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Introduced the Word2VecSynonymFilter (#12169)
Co-authored-by: Alessandro Benedetti <[email protected]>
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126 changes: 126 additions & 0 deletions
126
...analysis/common/src/java/org/apache/lucene/analysis/synonym/word2vec/Dl4jModelReader.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * 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. | ||
| */ | ||
|
|
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| package org.apache.lucene.analysis.synonym.word2vec; | ||
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| import java.io.BufferedInputStream; | ||
| import java.io.BufferedReader; | ||
| import java.io.Closeable; | ||
| import java.io.IOException; | ||
| import java.io.InputStream; | ||
| import java.io.InputStreamReader; | ||
| import java.nio.charset.StandardCharsets; | ||
| import java.util.Base64; | ||
| import java.util.Locale; | ||
| import java.util.zip.ZipEntry; | ||
| import java.util.zip.ZipInputStream; | ||
| import org.apache.lucene.util.BytesRef; | ||
| import org.apache.lucene.util.TermAndVector; | ||
|
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| /** | ||
| * Dl4jModelReader reads the file generated by the library Deeplearning4j and provide a | ||
| * Word2VecModel with normalized vectors | ||
| * | ||
| * <p>Dl4j Word2Vec documentation: | ||
| * https://deeplearning4j.konduit.ai/v/en-1.0.0-beta7/language-processing/word2vec Example to | ||
| * generate a model using dl4j: | ||
| * https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/modelling/embeddingsfromcorpus/word2vec/Word2VecRawTextExample.java | ||
| * | ||
| * @lucene.experimental | ||
| */ | ||
| public class Dl4jModelReader implements Closeable { | ||
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| private static final String MODEL_FILE_NAME_PREFIX = "syn0"; | ||
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| private final ZipInputStream word2VecModelZipFile; | ||
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| public Dl4jModelReader(InputStream stream) { | ||
| this.word2VecModelZipFile = new ZipInputStream(new BufferedInputStream(stream)); | ||
| } | ||
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| public Word2VecModel read() throws IOException { | ||
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| ZipEntry entry; | ||
| while ((entry = word2VecModelZipFile.getNextEntry()) != null) { | ||
| String fileName = entry.getName(); | ||
| if (fileName.startsWith(MODEL_FILE_NAME_PREFIX)) { | ||
| BufferedReader reader = | ||
| new BufferedReader(new InputStreamReader(word2VecModelZipFile, StandardCharsets.UTF_8)); | ||
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| String header = reader.readLine(); | ||
| String[] headerValues = header.split(" "); | ||
| int dictionarySize = Integer.parseInt(headerValues[0]); | ||
| int vectorDimension = Integer.parseInt(headerValues[1]); | ||
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| Word2VecModel model = new Word2VecModel(dictionarySize, vectorDimension); | ||
| String line = reader.readLine(); | ||
| boolean isTermB64Encoded = false; | ||
| if (line != null) { | ||
| String[] tokens = line.split(" "); | ||
| isTermB64Encoded = | ||
| tokens[0].substring(0, 3).toLowerCase(Locale.ROOT).compareTo("b64") == 0; | ||
| model.addTermAndVector(extractTermAndVector(tokens, vectorDimension, isTermB64Encoded)); | ||
| } | ||
| while ((line = reader.readLine()) != null) { | ||
| String[] tokens = line.split(" "); | ||
| model.addTermAndVector(extractTermAndVector(tokens, vectorDimension, isTermB64Encoded)); | ||
| } | ||
| return model; | ||
| } | ||
| } | ||
| throw new IllegalArgumentException( | ||
| "Cannot read Dl4j word2vec model - '" | ||
| + MODEL_FILE_NAME_PREFIX | ||
| + "' file is missing in the zip. '" | ||
| + MODEL_FILE_NAME_PREFIX | ||
| + "' is a mandatory file containing the mapping between terms and vectors generated by the DL4j library."); | ||
| } | ||
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| private static TermAndVector extractTermAndVector( | ||
| String[] tokens, int vectorDimension, boolean isTermB64Encoded) { | ||
| BytesRef term = isTermB64Encoded ? decodeB64Term(tokens[0]) : new BytesRef((tokens[0])); | ||
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| float[] vector = new float[tokens.length - 1]; | ||
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| if (vectorDimension != vector.length) { | ||
| throw new RuntimeException( | ||
| String.format( | ||
| Locale.ROOT, | ||
| "Word2Vec model file corrupted. " | ||
| + "Declared vectors of size %d but found vector of size %d for word %s (%s)", | ||
| vectorDimension, | ||
| vector.length, | ||
| tokens[0], | ||
| term.utf8ToString())); | ||
| } | ||
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| for (int i = 1; i < tokens.length; i++) { | ||
| vector[i - 1] = Float.parseFloat(tokens[i]); | ||
| } | ||
| return new TermAndVector(term, vector); | ||
| } | ||
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| static BytesRef decodeB64Term(String term) { | ||
| byte[] buffer = Base64.getDecoder().decode(term.substring(4)); | ||
| return new BytesRef(buffer, 0, buffer.length); | ||
| } | ||
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| @Override | ||
| public void close() throws IOException { | ||
| word2VecModelZipFile.close(); | ||
| } | ||
| } |
95 changes: 95 additions & 0 deletions
95
...e/analysis/common/src/java/org/apache/lucene/analysis/synonym/word2vec/Word2VecModel.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,95 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * 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. | ||
| */ | ||
|
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| package org.apache.lucene.analysis.synonym.word2vec; | ||
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| import java.io.IOException; | ||
| import org.apache.lucene.util.BytesRef; | ||
| import org.apache.lucene.util.BytesRefHash; | ||
| import org.apache.lucene.util.TermAndVector; | ||
| import org.apache.lucene.util.hnsw.RandomAccessVectorValues; | ||
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| /** | ||
| * Word2VecModel is a class representing the parsed Word2Vec model containing the vectors for each | ||
| * word in dictionary | ||
| * | ||
| * @lucene.experimental | ||
| */ | ||
| public class Word2VecModel implements RandomAccessVectorValues<float[]> { | ||
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| private final int dictionarySize; | ||
| private final int vectorDimension; | ||
| private final TermAndVector[] termsAndVectors; | ||
| private final BytesRefHash word2Vec; | ||
| private int loadedCount = 0; | ||
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| public Word2VecModel(int dictionarySize, int vectorDimension) { | ||
| this.dictionarySize = dictionarySize; | ||
| this.vectorDimension = vectorDimension; | ||
| this.termsAndVectors = new TermAndVector[dictionarySize]; | ||
| this.word2Vec = new BytesRefHash(); | ||
| } | ||
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| private Word2VecModel( | ||
| int dictionarySize, | ||
| int vectorDimension, | ||
| TermAndVector[] termsAndVectors, | ||
| BytesRefHash word2Vec) { | ||
| this.dictionarySize = dictionarySize; | ||
| this.vectorDimension = vectorDimension; | ||
| this.termsAndVectors = termsAndVectors; | ||
| this.word2Vec = word2Vec; | ||
| } | ||
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| public void addTermAndVector(TermAndVector modelEntry) { | ||
| modelEntry.normalizeVector(); | ||
| this.termsAndVectors[loadedCount++] = modelEntry; | ||
| this.word2Vec.add(modelEntry.getTerm()); | ||
| } | ||
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| @Override | ||
| public float[] vectorValue(int targetOrd) { | ||
| return termsAndVectors[targetOrd].getVector(); | ||
| } | ||
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| public float[] vectorValue(BytesRef term) { | ||
| int termOrd = this.word2Vec.find(term); | ||
| if (termOrd < 0) return null; | ||
| TermAndVector entry = this.termsAndVectors[termOrd]; | ||
| return (entry == null) ? null : entry.getVector(); | ||
| } | ||
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| public BytesRef termValue(int targetOrd) { | ||
| return termsAndVectors[targetOrd].getTerm(); | ||
| } | ||
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| @Override | ||
| public int dimension() { | ||
| return vectorDimension; | ||
| } | ||
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| @Override | ||
| public int size() { | ||
| return dictionarySize; | ||
| } | ||
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| @Override | ||
| public RandomAccessVectorValues<float[]> copy() throws IOException { | ||
| return new Word2VecModel( | ||
| this.dictionarySize, this.vectorDimension, this.termsAndVectors, this.word2Vec); | ||
| } | ||
| } |
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