Update README.md

Author: donglixp

README.md

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 # UniLM
 **We develop pre-trained models for natural language understanding (NLU) and generation (NLG) tasks**
 
-**\*\*\*\*\* ```New February, 2020```: [UniLM v2](https://github.com/microsoft/unilm/tree/master/unilm) | [MiniLM v1](https://github.com/microsoft/unilm/tree/master/minilm) | [LayoutLM v1](https://github.com/microsoft/unilm/tree/master/layoutlm) | [s2s-ft v1](https://github.com/microsoft/unilm/tree/master/s2s-ft) release \*\*\*\*\***
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 The family of UniLM:
 > [**UniLM**](https://github.com/microsoft/unilm/tree/master/unilm): **unified pre-training for language understanding and generation**
 
@@ -12,17 +10,20 @@ The family of UniLM:
 
 >  [**s2s-ft**](https://github.com/microsoft/unilm/tree/master/s2s-ft) (```new```): **sequence-to-sequence fine-tuning toolkit**
 
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+**\*\*\*\*\* ```New February, 2020```: [UniLM v2](https://github.com/microsoft/unilm/tree/master/unilm) | [MiniLM v1](https://github.com/microsoft/unilm/tree/master/minilm) | [LayoutLM v1](https://github.com/microsoft/unilm/tree/master/layoutlm) | [s2s-ft v1](https://github.com/microsoft/unilm/tree/master/s2s-ft) release \*\*\*\*\***
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 Update in this release:
 - [x] [**LayoutLM 1.0**](https://github.com/microsoft/unilm/tree/master/layoutlm) (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](https://rrc.cvc.uab.es/?ch=13&com=evaluation&task=3) 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](https://arxiv.org/abs/1912.13318)"
 - [x] [**s2s-ft 1.0**](https://github.com/microsoft/unilm/tree/master/s2s-ft) (February 26, 2020): A PyTorch package used to fine-tune pre-trained Transformers for sequence-to-sequence language generation. "[s2s-ft: Fine-Tuning Bidirectional Transformers for Sequence-to-Sequence Learning](#)"
 - [x] [**MiniLM 1.0**](https://github.com/microsoft/unilm/tree/master/minilm) (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](https://arxiv.org/abs/2002.10957)"
-- [x] [**UniLM 2.0**](https://github.com/microsoft/unilm/tree/master/unilm) (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 across several widely used benchmarks. "[UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://arxiv.org/abs/2002.12804)"
+- [x] [**UniLM 2.0**](https://github.com/microsoft/unilm/tree/master/unilm) (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](https://arxiv.org/abs/2002.12804)"
 
 
 
 **\*\*\*\*\* October 1st, 2019: UniLM v1 release \*\*\*\*\***
 
-- [x] [**UniLM v1**](https://github.com/microsoft/unilm/tree/master/unilm-v1) (September 30, 2019): the code and pre-trained models for the NeurIPS 2019 paper entitled "[Unified Language Model Pre-training for Natural Language Understanding and Generation](https://arxiv.org/abs/1905.03197)". UniLM (v1) achieves the **new SOTA results** in **NLG** (especially **sequence-to-sequence generation**) tasks/benchmarks, including abstractive summarization (the Gigaword and CNN/DM dataset), question generation (the SQuAD QG dataset), etc. 
+- [x] [**UniLM v1**](https://github.com/microsoft/unilm/tree/master/unilm-v1) (September 30, 2019): the code and pre-trained models for the NeurIPS 2019 paper entitled "[Unified Language Model Pre-training for Natural Language Understanding and Generation](https://arxiv.org/abs/1905.03197)". UniLM (v1) achieves the **new SOTA results** in **NLG** (especially **sequence-to-sequence generation**) tasks, including abstractive summarization (the Gigaword and CNN/DM datasets), question generation (the SQuAD QG dataset), etc. 
 
 ## License
 This project is licensed under the license found in the LICENSE file in the root directory of this source tree.