soledad921 / NLP-Interview-Notes
本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料,该资料目前包含 自然语言处理各领域的 面试题积累。
☆84Updated 3 years ago
Related projects: ⓘ
- 收集了目前为止中文领域的MRC抽取式数据集☆118Updated 3 months ago
- NLP句子编码、句子embedding、语义相似度:BERT_avg、BERT_whitening、SBERT、SmiCSE☆173Updated 2 years ago
- CoSENT、STS、SentenceBERT☆161Updated last year
- SimCSE有监督与无监督实验复现☆140Updated 6 months ago
- 基于GlobalPointer的实体/关系/事件抽取☆140Updated 2 years ago
- 中文无监督SimCSE Pytorch实现☆131Updated 3 years ago
- 中文数据集下SimCSE+ESimCSE的实现☆185Updated 2 years ago
- Knowledge Graph☆168Updated 2 years ago
- A simple framework for building some basic NLP tasks☆59Updated last year
- SinglepassTextCluster, an TextCluster tools based on Singlepass cluster algorithm that use tfidf vector and doc2vec,which can be used for…☆60Updated 3 years ago
- 每天阅读过的论文的简要笔记☆185Updated this week
- Pattern-Exploiting Training在中文上的简单实验☆169Updated 3 years ago
- [SIGIR 2022] Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval☆164Updated last year
- Implemention of NER model on chinese dataset.☆64Updated last year
- This is the repo of the medical dialogue dataset 'imcs21' in CBLUE@Tianchi☆72Updated last year
- 全局指针统一处理嵌套与非嵌套NER☆248Updated 3 years ago
- 无监督中文关键词抽取(Keyphrase Extraction),基于统计,基于图【LDA与PageRank(TextRank, TPR, Salience Rank, Single TPR等)】,基于嵌入【SIFRank等】,开箱即用!☆100Updated 2 years ago
- OCNLI: 中文原版自然语言推理任务☆144Updated 2 years ago
- 本项目是作者们根据个人面试和经验总 结出的自然语言处理(NLP)面试准备的学习笔记与资料,该资料目前包含 自然语言处理各领域的 面试题积累。☆54Updated 3 years ago
- ☆275Updated 2 years ago
- 中文机器阅读理解数据集☆96Updated 3 years ago
- 基于词汇信息融合的中文NER模型☆159Updated 2 years ago
- 基于SpanBert的中文指代消解,pytorch实现☆95Updated last year
- SimCSE在中文上的复现,有监督+无监督☆261Updated 2 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 last year
- experiments of some semantic matching models and comparison of experimental results.☆154Updated last year
- basic framework for rag(retrieval augment generation)☆69Updated 8 months ago
- 基于 pytorch 的 bert 实现和下游任务微调☆46Updated 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。☆40Updated last year
- ☆84Updated 2 years ago