About pytorch-pretrained-bert

Home: https://github.com/huggingface/pytorch-pretrained-BERT

Package license: Apache-2.0

Feedstock license: BSD 3-Clause

Summary: PyTorch version of Google AI BERT model with script to load Google pre-trained models

This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for:

• Google's BERT model,

• OpenAI's GPT model,

• Google/CMU's Transformer-XL model, and

• OpenAI's GPT-2 model. These implementations have been tested on several datasets (see the examples) and should match the performances of the associated TensorFlow implementations (e.g. ~91 F1 on SQuAD for BERT, ~88 F1 on RocStories for OpenAI GPT and ~18.3 perplexity on WikiText 103 for the Transformer-XL).

Current build status


Azure
OSX
Windows

Current release info

Name	Downloads	Version	Platforms

Installing pytorch-pretrained-bert

Installing pytorch-pretrained-bert from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge

Once the conda-forge channel has been enabled, pytorch-pretrained-bert can be installed with:

conda install pytorch-pretrained-bert

It is possible to list all of the versions of pytorch-pretrained-bert available on your platform with:

conda search pytorch-pretrained-bert --channel conda-forge

About conda-forge

conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by CircleCI, AppVeyor and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud channel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenance conda-smithy has been developed. Using the conda-forge.yml within this repository, it is possible to re-render all of this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.

For more information please check the conda-forge documentation.

Terminology

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock. Its primary use is in the construction of the CI .yml files and simplify the management of many feedstocks.

conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)

Updating pytorch-pretrained-bert-feedstock

If you would like to improve the pytorch-pretrained-bert recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel. Note that all branches in the conda-forge/pytorch-pretrained-bert-feedstock are immediately built and any created packages are uploaded, so PRs should be based on branches in forks and branches in the main repository should only be used to build distinct package versions.

In order to produce a uniquely identifiable distribution:

• If the version of a package is not being increased, please add or increase the build/number.

• If the version of a package is being increased, please remember to return the build/number back to 0.

Feedstock Maintainers

• @CurtLH

• @sodre