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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/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/airsplay/vimpac
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aistairc/kirt_bert_on_abci
https://github.com/aivolcano/BERT_MRC_CLS
https://github.com/albpurpura/PE4IR
https://github.com/aleksac99/SQuAD-SR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alextmallen/adaptive-retrieval
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
https://github.com/algharak/BERTenhance
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alibaba-edu/simple-effective-text-matching
Simple and Effective Text Matching with Richer Alignment Features
https://github.com/alibaba-edu/simple-effective-text-matching-pytorch
Simple and Effective Text Matching with Richer Alignment Features
https://github.com/alibaba/EasyNLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/scifact
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/amarazad/DSRNet
Meta-Context Transformers for Domain-Specific Response Generation
https://github.com/amauriciorr/AubreyBot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/contraclm
https://github.com/ambityga/memsem
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amoramine/FNet_classification
https://github.com/amoramine/FNet_with_BART_classification
https://github.com/amore-upf/masked-coreference
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/andi611/Mockingjay-Speech-Representation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aneesha/SiameseBERT-Notebook
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
https://github.com/anlausch/LIBERT
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
https://github.com/anonymousxzy/compacter
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
https://github.com/antoine77340/howto100m
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/anton-bushuiev/ppiformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/antonionlp/antonio
NLP Verification: Towards a General Methodology for Certifying Robustness
https://github.com/anupamsingh610/bert_ner_stride
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/appcoreopc/berty
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aranciokov/fsmmda_videoretrieval
https://github.com/aranciokov/ranp
https://github.com/archinetai/smart-pytorch
https://github.com/archinetai/vat-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.