https://github.com/pfecht/bert-exploration
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/phoebussi/alpaca-cot
https://github.com/phohenecker/pytorch-transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/piegu/language-models
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pingheng001/Cnn-Bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pisalore/roberta_results
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/plkmo/BERT-Relation-Extraction
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/polzinben/TensorFlow_NLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/postech-ami/smile-dataset
https://github.com/ps2811/Bert-Multi-Label-Text-Classification-GitVersion
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pwc-1/Paper-5/tree/main/mt5
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/pwc-1/Paper-8/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/pwc-1/Paper-8/tree/main/deberta
https://github.com/pwc-1/Paper-8/tree/main/deberta_v2
https://github.com/pwc-1/Paper-9/tree/main/2/mistral
https://github.com/pwc-1/Paper-9/tree/main/2/mixtral
https://github.com/pwc-1/Paper-9/tree/main/3/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/pwc-1/Paper-9/tree/main/4/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/pwc-1/Paper-9/tree/main/5/xlm_roberta
https://github.com/pwc-1/Paper-9/tree/main/luke
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/pytorch/text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/contextualized-embedding-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/pretrained-language-model-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/qiujihao19/artemis
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
https://github.com/qizekun/ShapeLLM
PointLLM: Empowering Large Language Models to Understand Point Clouds
https://github.com/qwenlm/qwen1.5
https://github.com/qwenlm/qwen2
https://github.com/qwenlm/qwen2-vl
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/qwopqwop200/GPTQ-for-LLaMa
https://github.com/qywu/Chinese-GPT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rajatgermany/qa-nlp
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/rajlm10/Chandler
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ramanakshay/nanogpt
https://github.com/rasmith/vllm
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.