target deletes any previous "build" and "dist" directory,
+  so that you can be ensured the application can be built from scratch.
+
+-->
+
+  <target name="clean" description="Delete old classes">
+    <delete dir="${build.home}/edu"/>
+  </target>
+
+
+
+<!-- ==================== Compile Target ================================== -->
+
+<!--
+
+  The "compile" target transforms source files (from your "src" directory)
+  into object files in the appropriate location in the build directory.
+  This example assumes that you will be including your classes in an
+  unpacked directory hierarchy under "/WEB-INF/classes".
+
+-->
+
+  <target name="compile" depends="prepare"
+   description="Compile Java sources">
+
+    <!-- Compile Java classes as necessary -->
+    <mkdir    dir="${build.home}"/>
+    <javac srcdir="${src.home}"
+          destdir="${build.home}"
+            debug="${compile.debug}"
+         encoding="${compile.encoding}"
+      deprecation="${compile.deprecation}"
+         optimize="${compile.optimize}"
+	   source="${compile.source}"
+           target="${compile.target}"
+            includeantruntime="false">
+      <compilerarg value="-Xmaxerrs"/>
+      <compilerarg value="20"/>
+      <classpath>
+        <fileset dir="${basedir}/lib">
+          <include name="*.jar"/>
+          <exclude name="javanlp*"/>
+        </fileset>
+      </classpath>
+      <!-- <compilerarg value="-Xlint"/> -->
+    </javac>
+
+    <!-- Copy application resources -->
+<!--
+    <copy  todir="${build.home}/WEB-INF/classes">
+      <fileset dir="${src.home}" excludes="**/*.java"/>
+    </copy>
+-->
+
+  </target>
+
+
+<!-- ==================== Javadoc Target ================================== -->
+
+<!--
+
+  The "javadoc" target creates Javadoc API documentation for the Java
+  classes included in your application.  Normally, this is only required
+  when preparing a distribution release, but is available as a separate
+  target in case the developer wants to create Javadocs independently.
+
+-->
+
+  <target name="javadoc" depends="compile"
+   description="Create Javadoc API documentation">
+
+    <mkdir          dir="${javadoc.home}"/>
+    <javadoc sourcepath="${src.home}"
+                destdir="${javadoc.home}"
+              maxmemory="1g"
+                 author="true"
+                 source="${compile.source}"
+                overview="${src.home}/edu/stanford/nlp/overview.html"
+                doctitle="Stanford JavaNLP API Documentation"
+             windowtitle="Stanford JavaNLP API"
+             encoding="${compile.encoding}"
+	     docencoding="${compile.encoding}"
+	     charset="${compile.encoding}"
+            packagenames="*">
+      <!-- Allow @generated, @modifiable and @ordered tags -->
+      <tag name="generated" scope="all" description="Generated" />
+      <tag name="modifiable" scope="all" description="Modifiable" />
+      <tag name="ordered" scope="all" description="Ordered" />
+      <!-- Depends on lib and classes folders -->
+      <classpath>
+        <fileset dir="${basedir}/lib">
+          <include name="*.jar"/>
+          <exclude name="javanlp*"/>
+        </fileset>
+        <pathelement path="${build.home}" />
+      </classpath>
+      <bottom><![CDATA[<font size="2"><a href="https://nlp.stanford.edu" target="_top">Stanford NLP Group</a></font>]]></bottom>
+      <link href="https://docs.oracle.com/javase/8/docs/api/"/>
+    </javadoc>
+
+  </target>
+
+
+<!-- ==================== Prepare Target ================================== -->
+
+<!--
+
+  The "prepare" target is used to create the "build" destination directory,
+  and copy the static contents of your web application to it.  If you need
+  to copy static files from external dependencies, you can customize the
+  contents of this task.
+
+  Normally, this task is executed indirectly when needed.
+
+-->
+
+  <target name="prepare">
+
+    <!-- Create build directories as needed -->
+    <mkdir  dir="${build.home}"/>
+
+  </target>
+
+</project>

stanford_model/ner-gui.bat

@@ -0,0 +1 @@
+java -mx1500m -cp "stanford-ner.jar;lib/*" edu.stanford.nlp.ie.crf.NERGUI

stanford_model/ner-gui.command

@@ -0,0 +1,2 @@
+#!/bin/sh
+java -mx500m -cp `dirname $0`/stanford-ner.jar:`dirname $0`/lib/* edu.stanford.nlp.ie.crf.NERGUI

stanford_model/ner-gui.sh

@@ -0,0 +1,4 @@
+#!/bin/sh
+scriptdir=`dirname $0`
+
+java -mx700m -cp "$scriptdir/stanford-ner.jar:lib/*" edu.stanford.nlp.ie.crf.NERGUI

stanford_model/ner.bat

@@ -0,0 +1 @@
+java -mx1000m -cp stanford-ner.jar;lib/* edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers\english.all.3class.distsim.crf.ser.gz -textFile %1

stanford_model/ner.sh

@@ -0,0 +1,4 @@
+#!/bin/sh
+scriptdir=`dirname $0`
+
+java -mx700m -cp "$scriptdir/stanford-ner.jar:$scriptdir/lib/*" edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier $scriptdir/classifiers/english.all.3class.distsim.crf.ser.gz -textFile $1

stanford_model/sample-conll-file.txt

@@ -0,0 +1,9 @@
+John	PERSON
+Kerry	PERSON
+will	O
+fly	O
+to	O
+Paris	LOCATION
+this	O
+weekend	O
+.	O

stanford_model/sample-w-time.txt

@@ -0,0 +1,2 @@
+Barack Obama was born on August 4, 1961 in Honolulu, Hawaii which was 4 days
+ago.

stanford_model/sample.ner.txt

@@ -0,0 +1,2 @@
+The/O fate/O of/O Lehman/ORGANIZATION Brothers/ORGANIZATION ,/O the/O beleaguered/O investment/O bank/O ,/O hung/O in/O the/O balance/O on/O Sunday/O as/O Federal/ORGANIZATION Reserve/ORGANIZATION officials/O and/O the/O leaders/O of/O major/O financial/O institutions/O continued/O to/O gather/O in/O emergency/O meetings/O trying/O to/O complete/O a/O plan/O to/O rescue/O the/O stricken/O bank/O ./O 
+Several/O possible/O plans/O emerged/O from/O the/O talks/O ,/O held/O at/O the/O Federal/ORGANIZATION Reserve/ORGANIZATION Bank/ORGANIZATION of/ORGANIZATION New/ORGANIZATION York/ORGANIZATION and/O led/O by/O Timothy/PERSON R./PERSON Geithner/PERSON ,/O the/O president/O of/O the/O New/ORGANIZATION York/ORGANIZATION Fed/ORGANIZATION ,/O and/O Treasury/ORGANIZATION Secretary/O Henry/PERSON M./PERSON Paulson/PERSON Jr./PERSON ./O 

stanford_model/sample.txt

@@ -0,0 +1 @@
+The fate of Lehman Brothers, the beleaguered investment bank, hung in the balance on Sunday as Federal Reserve officials and the leaders of major financial institutions continued to gather in emergency meetings trying to complete a plan to rescue the stricken bank.  Several possible plans emerged from the talks, held at the Federal Reserve Bank of New York and led by Timothy R. Geithner, the president of the New York Fed, and Treasury Secretary Henry M. Paulson Jr.

stanford_model/stanford-ner-4.2.0-javadoc.jar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2400a6d94ec30c8aec36003e5a4d35d7f95651955220d608841566b123491bbf
+size 5887473

stanford_model/stanford-ner-4.2.0-sources.jar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67107462c51833c3073784e70d504fd1b5f5153c81eddc60ec106ae872d95b6c
+size 3489930

stanford_model/stanford-ner-4.2.0.jar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2478a64f37750c52c5aca933b4e29fa399b464ab80817946fa8f4f30c92ea60
+size 4661456

stanford_model/stanford-ner.jar

@@ -0,0 +1,3 @@
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+oid sha256:b2478a64f37750c52c5aca933b4e29fa399b464ab80817946fa8f4f30c92ea60
+size 4661456