Papers with Code 아카이브
Semantic Textual Similarity
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Semantic textual similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.
Image source: Learning Semantic Textual Similarity from Conversations
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vijay120/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vincenzo-scotti/def_2_vec
https://github.com/vineet54/fnet-google-pytorch
https://github.com/viniciusoliveirasd/bert-applications
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vmware-labs/bert-pretraining
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/volcengine/vegiantmodel
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vvvm23/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/wadoodabdul/clinical_ner_benchmark
https://github.com/wakafengfan/simcse-pytorch
https://github.com/wangcongcong123/ttt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wayalhruhi/gogle_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wbglaeser/brexit-sentiment
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
https://github.com/wchh127/yykf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/weidafeng/NLU2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/weiyezhimeng/silent-guardian
https://github.com/wenhuchen/HDSA-Dialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/whqwill/seq2seq-keyphrase-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/why2000/DuReader-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/woodfrog/vse_infty
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO
https://github.com/wooseok-AI/Korean_e2e_CR_BERT
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/writerai/fitbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wyf-python/Bert-Pytorch-TextClassification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wzzzd/LM_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wzzzd/Pretrain_Bert_with_MaskLM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xbtlin/All-about-Machine-Learning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xesdiny/test-bert-master
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiangyue9607/CliniRC
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiangyue9607/SanText
https://github.com/xiaolilaoli/tiny_bert_ms
TinyBERT: Distilling BERT for Natural Language Understanding
https://github.com/xiaoouwang/frenchnlp
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/xiaopingzhong/bert-finetune-for-classfier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiaopp123/bert_explain
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiaoqian19940510/text-classification-
https://github.com/xiaoqian19940510/text-classification-surveys
https://github.com/xinyooo/ALBERT4Rec
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.