https://github.com/assafelovic/gpt-researcher
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/asultan123/DL_LLC_Prefetching
https://github.com/atharvajdhumal/Sentiment-Analysis
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/athms/learning-from-brains
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/atreyasha/semantic-isometry-nmt
https://github.com/au1khan/FactQA
https://github.com/augustgw/early-exit-transformer
https://github.com/aukhanee/FactQA
https://github.com/aus10powell/Automated-Health-Responses
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/autobotasia/vibert
https://github.com/automl-research/red-gnn
https://github.com/autonomousvision/carla_garage
https://github.com/avichaychriqui/HeBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/avidale/encodechka
https://github.com/avishek-018/single_notebook_projects
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awalther/scibart
https://github.com/aws-samples/amazon-sagemaker-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awslabs/mlm-scoring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awslabs/sockeye
https://github.com/axiomlab/Cable
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/ayeffkay/rubert-tiny
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/aykutaaykut/Memory-Networks
https://github.com/aymericdamien/TopDeepLearning
https://github.com/ays-dev/keras-transformer
https://github.com/azreasoners/CRCG
https://github.com/b-faye/lightmdetr
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/baai-dcai/bunny
https://github.com/baai-dcai/segvol
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
https://github.com/babangain/translation
https://github.com/babelscape/wikineural
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/baichuan-inc/Baichuan-Omni-1.5
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/baichuan-inc/baichuan2
https://github.com/baidu/DuReader
https://github.com/baithebest/adagp
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/baithebest/sparsellm
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
https://github.com/bangoc123/drop-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.