Skip to main content

Curated transformer models

Project description

🤖 Curated transformers

This Python package provides a curated set of transformer models for spaCy. It is focused on deep integration into spaCy and will support deployment-focused features such as distillation and quantization. Curated transformers currently supports the following model types:

  • ALBERT
  • BERT
  • CamemBERT
  • RoBERTa
  • XLM-RoBERTa

Supporting a wide variety of transformer models is a non-goal. If you want to use another type of model, use spacy-transformers, which allows you to use Hugging Face transformers models with spaCy.

⚠️ Warning: experimental package

This package is experimental and it is possible that the models will still change in incompatible ways.

⏳ Install

pip install curated-transformers

🚀 Quickstart

An example project is provided in the project directory.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

curated-transformers-0.0.8.tar.gz (218.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

curated_transformers-0.0.8-py2.py3-none-any.whl (243.5 kB view details)

Uploaded Python 2Python 3

File details

Details for the file curated-transformers-0.0.8.tar.gz.

File metadata

  • Download URL: curated-transformers-0.0.8.tar.gz
  • Upload date:
  • Size: 218.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.9

File hashes

Hashes for curated-transformers-0.0.8.tar.gz
Algorithm Hash digest
SHA256 26cdc0ca0a097ff8ee0cd816f4c104a2c209a9b9c51bf22217d40fb9dc5d811d
MD5 84423df5dbbab32188254ea259c39c5f
BLAKE2b-256 60231334463950065f98b556af68c20b4916023a3b8ff8e1a138f5140d8e8752

See more details on using hashes here.

File details

Details for the file curated_transformers-0.0.8-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for curated_transformers-0.0.8-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 b732983698ef5446ab015c2c33a857384c15feb41c22c511e08f0acf97942c4f
MD5 65dc0da99b91147bed8c7cb98705acb7
BLAKE2b-256 c5a6da6fa4a4713c2073ca547bad07298af693f5f5dc556950eb35aaf3eecdb8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page