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Large-scale Self-supervised Pre-training across Tasks, Languages, and Modalities


Pre-trained (foundation) models across tasks (understanding, generation and translation), languages (100+ languages), and modalities (language, image, audio, vision + language, audio + language, etc.)

The family of UniLM AI:

UniLM ([email protected]'19 | [email protected]'20 | [email protected]'21): unified pre-training for language understanding and generation

InfoXLM ([email protected]'21 | [email protected]'21): multilingual/cross-lingual pre-trained models for 100+ languages

DeltaLM (NEW): encoder-decoder pre-training for language generation and translation for 100+ languages

MiniLM ([email protected]'20 | [email protected]'21): small and fast pre-trained models for language understanding and generation

AdaLM ([email protected]'21): domain, language, and task adaptation of pre-trained models

LayoutLM ([email protected]'20 | [email protected]'21): multimodal (text + layout/format + image) pre-training for Document AI (e.g. scanned documents, PDF, etc.)

LayoutXLM (NEW): multimodal (text + layout/format + image) pre-training for multilingual document understanding

LayoutReader (EMNLP'21): Pre-training of text and layout for reading order detection

BEiT (NEW): BERT Pre-Training of Image Transformers

UniSpeech ([email protected]'21): Speech Pre-Training for ASR and TTS

s2s-ft: sequence-to-sequence fine-tuning toolkit

XLM-T (NEW): Multilingual NMT w/ pretrained cross-lingual encoders

News

  • August 2021: LayoutLMv2 and LayoutXLM are on HuggingFace
  • [Model Release] August, 2021: LayoutReader – Built with LayoutLM to improve general reading order detection.
  • [Model Release] August, 2021: DeltaLM – Encoder-decoder pre-training for language generation and translation.
  • August 2021: BEiT is on HuggingFace
  • [Model Release] July, 2021: BEiT – Towards BERT moment for CV
  • [Model Release] June, 2021: LayoutLMv2, LayoutXLM, MiniLMv2, and AdaLM.
  • May, 2021: LayoutLMv2, InfoXLMv2, MiniLMv2, UniLMv3, and AdaLM were accepted by ACL 2021.
  • April, 2021: LayoutXLM is coming by extending the LayoutLM into multilingual support! A multilingual form understanding benchmark XFUND is also introduced, which includes forms with human labeled key-value pairs in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese).
  • March, 2021: InfoXLM was accepted by NAACL 2021.
  • December 29th, 2020: LayoutLMv2 is coming with the new SOTA on a wide varierty of document AI tasks, including DocVQA and SROIE leaderboard.
  • October 8th, 2020: T-ULRv2 (aka InfoXLM) as the SOTA on the XTREME leaderboard. // Blog
  • September, 2020: MiniLM was accepted by NeurIPS 2020.
  • July 16, 2020: InfoXLM (Multilingual UniLM) arXiv
  • June, 2020: UniLMv2 was accepted by ICML 2020; LayoutLM was accepted by KDD 2020.
  • April 5, 2020: Multilingual MiniLM released!
  • September, 2019: UniLMv1 was accepted by NeurIPS 2019.

Release

***** New August, 2021: LayoutReader release *****

***** New August, 2021: DeltaLM release *****

***** New July, 2021: BEiT release *****

***** New June, 2021: LayoutXLM | AdaLM | MiniLMv2 release *****

***** New May, 2021: LayoutLMv2 | LayoutXLM release *****

  • LayoutLM 2.0 (December 29, 2020): multimodal pre-training for visually-rich document understanding by leveraging text, layout and image information in a single framework. It is coming with new SOTA on a wide range of document understanding tasks, including FUNSD (0.7895 -> 0.8420), CORD (0.9493 -> 0.9601), SROIE (0.9524 -> 0.9781), Kleister-NDA (0.834 -> 0.852), RVL-CDIP (0.9443 -> 0.9564), and DocVQA (0.7295 -> 0.8672). “LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding ACL 2021

***** February, 2020: UniLM v2 | MiniLM v1 | LayoutLM v1 | s2s-ft v1 release *****

***** October 1st, 2019: UniLM v1 release *****

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree. Portions of the source code are based on the transformers project.

Microsoft Open Source Code of Conduct

Contact Information

For help or issues using UniLM AI models, please submit a GitHub issue.

For other communications related to UniLM AI, please contact Li Dong ([email protected]), Furu Wei ([email protected]).

GitHub

https://github.com/microsoft/unilm


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