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Word2vec basic.py: Difference between revisions

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(word2vec_basic_algolit.py)
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* Algolit step 1: read data from plain text file
 
* Algolit step 1: read data from plain text file
 
* Step 2: Create a dictionary and replace rare words with UNK token.
 
* Step 2: Create a dictionary and replace rare words with UNK token.
 +
** Algolit extension: write the dictionary to dictionary.txt
 
* Step 3: Function to generate a training batch for the skip-gram model.
 
* Step 3: Function to generate a training batch for the skip-gram model.
 
* Step 4: Build and train a skip-gram model.
 
* Step 4: Build and train a skip-gram model.
 +
** Algolit extension: select your own set of test words, using the dictionary.txt
 
* Step 5: Begin training.
 
* Step 5: Begin training.
** Algolit extension: write training log to a text document
+
** Algolit extension: write training log to logfile.txt
 
* Step 6: Visualize the embeddings.
 
* Step 6: Visualize the embeddings.
 
  
 
==Source==
 
==Source==

Revision as of 16:27, 3 October 2017

Graph generated by the word2vec_basic.py example script, trained on the book "Mankind in the Making" by H.G. Wells.

This is an annotated version of the basic word2vec script. The code is based on the Word2Vec tutorial provided by Tensorflow: https://www.tensorflow.org/tutorials/word2vec.

History

Word2vec is a neural network

word2vec_basic_algolit.py

The structure of the annotated word2vec script is the following:

  • Step 1: Download data. (optional)
  • Algolit step 1: read data from plain text file
  • Step 2: Create a dictionary and replace rare words with UNK token.
    • Algolit extension: write the dictionary to dictionary.txt
  • Step 3: Function to generate a training batch for the skip-gram model.
  • Step 4: Build and train a skip-gram model.
    • Algolit extension: select your own set of test words, using the dictionary.txt
  • Step 5: Begin training.
    • Algolit extension: write training log to logfile.txt
  • Step 6: Visualize the embeddings.

Source

The script provides an option to download a dataset from

Dictionary

A snippet from the dictionary.txt file:

0: 'UNK', 1: 'the', 2: 'of', 3: 'and', 4: 'to', 5: 'a', 6: 'in', 7: 'is', 8: 'that', 9: 'it', 10: 'be', 11: 'for', 12: 'as', 13: 'are', 14: 'with', 15: 'not', 16: 'this', 17: 'or', 18: 'will', 19: 'at', 20: 'we', 21: 'but', 22: 'by', 23: 'may', 24: 'his', 25: 'all', 26: 'an', 27: 'these', 28: 'they', 29: 'have', 30: 'he', 31: 'from', 32: 'our', 33: 'has', 34: 'The', 35: 'no', 36: 'more', 37: 'which', 38: 'one', 39: 'there', 40: 'would', 41: 'its', 42: 'so', 43: 'their', 44: 'than', 45: 'children', 46: 'very', 47: 'things', 48: 'any', 49: 'upon', 50: 'i', 51: 'can', 52: 'if', 53: 'do', 54: 'who', 55: 'child', 56: 'new', 57: 'life', 58: 'It', 59: 'should', 60: 'them', 61: 'only', 62: 'world', 63: 'must', 64: 'on', 65: 'such', 66: 'great', 67: 'people', 68: 'man', 69: 'into', 70: 'most', 71: 'out', 72: 'little', 73: 'what', 74: 'was', 75: 'every', 76: 'some', 77: 'much', 78: 'certain', 79: 'And', 80: 'about', 81: 'men', 82: 'english', 83: 'far', 84: 'present', 85: 'first', 86: 'many', 87: 'been', 88: 'thing', 89: 'those', 90: 'home', 91: 'good', 92: 'But', 93: 'quite', 94: 'way', 95: 'might', 96: 'other', 97: 'us', 98: 'general', 99: 'They', 100: 'social',

Logs