https://github.com/deepmipt/bert/tree/feat/multi_gpu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Papers with Code 아카이브
Common Sense Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 24
- 보존된 평가 기록
- 397
Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deepset-ai/FARM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deping-1/2019-false-news-detection-challenge
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/derronxu/sparsebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/devJWSong/bert-crf-entity-recognition-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/devhemza/BERTweet_sentiment_analysis
https://github.com/dhlee347/pytorchic-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/diabhaque/Sixth-Sense
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dialogue-evaluation/rusentne-evaluation
Reasoning Implicit Sentiment with Chain-of-Thought Prompting
https://github.com/dig-team/hanna-benchmark-asg
https://github.com/dispink/xpt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dmlc/gluon-nlp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dreamgonfly/BERT-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/duanchi1230/NLP_Project_AI2_Reasoning_Challenge
https://github.com/duyunshu/bert-sentiment-analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dzqjorking/transpose
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eagle705/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eaishwa/quora-ques-pair-similarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ecnu-icalk/educhat
https://github.com/ecolab-postech/owq
https://github.com/efficient-large-model/vila
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/elenalenaelena/machine-learning-viz
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/enod/arxiv-nlp-notes
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/epcpu/deep-nlp-links
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/epfml/collaborative-attention
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/eternityyw/tram-benchmark
https://github.com/eth-easl/deltazip
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/ethanjperez/true_few_shot
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/eunseo1092/Graduation_project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/evtaktasheva/dependency_extraction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/expertailab/spaceqa
https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.