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:
[email protected]'19 | [email protected]'20 | [email protected]'21): unified pre-training for language understanding and generation
[email protected]'21 | [email protected]'21): multilingual/cross-lingual pre-trained models for 100+ languages
NEW): encoder-decoder pre-training for language generation and translation for 100+ languages
[email protected]'20 | [email protected]'21): small and fast pre-trained models for language understanding and generation
[email protected]'21): domain, language, and task adaptation of pre-trained models
NEW): multimodal (text + layout/format + image) pre-training for multilingual document understanding
EMNLP'21): Pre-training of text and layout for reading order detection
NEW): BERT Pre-Training of Image Transformers
[email protected]'21): Speech Pre-Training for ASR and TTS
s2s-ft: sequence-to-sequence fine-tuning toolkit
NEW): Multilingual NMT w/ pretrained cross-lingual encoders
- 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.
New August, 2021: LayoutReader release *****
New August, 2021: DeltaLM release *****
New July, 2021: BEiT release *****
- LayoutXLM (April 17, 2021): multimodal pre-training for multilingual visually-rich document understanding. The pre-trained LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the FUNSD and multilingual XFUND dataset including 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese). “LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding“
- AdaLM (June 2021): a simple yet effective approach for domain adaptation of pre-trained models. Biomedical specific pre-trained models are released. “Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains
- MiniLMv2 (December, 2020): a simple yet effective task-agnostic knoweldge distillation method, namely multi-head self-attention relation distillation, for compressing large pre-trained Transformers into small and fast pre-trained models. MiniLMv2 significantly outperforms MiniLMv1. Both English and multilingual MiniLM models are released. “MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
- 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
- LayoutLM 1.0 (February 18, 2020): pre-trained models for document (image) understanding (e.g. receipts, forms, etc.) . It achieves new SOTA results in several downstream tasks, including form understanding (the FUNSD dataset from 70.72 to 79.27), receipt understanding (the ICDAR 2019 SROIE leaderboard from 94.02 to 95.24) and document image classification (the RVL-CDIP dataset from 93.07 to 94.42). “LayoutLM: Pre-training of Text and Layout for Document Image Understanding
- s2s-ft 1.0 (February 26, 2020): A PyTorch package used to fine-tune pre-trained Transformers for sequence-to-sequence language generation. “s2s-ft: Fine-Tuning Pre-Trained Transformers for Sequence-to-Sequence Learning“
- MiniLM 1.0 (February 26, 2020): deep self-attention distillation is all you need (for task-agnostic knowledge distillation of pre-trained Transformers). MiniLM (12-layer, 384-hidden) achieves 2.7x speedup and comparable results over BERT-base (12-layer, 768-hidden) on NLU tasks as well as strong results on NLG tasks. The even smaller MiniLM (6-layer, 384-hidden) obtains 5.3x speedup and produces very competitive results. “MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
- UniLM 2.0 (February 28, 2020): unified pre-training of bi-directional LM (via autoencoding) and sequence-to-sequence LM (via partially autoregressive) w/ Pseudo-Masked Language Model for language understanding and generation. UniLM v2 achieves new SOTA in a wide range of natural language understanding and generation tasks. “UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
***** October 1st, 2019: UniLM v1 release *****
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.
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 (