opennlp.dl.vectors.SentenceVectorsDL Maven / Gradle / Ivy
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* 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.
*/
package opennlp.dl.vectors;
import java.io.File;
import java.io.IOException;
import java.nio.LongBuffer;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import ai.onnxruntime.OnnxTensor;
import ai.onnxruntime.OrtEnvironment;
import ai.onnxruntime.OrtException;
import ai.onnxruntime.OrtSession;
import opennlp.dl.AbstractDL;
import opennlp.dl.Tokens;
import opennlp.tools.tokenize.Tokenizer;
import opennlp.tools.tokenize.WordpieceTokenizer;
/**
* Facilitates the generation of sentence vectors using
* a sentence-transformers model converted to ONNX.
*/
public class SentenceVectorsDL extends AbstractDL {
/**
* Instantiates a {@link SentenceVectorsDL sentence detector} using ONNX models.
*
* @param model The file name of a sentence vectors ONNX model.
* @param vocabulary The file name of the vocabulary file for the model.
*
* @throws OrtException Thrown if the {@code model} cannot be loaded.
* @throws IOException Thrown if errors occurred loading the {@code model} or {@code vocabulary}.
*/
public SentenceVectorsDL(final File model, final File vocabulary)
throws OrtException, IOException {
env = OrtEnvironment.getEnvironment();
session = env.createSession(model.getPath(), new OrtSession.SessionOptions());
vocab = loadVocab(new File(vocabulary.getPath()));
tokenizer = new WordpieceTokenizer(vocab.keySet());
}
/**
* Generates vectors given a sentence.
*
* @param sentence The input sentence.
*
* @throws OrtException Thrown if an error occurs during inference.
*/
public float[] getVectors(final String sentence) throws OrtException {
final Tokens tokens = tokenize(sentence, tokenizer, vocab);
final Map inputs = new HashMap<>();
inputs.put(INPUT_IDS, OnnxTensor.createTensor(env, LongBuffer.wrap(tokens.ids()),
new long[] {1, tokens.ids().length}));
inputs.put(ATTENTION_MASK, OnnxTensor.createTensor(env,
LongBuffer.wrap(tokens.mask()), new long[] {1, tokens.mask().length}));
inputs.put(TOKEN_TYPE_IDS, OnnxTensor.createTensor(env,
LongBuffer.wrap(tokens.types()), new long[] {1, tokens.types().length}));
final float[][][] v = (float[][][]) session.run(inputs).get(0).getValue();
return v[0][0];
}
private Tokens tokenize(final String text, Tokenizer tokenizer, Map vocab) {
final String[] tokens = tokenizer.tokenize(text);
final int[] ids = new int[tokens.length];
final long[] mask = new long[ids.length];
for (int x = 0; x < tokens.length; x++) {
ids[x] = vocab.get(tokens[x]);
}
final long[] lids = Arrays.stream(ids).mapToLong(i -> i).toArray();
final long[] types = new long[ids.length];
Arrays.fill(types, 1);
return new Tokens(tokens, lids, mask, types);
}
}