https://github.com/amansrivastava17/lstm-siamese-text-similarity
⚛️ It is keras based implementation of siamese architecture using lstm encoders to compute text similarity
https://github.com/amansrivastava17/lstm-siamese-text-similarity
bidirectional-lstm deep-learning keras lstm lstm-neural-networks sentence-similarity siamese-network text-similarity
Last synced: about 2 months ago
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⚛️ It is keras based implementation of siamese architecture using lstm encoders to compute text similarity
- Host: GitHub
- URL: https://github.com/amansrivastava17/lstm-siamese-text-similarity
- Owner: amansrivastava17
- License: mit
- Created: 2018-03-23T11:36:08.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T23:51:41.000Z (over 2 years ago)
- Last Synced: 2025-08-12T07:51:23.597Z (about 2 months ago)
- Topics: bidirectional-lstm, deep-learning, keras, lstm, lstm-neural-networks, sentence-similarity, siamese-network, text-similarity
- Language: Python
- Homepage:
- Size: 69.3 KB
- Stars: 286
- Watchers: 8
- Forks: 89
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Text Similarity Using Siamese Deep Neural Network
Siamese neural network is a class of **neural network architectures that contain two or more** **identical** **subnetworks**. *identical* here means they have the same configuration with the same parameters
and weights. Parameter updating is mirrored across both subnetworks.
It is a keras based implementation of deep siamese Bidirectional LSTM network to capture phrase/sentence similarity using word embeddings.
Below is the architecture description for the same.
#### Install dependencies
`pip install -r requirements.txt`
### Usage
#### Training
```python
from model import SiameseBiLSTM
from inputHandler import word_embed_meta_data, create_test_data
from config import siamese_config
import pandas as pd
############ Data Preperation ##########
df = pd.read_csv('sample_data.csv')
sentences1 = list(df['sentences1'])
sentences2 = list(df['sentences2'])
is_similar = list(df['is_similar'])
del df
######## Word Embedding ############
tokenizer, embedding_matrix = word_embed_meta_data(sentences1 + sentences2, siamese_config['EMBEDDING_DIM'])
embedding_meta_data = {
'tokenizer': tokenizer,
'embedding_matrix': embedding_matrix
}
## creating sentence pairs
sentences_pair = [(x1, x2) for x1, x2 in zip(sentences1, sentences2)]
del sentences1
del sentences2
######## Training ########
class Configuration(object):
"""Dump stuff here"""
CONFIG = Configuration()
CONFIG.embedding_dim = siamese_config['EMBEDDING_DIM']
CONFIG.max_sequence_length = siamese_config['MAX_SEQUENCE_LENGTH']
CONFIG.number_lstm_units = siamese_config['NUMBER_LSTM']
CONFIG.rate_drop_lstm = siamese_config['RATE_DROP_LSTM']
CONFIG.number_dense_units = siamese_config['NUMBER_DENSE_UNITS']
CONFIG.activation_function = siamese_config['ACTIVATION_FUNCTION']
CONFIG.rate_drop_dense = siamese_config['RATE_DROP_DENSE']
CONFIG.validation_split_ratio = siamese_config['VALIDATION_SPLIT']
siamese = SiameseBiLSTM(CONFIG.embedding_dim , CONFIG.max_sequence_length, CONFIG.number_lstm_units , CONFIG.number_dense_units, CONFIG.rate_drop_lstm, CONFIG.rate_drop_dense, CONFIG.activation_function, CONFIG.validation_split_ratio)
best_model_path = siamese.train_model(sentences_pair, is_similar, embedding_meta_data, model_save_directory='./')
```
#### Testing
```python
from operator import itemgetter
from keras.models import load_model
model = load_model(best_model_path)
test_sentence_pairs = [('What can make Physics easy to learn?','How can you make physics easy to learn?'),('How many times a day do a clocks hands overlap?','What does it mean that every time I look at the clock the numbers are the same?')]
test_data_x1, test_data_x2, leaks_test = create_test_data(tokenizer,test_sentence_pairs, siamese_config['MAX_SEQUENCE_LENGTH'])
preds = list(model.predict([test_data_x1, test_data_x2, leaks_test], verbose=1).ravel())
results = [(x, y, z) for (x, y), z in zip(test_sentence_pairs, preds)]
results.sort(key=itemgetter(2), reverse=True)
print results
```
### References:
1. [Siamese Recurrent Architectures for Learning Sentence Similarity (2016)](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12195)
2. Inspired from Tensorflow Implementation of https://github.com/dhwajraj/deep-siamese-text-similarity