WenmuZhou / unilm

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

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Hiring

We are hiring at all levels (including FTE researchers and interns)! If you are interested in working with us on NLP and large-scale pre-trained models, please send your resume to fuwei@microsoft.com.

Pre-trained Models

Large-scale self-supervised pre-training across tasks (predictive and generative), languages (100+ languages), and modalities (language, image, audio, layout/format + language, vision + language, audio + language, etc.)

Language & Multilingual

UniLM: unified pre-training for language understanding and generation

InfoXLM/XLM-E: multilingual/cross-lingual pre-trained models for 100+ languages

DeltaLM/mT6: encoder-decoder pre-training for language generation and translation for 100+ languages

MiniLM: small and fast pre-trained models for language understanding and generation

AdaLM: domain, language, and task adaptation of pre-trained models

Vision

BEiT (NEW): generative self-supervised pre-training for image / BERT Pre-Training of Image Transformers

Speech

WavLM (NEW): speech pre-training for full stack tasks

Multimodal (X + Language)

LayoutLM: multimodal (text + layout/format + image) pre-training for Document AI (e.g. scanned documents, PDF, etc.)

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

MarkupLM (NEW): markup language model pre-training for visually-rich document understanding

UniSpeech: unified pre-training for self-supervised learning and supervised learning for ASR

UniSpeech-SAT: universal speech representation learning with speaker-aware pre-training

SpeechT5 (NEW): encoder-decoder pre-training for spoken language processing

VLMo (NEW): Unified vision-language pre-training - evolution of BEiT to multimodal

Toolkits

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

Applications

TrOCR (NEW): transformer-based OCR w/ pre-trained models

LayoutReader: pre-training of text and layout for reading order detection

XLM-T: multilingual NMT w/ pretrained cross-lingual encoders

News

  • [Model Release] December 16th, 2021: TrOCR small models for handwritten and printed texts.
  • November 24th, 2021: VLMo as the new SOTA on the VQA Challenge
  • November, 2021: Multilingual translation at scale: 10000 language pairs and beyond
  • [Model Release] November, 2021: MarkupLM
  • [Model Release] November, 2021: VLMo - Unified vision-language pre-training w/ BEiT
  • [Model Release] October, 2021: WavLM - Large-scale self-supervised pre-trained models for speech.
  • [Model Release] October 2021: TrOCR is on HuggingFace
  • September 28th, 2021: T-ULRv5 (aka XLM-E/InfoXLM) as the SOTA on the XTREME leaderboard. // Blog
  • [Model Release] September, 2021: LayoutLM-cased are on HuggingFace
  • [Model Release] September, 2021: TrOCR - Transformer-based OCR w/ pre-trained BEiT and RoBERTa models.
  • 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 October, 2021: WavLM release *****

  • WavLM (October 27, 2021): WavLM, a new pre-trained speech model, to solve full-stack downstream speech tasks. WavLM integrates the gated relative position embedding structure and the utterance mixing method, to model both spoken content and speaker identity preservation. WavLM is trained on 94k hours of public audio data, which is larger than other released checkpoints for English Speech modeling. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. "WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing"

***** New October, 2021: MarkupLM release *****

  • MarkupLM (October 19, 2021): MarkupLM, a simple yet effective pre-training approach for text and markup language. With the Transformer architecture, MarkupLM integrates different input embeddings including text embeddings, position embeddings, and XPath embeddings. Furthermore, we also propose new pre-training objectives that are specially designed for understanding the markup language. We evaluate the pre-trained MarkupLM model on the WebSRC and SWDE datasets. Experiments show that MarkupLM significantly outperforms several SOTA baselines in these tasks. "MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding"

***** September, 2021: TrOCR release *****

  • TrOCR (September 22, 2021): Transformer-based OCR with pre-trained models, which leverages the Transformer architecture for both image understanding and bpe-level text generation. The TrOCR model is simple but effective (convolution free), and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. "TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models"

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

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

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

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

***** 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 the pre-trained models, please submit a GitHub issue.

For other communications, please contact Furu Wei (fuwei@microsoft.com).

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

License:MIT License


Languages

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