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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.deeplearning4j.text.documentiterator;
import lombok.Data;
import lombok.ToString;
import org.deeplearning4j.models.word2vec.VocabWord;
import java.util.ArrayList;
import java.util.List;
@Data
@ToString(exclude = "referencedContent")
public class LabelledDocument {
// optional field
private String id;
// initial text representation of current document
private String content;
private List labels = new ArrayList<>();
/*
as soon as sentence was parsed for vocabulary words, there's no need to hold string representation, and we can just stick to references to those VocabularyWords
*/
private List referencedContent;
/**
* This method returns first label for the document.
*
* PLEASE NOTE: This method is here only for backward compatibility purposes, getLabels() should be used instead.
*
* @return
*/
@Deprecated
public String getLabel() {
return labels.get(0);
}
@Deprecated
public void setLabel(String label) {
if (!labels.isEmpty())
labels.set(0, label);
else
labels.add(label);
}
public void addLabel(String label) {
labels.add(label);
}
}
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