YaoXinZhi / Convert-Bert-TF-checkpoint-to-Pytorch
Convert Bert TF-checkpoint to Pytorch
☆15Updated 4 years ago
Alternatives and similar repositories for Convert-Bert-TF-checkpoint-to-Pytorch:
Users that are interested in Convert-Bert-TF-checkpoint-to-Pytorch are comparing it to the libraries listed below
- 苏神SPACE pytorch版本复现☆42Updated 3 years ago
- 中文无监督SimCSE Pytorch实现☆133Updated 3 years ago
- ☆88Updated 3 years ago
- TPlinker for NER 中文/英文命名实体识别☆122Updated 3 years ago
- TIANCHI-小布助手对话短文本语义匹配BERT baseline☆32Updated 3 years ago
- 论文复现《Named Entity Recognition as Dependency Parsing》☆128Updated 3 years ago
- 天池中药说明书实体识别挑战冠军方案；中文命名实体识别；NER; BERT-CRF & BERT-SPAN & BERT-MRC；Pytorch☆23Updated 4 years ago
- CCKS 2020：面向金融领域的小样本跨类迁移事件抽取。该项目实现基于MRC的事件抽取方法☆39Updated 2 years ago
- Label Mask for Multi-label Classification☆56Updated 3 years ago
- NER任务SOTA模型BERT_MRC☆60Updated 10 months ago
- code for ACL2020:《FLAT: Chinese NER Using Flat-Lattice Transformer》 我注释&修改&添加了部分源码，使得大家更容易复现这个代码。☆56Updated 4 years ago
- CCKS2020面向金融领域的小样本跨类迁移事件抽取baseline☆55Updated 2 years ago
- 中文文本句对相似度匹配-ATEC数据集☆21Updated 4 years ago
- 2022搜狐校园算法大赛NLP赛道第一名开源方案（实验代码）☆84Updated 2 years ago
- NLP实验：新词挖掘+预训练模型继续Pre-training☆47Updated last year
- 使用Mask LM预训练任务来预训练Bert模型。训练垂直领域语料的模型表征，提升下游任务的表现。☆41Updated last year
- ccks金融事件主体抽取☆72Updated 4 years ago
- R-Drop方法在中文任务上的简单实验☆90Updated 2 years ago
- NLP关系抽取：序列标注、层叠式指针网络、Multi-head Selection、Deep Biaffine Attention☆100Updated 3 years ago
- ☆60Updated 4 years ago
- 对苏神的bert4keras的实现原理和矩阵运算进行详细的注释，方便学习；bert4keras链接：https://github.com/bojone/bert4keras☆41Updated 4 years ago
- 句子匹配模型，包括无监督的SimCSE、ESimCSE、PromptBERT，和有监督的SBERT、CoSENT。☆96Updated 2 years ago
- GAIIC2022商品标题实体识别Baseline，使用GlobalPointer实现，线上0.80349☆53Updated 2 years ago
- 基于prompt的中文文本分类。☆54Updated last year
- CoSENT、STS、SentenceBERT☆162Updated last year
- 中文数据集下SimCSE+ESimCSE的实现☆192Updated 2 years ago
- 基于tensorflow1.x的预训练模型调用，支持单机多卡、梯度累积，XLA加速，混合精度。可灵活训练、验证、预测。☆58Updated 3 years ago
- pytorch Efficient GlobalPointer☆53Updated 2 years ago
- 本项目是NLP领域一些任务的基准模型实现，包括文本分类、命名实体识别、实体关系抽取、NL2SQL、CKBQA以及BERT的各种下游任务应用。☆47Updated 3 years ago
- GPLinker_pytorch☆80Updated 2 years ago