torch-multilabel-embedding

The package contains a TensorFlow2/Keras class to train an Embedding matrix for multi-label inputs, i.e. instead of 1 ID per token (one hot encoding), N IDs per token can be provided as model input.

An TensorFlow2/Keras implementation can be found here: https://github.com/ulf1/keras-multilabel-embedding (pip install keras-multilabel-embedding)

Usage

import torch_multilabel_embedding as tml
import torch

# a sequence of multi-label data points
x_ids = [[1, 2, 4], [0, 1, 2], [2, 1, 4], [3, 2, 1]]
x_ids = torch.tensor(x_ids)

# initialize layer
layer = tml.MultiLabelEmbedding(
    vocab_size=5, embed_size=300, random_state=42)

# predict
y = layer(x_ids)

Appendix

Installation

The torch-multilabel-embedding git repo is available as PyPi package

pip install torch-multilabel-embedding
pip install git+ssh://git@github.com/ulf1/torch-multilabel-embedding.git

Install a virtual environment

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir
pip install -r requirements-dev.txt --no-cache-dir
pip install -r requirements-demo.txt --no-cache-dir

(If your git repo is stored in a folder with whitespaces, then don’t use the subfolder .venv. Use an absolute path without whitespaces.)

Python commands

Jupyter for the examples: jupyter lab
Check syntax: flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
Run Unit Tests: PYTHONPATH=. pytest

Publish

pandoc README.md --from markdown --to rst -s -o README.rst
python setup.py sdist
twine upload -r pypi dist/*

Clean up

find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.

Metadata

Release files for torch-multilabel-embedding 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for torch-multilabel-embedding 0.1.1
File	Size	Uploaded
torch-multilabel-embedding-0.1.1.tar.gz	7.7 kB	Mar 1, 2022	Details

Release files / torch-multilabel-embedding-0.1.1.tar.gz

Download URL	torch-multilabel-embedding-0.1.1.tar.gz
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SHA-256 checksum How to use checksums	`3c7c4b7e4786582aed136bbd303daf512b408ee454af17aa402f4225fbd7c8ce`
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Uploaded via	`twine/3.3.0 pkginfo/1.8.2 requests/2.27.1 setuptools/60.9.3 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.9`