Tutorials on training the Skip-thoughts vectors for features extraction of sentence.

Tutorials on training the Skip-thoughts vectors for features extraction of sentence. 

1. Send emails and download the training dataset. 

　　the dataset used in skip_thoughts vectors is from [BookCorpus]: http://yknzhu.wixsite.com/mbweb

　　first, you should send a email to the auther of this paper and ask for the link of this dataset. Then you will download the following files:

　　

　　unzip these files in the current folders.

2. Open and download the tensorflow version code. 　　

　　Do as the following links: https://github.com/tensorflow/models/tree/master/research/skip_thoughts

　　Then, you will see the processing as follows:

　　　　　　　　　

　　[Attention]  when you install the bazel, you need to install this software, but do not update it. Or, it may shown you some errors in the following operations.

3. Install the packages needed. 

• Bazel (instructions)
• TensorFlow (instructions)
• NumPy (instructions)
• scikit-learn (instructions)
• Natural Language Toolkit (NLTK)
  • First install NLTK (instructions)
  • Then install the NLTK data (instructions)
• gensim (instructions)
  • Only required if you will be expanding your vocabulary with the word2vec model.

4. Encoding Sentences :

　　run the following py files.

　　

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import numpy as np
 import os.path
 import scipy.spatial.distance as sd
 from skip_thoughts import configuration
 from skip_thoughts import encoder_manager
 import pdb 

 print("==>> Skip-Thought Vector ")

 # Set paths to the model.
 VOCAB_FILE = "./skip_thoughts/pretrained/skip_thoughts_bi_2017_02_16/vocab.txt"
 EMBEDDING_MATRIX_FILE = "./skip_thoughts/pretrained/skip_thoughts_bi_2017_02_16/embeddings.npy"
 CHECKPOINT_PATH = "./skip_thoughts/model/train/model.ckpt-78581"
 # The following directory should contain files rt-polarity.neg and rt-polarity.pos.

 # Set up the encoder. Here we are using a single unidirectional model.
 # To use a bidirectional model as well, call load_model() again with
 # configuration.model_config(bidirectional_encoder=True) and paths to the
 # bidirectional model's files. The encoder will use the concatenation of
 # all loaded models.

 print("==>> loading the pre-trained models ... ")

 encoder = encoder_manager.EncoderManager()
 encoder.load_model(configuration.model_config(),
                    vocabulary_file=VOCAB_FILE,
                    embedding_matrix_file=EMBEDDING_MATRIX_FILE,
                    checkpoint_path=CHECKPOINT_PATH)

 print("==>> Done !") 

 # Load the movie review dataset.
 data = [' This is my second attempt  to the tensorflow version skip_thought_vectors ... ']

 print("==>> the given sentence is: ", data) 

 # Generate Skip-Thought Vectors for each sentence in the dataset.
 encodings = encoder.encode(data)

 print("==>> the sentence feature is: ", encodings)    ## the output feature is 2400 dimension. 

wangxiao@AHU:/media/wangxiao/49cd8079-e619-4e4b-89b1-15c86afb5102/skip_thought_vector_onlineModels$ python run_sentence_feature_extraction.py
RuntimeError: module compiled against API version 0xc but this version of numpy is 0xb
RuntimeError: module compiled against API version 0xc but this version of numpy is 0xb
==>> Skip-Thought Vector
==>> loading the pre-trained models ...
WARNING:tensorflow:From ./skip_thoughts/skip_thoughts_model.py:360: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.create_global_step
2018-05-13 21:36:27.670186: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
==>> Done !
==>> the given sentence is: [' This is my second attempt to the tensorflow version skip_thought_vectors ... ']
==>> the sentence feature is: [[-0.00676637 0.01928637 -0.01759908 ..., 0.00851333 0.00875245 -0.0040213 ]]
> ./run_sentence_feature_extraction.py(48)<module>()
-> print("==>> the encodings[0] is: ", encodings[0])
(Pdb) x = encodings[0]
(Pdb) x.size
2400
(Pdb)

as we can see from above terminal, the output feature vector is 2400-D. 　　

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