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        "description": "# tfembedhub\n\nConvert embeddings vectors (.txt format) into TensorFlowHub embedding lookup module.\n\n## How to\n\n1. Save lookup keys and embeddings values in text file.\n\nKeys should be in first column. Other columns treated as embedding values. Any space-like characters allowed as columns separator.\n\nNon-existing keys will refer to \"<UNQ>\" key embeddings. You may provide embedding values for that, otherwise it will be initialized with zeros.\n\n```\nkey1 1. 2. 3.\nkey2 4. 5. 6.\n<UNQ> 0. -1. 0.\n```\n\n2. Sonvert saved embeddings into TF Hub Module with \"tfembedhub-convert\" command.\n```bash\ntfembedhub-convert vectors.txt vectors-hub/\n```\n\n3. Use embedding hub via columns in your estimator.\n```python\nfrom tfembedhub text_embedding_column, sequence_text_embedding_column\n\nmy_words_embedding = sequence_text_embedding_column(\n    key='sparse_key_from_features',\n    module_spec='path/to/my/hub'\n)\n\n# Then pass my_words_embedding to estimator \"columns\" list.\n```",
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