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        "author": "Daniel Perez",
        "author_email": "tuvistavie@gmail.com",
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            "Programming Language :: Python :: 3"
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        "description": "# bigcode-embeddings\n\nNOTE: data must be generated with [`bigcode-ast-tools`][2] before being able to use\nthis tool\n\n`bigcode-embeddings` allows to generate and visualize embeddings for\nAST nodes.\n\n## Install\n\nThis project should be used with Python 3.\n\nTo install the package either run\n\n```\npip install bigcode-embeddings\n```\n\nor clone the repository and run\n\n```\ncd bigcode-embeddings\npip install -r requirements.txt\npython setup.py install\n```\n\nNOTE: tensorflow needs to be installed separately.\n\n## Usage\n\n### Training embeddings\n\nTraining data can be generated using [`bigcode-ast-tools`][2]\n\nGiven a `data.txt.gz` generated from a vocabulary of size 30000,\n100D embeddings can be trained using\n\n```\n./bin/bigcode-embeddings train -o embeddings/ --vocab-size 30000 --emb-size 100 --l2-value 0.05 --learning-rate 0.01 data.txt.gz\n```\n\n[Tensorboard][2] can be used to visualize the progress\n\n```\ntensorboard --logdir embeddings/\n```\n\nAfter the first epoch, embeddings visualization becomes available from\nTensorboard. The vocabulary TSV file generated by `bigcode-ast-tools` can\nbe loaded to have labels on the embeddings.\n\n### Visualizing the embeddings\n\nTrained embeddings can be visualized using the `visualize` subcommand\nIf the generated vocabulary file is `vocab.tsv`, the above embeddings\ncan be visualized with the following command\n\n```\n./bin/data-explorer visualize clusters -m embeddings/embeddings.bin-STEP -l vocab.tsv\n```\n\nwhere `STEP` should be the largest value found in the `embeddings/` directory.\n\nThe `-i` flag can be passed to generate an interactive plot.\n\n[1]: ../bigcode-ast-tools/README.md\n[2]: https://github.com/tensorflow/tensorboard\n\n\n",
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