Metadata-Version: 2.1
Name: tfembedhub
Version: 1.2.1
Summary: TensorFlow Hub module producer for text embedding lookup
Home-page: https://github.com/shkarupa-alex/tfembedhub
Author: Shkarupa Alex
Author-email: shkarupa.alex@gmail.com
License: MIT
Description: # tfembedhub
        
        Convert embeddings vectors (.txt format) into TensorFlowHub embedding lookup module.
        
        ## How to
        
        1. Save lookup keys and embeddings values in text file.
        
        Keys should be in first column. Other columns treated as embedding values. Any space-like characters allowed as columns separator.
        
        Non-existing keys will refer to "<UNQ>" key embeddings. You may provide embedding values for that, otherwise it will be initialized with zeros.
        
        ```
        key1 1. 2. 3.
        key2 4. 5. 6.
        <UNQ> 0. -1. 0.
        ```
        
        2. Sonvert saved embeddings into TF Hub Module with "tfembedhub-convert" command.
        ```bash
        tfembedhub-convert vectors.txt vectors-hub/
        ```
        
        3. Use embedding hub via columns in your estimator.
        ```python
        from tfembedhub text_embedding_column, sequence_text_embedding_column
        
        my_words_embedding = sequence_text_embedding_column(
            key='sparse_key_from_features',
            module_spec='path/to/my/hub'
        )
        
        # Then pass my_words_embedding to estimator "columns" list.
        ```
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: tf_cpu
Provides-Extra: tf_gpu
