[Translation] Update on README_en.md

README_en.md

@@ -1,15 +1,14 @@
-[简体中文🀄](./README.md) |  **English**🌎
 
-<p align="center">
-  <img src="https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png" align="middle"  width="500" />
-</p>
+[简体中文🀄](./README.MD) | **English🌎**
+
+<p align="center"> <img src="https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png" align="middle"  width="500" /> </p>
 
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 <p align="center">
     <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
     <a href="https://github.com/PaddlePaddle/PaddleNLP/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleNLP?color=ffa"></a>
-    <a href=""><img src="https://img.shields.io/badge/python-3.6.2+-aff.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
     <a href="https://github.com/PaddlePaddle/PaddleNLP/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/PaddleNLP?color=9ea"></a>
     <a href="https://github.com/PaddlePaddle/PaddleNLP/commits"><img src="https://img.shields.io/github/commit-activity/m/PaddlePaddle/PaddleNLP?color=3af"></a>
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     <a href="https://github.com/PaddlePaddle/PaddleNLP/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleNLP?color=ccf"></a>
 </p>
 
-<h4 align="center">
-  <a href=#features> Features </a> |
-  <a href=#installation> Installation </a> |
-  <a href=#quick-start> Quick Start </a> |
-  <a href=#api-reference> API Reference </a> |
-  <a href=#community> Community </a>
-</h4>
+<h4 align="center"> <a href=#features> Features </a> | <a href=#installation> Installation </a> | <a href=#quick-start> Quick Start </a> | <a href=#api-reference> API Reference </a> | <a href=#community> Community </a>
 
-**PaddleNLP** is an *easy-to-use* and *powerful* NLP library with **Awesome** pre-trained model zoo, supporting wide-range of NLP tasks from research to industrial applications.
+**PaddleNLP** is a natural language processing development library that is both **easy to use** and **powerful**. It aggregates high-quality pre-trained models in the industry and provides a **plug-and-play** development experience, covering a model library for various NLP scenarios. With practical examples from industry practices, PaddleNLP can meet the needs of developers who require **flexible customization**.
 
 ## News 📢
 
-* 🔥 **Latest Features**
-  * 📃 Release **[UIE-X](./applications/information_extraction)**, an universal information extraction model that supports both document and text inputs.
-  * ❣️Release  **[Opinion Mining and Sentiment Analysis Models](./applications/sentiment_analysis/unified_sentiment_extraction)** based on UIE,  including abilities of sentence-level and aspect-based sentiment classification, attribute extraction,  opinion extraction,  attribute aggregation and implicit opinion extraction.
-* **2022.9.6 [PaddleNLPv2.4](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.4.0) Released!**
-  * 💎 NLP Tools: Released **[Pipelines](./pipelines)** which supports turn-key construction of search engine and question answering systems. It features a flexible design that is applicable for all kinds of NLP systems so you can build end-to-end NLP pipelines like Legos!
+* **2023.1.12: [Release of PaddleNLP v2.5](<https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.5.0>)**
+
+    * 🔨 NLP Tools: [PPDiffusers](./ppdiffusers), a domestically produced diffusion model toolbox, has been released. It integrates multiple diffusion model parameters and components, provides a complete training process for diffusion models, and supports FastDeploy inference acceleration and multi-hardware deployment (supports Ascend chips and Kunlun core deployment).
+    * 💎 Industrial Applications: Information extraction, text classification, sentiment analysis, and intelligent question answering have all been newly upgraded. New releases include document information extraction [UIE-X](./applications/information_extraction/document), unified text classification [UTC](./applications/zero_shot_text_classification), unified sentiment analysis [UIE-Senta](./applications/sentiment_analysis/unified_sentiment_extraction) , and [unsupervised QA application](./applications/question_answering/unsupervised_qa). At the same time, the [ERNIE 3.0 Tiny v2](./model_zoo/ernie-tiny) series of pre-trained small models have been released, which are more effective with low-resource and foreign data. They provide open-source end-to-end deployment solutions such as model pruning, model quantization, FastDeploy inference acceleration, and edge-side deployment to reduce the difficulty of pre-trained model deployment.
+    * 💪 Framework Upgrade: Pre-training model [parameter configuration unification](./paddlenlp/transformers/configuration_utils.py), saving and loading custom parameter configurations no longer requires additional development; [Trainer API](./docs/trainer.md) has added BF16 training, recompute recalculations, sharding, and other distributed capabilities. Large-scale pre-training model training can easily be accomplished through simple configuration. [Model Compression API](./docs/compression.md) supports quantization training, vocabulary compression, and other functions. The compressed model has smaller accuracy loss, and the memory consumption of model deployment is greatly reduced. [Data Augmentation API](./docs/dataaug.md) has been comprehensively upgraded to support three granularities of data augmentation strategy: character, word, and sentence, making it easy to customize data augmentation strategies.
+    * 🤝 Ecological Collaboration: 🤗Huggingface hub officially supports PaddleNLP pre-trained models, supporting PaddleNLP Model and Tokenizer downloads and uploads directly from the 🤗Huggingface hub. Everyone is welcome to experience PaddleNLP pre-trained model effects on the 🤗Huggingface hub [here](https://huggingface.co/PaddlePaddle).
 
-  * 🔨 Industrial application: Release **[Complete Solution of Text Classification](./applications/text_classification)** covering various scenarios of text classification: multi-class, multi-label and hierarchical, it also supports **few-shot learning** and the training and optimization of **TrustAI**. Upgrade for [**UIE**](./model_zoo/uie) and release **UIE-M**, support both Chinese and English information extraction in a single model; release the data distillation solution for UIE to break the bottleneck of time-consuming of inference.
+* **September 6, 2022: [Release of PaddleNLP v2.4](<https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.4.0>)**
 
-  * 🍭 AIGC: Release code generation SOTA model [**CodeGen**](./examples/code_generation/codegen) that supports  multiple programming languages code generation. Integrate [**Text to Image Model**](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/docs/model_zoo/taskflow.md#%E6%96%87%E5%9B%BE%E7%94%9F%E6%88%90) DALL·E Mini, Disco Diffusion, Stable Diffusion, let's play and have some fun!
+    * 🔨 NLP Tools: [NLP Pipeline System Pipelines](./pipelines) has been released, supporting the rapid construction of search engines and question-answering systems, and can be extended to support various NLP systems, making it easy, flexible, and efficient to solve NLP tasks like building blocks!
+    * 💎 Industrial Applications: A new [text classification full-process application solution](./applications/text_classification) has been added, covering various scenarios such as multi-classification, multi-label, and hierarchical classification, supporting small-sample learning and TrustAI trustworthy computing model training and tuning.
+    * 🍭 AIGC: The SOTA model [CodeGen](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/examples/code_generation/codegen) for code generation in various programming languages has been added.
+    * 💪 Framework Upgrade: [Automatic Model Compression API](./docs/compression.md) has been released, which automatically cuts and quantizes models, greatly reducing the threshold for using model compression technology. [Small Sample Prompt](./applications/text_classification/multi_class/few-shot) capability has been released, integrating classic algorithms such as PET, P-Tuning, and RGL.
 
-  * 💪 Framework upgrade: Release [**Auto Model Compression API**](./docs/compression.md), supports for pruning and quantization automatically, lower the barriers of model compression; Release [**Few-shot Prompt**](./applications/text_classification/multi_class/few-shot), includes the algorithms such as PET, P-Tuning and RGL.