提交 62a13fd8 编写于 作者: L liyukun01

update readme

上级 87d3c630
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* [Discourse Relation Task](#discourse-relation-task)
* [IR Relevance Task](#ir-relevance-task)
* [ERNIE 1.0: <strong>E</strong>nhanced <strong>R</strong>epresentation through k<strong>N</strong>owledge <strong>I</strong>nt<strong>E</strong>gration](#ernie-10-enhanced-representation-through-knowledge-integration)
* [Compare the ERNIE 1.0 and ERNIE 2.0](#compare-the-ernie-10-and-ernie-20)
* [Results on English Datasets](#results-on-english-datasets)
* [Results on Chinese Datasets](#results-on-chinese-datasets)
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Integrating both phrase information and named entity information enables the model to obtain better language representation compare to BERT. ERNIE is trained on multi-source data and knowledge collected from encyclopedia articles, news, and forum dialogues, which improves its performance in context-based knowledge reasoning.
### Compare the ERNIE 1.0 and ERNIE 2.0
#### Pre-Training Tasks
| Tasks | ERNIE model 1.0 | ERNIE model 2.0 (en) | ERNIE model 2.0 (zh) |
| ------------------- | -------------------------- | ------------------------------------------------------------ | ----------------------------------------- |
| **Word-aware** | ✅ Knowledge Masking | ✅ Knowledge Masking <br> ✅ Capitalization Prediction <br> ✅ Token-Document Relation Prediction | ✅ Knowledge Masking |
| **Structure-aware** | | ✅ Sentence Reordering | ✅ Sentence Reordering <br> ✅ Sentence Distance |
| **Semantic-aware** | ✅ Next Sentence Prediction | ✅ Discourse Relation | ✅ Discourse Relation <br> ✅ IR Relevance |
## Release Notes
- July 30, 2019: release ERNIE 2.0
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*\*The DRCD dataset is converted from Traditional Chinese to Simplified Chinese based on tool: https://github.com/skydark/nstools/tree/master/zhtools*
\* *The pre-training data of ERNIE 1.0 BASE does not contain instances whose length exceeds 128, but other models is pre-trained with the instances whose length are 512. It causes poorer performance of ERNIE 1.0 BASE on long-text tasks. So We have released [ERNIE 1.0 Base(max-len-512)](https://ernie.bj.bcebos.com/ERNIE_1.0_max-len-512.tar.gz) in July 29th, 2019*
\* *The pre-training data of ERNIE 1.0 BASE does not contain instances whose length exceeds 128, but other models is pre-trained with the instances whose length are 512. It causes poorer performance of ERNIE 1.0 BASE on long-text tasks. So We have released [ERNIE 1.0 Base(max-len-512)](https://ernie.bj.bcebos.com/ERNIE_1.0_max-len-512.tar.gz) on July 29th, 2019*
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* [Discourse Relation Task](#discourse-relation-task)
* [IR Relevance Task](#ir-relevance-task)
* [ERNIE 1.0: <strong>E</strong>nhanced <strong>R</strong>epresentation through k<strong>N</strong>owledge <strong>I</strong>nt<strong>E</strong>gration](#ernie-10-enhanced-representation-through-knowledge-integration)
* [对比 ERNIE 1.0 和 ERNIE 2.0](#对比-ernie-10-和-ernie-20)
* [中文效果验证](#中文效果验证)
* [英文效果验证](#英文效果验证)
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训练数据方面,除百科类、资讯类中文语料外,**ERNIE** 还引入了论坛对话类数据,利用 **DLM**(Dialogue Language Model)建模 Query-Response 对话结构,将对话 Pair 对作为输入,引入 Dialogue Embedding 标识对话的角色,利用 Dialogue Response Loss 学习对话的隐式关系,进一步提升模型的语义表示能力。
### 对比 ERNIE 1.0 和 ERNIE 2.0
#### Pre-Training Tasks
| 任务 | ERNIE 1.0 模型 | ERNIE 2.0 英文模型 | ERNIE 2.0 中文模型 |
| ------------------- | -------------------------- | ------------------------------------------------------------ | ----------------------------------------- |
| **Word-aware** | ✅ Knowledge Masking | ✅ Knowledge Masking <br> ✅ Capitalization Prediction <br> ✅ Token-Document Relation Prediction | ✅ Knowledge Masking |
| **Structure-aware** | | ✅ Sentence Reordering | ✅ Sentence Reordering <br> ✅ Sentence Distance |
| **Semantic-aware** | ✅ Next Sentence Prediction | ✅ Discourse Relation | ✅ Discourse Relation <br> ✅ IR Relevance |
## 开源记录
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