https://github.com/amazon-science/alexa-teacher-models
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
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/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/efficient-longdoc-classification
https://github.com/amazon-science/qa-dataset-converter
https://github.com/ambityga/memsem
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amenra/ranx
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/amirassov/kaggle-imaterialist
https://github.com/amoramine/Pegasus_Longformer_summarization
https://github.com/amoramine/Pegasus_with_Longformer_summarization
https://github.com/amore-upf/masked-coreference
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/amsqr/Allen_AI_Kaggle
Moving Beyond the Turing Test with the Allen AI Science Challenge
https://github.com/amzn/seqzero
https://github.com/anantvir/Generative-Retrieval_Conversational_Model_Tensorflow
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/ancientshi/erm4
https://github.com/andi611/Mockingjay-Speech-Representation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/andotalao24/learn-forget-unsafe-llm
https://github.com/andrear632/ProjectDeepLearning
Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving
https://github.com/andreiilie1/dynamic_coattention_networks
https://github.com/andreiluca96/reversed-vqa
Visual Question Answering: Datasets, Algorithms, and Future Challenges
https://github.com/andrewschreiber/hs-math-nlp
https://github.com/andrewzhe/lawyer-llama
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
https://github.com/andy840314/QANet-pytorch-
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/andyweizhao/NLP-Capsule
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
https://github.com/andyweizhao/capsule
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
https://github.com/andyweizhao/capsule_text_classification
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
https://github.com/angelosnal/vision-diffmask
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
https://github.com/anhinga/JuliaCon2021-poster
https://github.com/anhtu293/transformer_from_scratch
https://github.com/anhtunguyen98/NER-biaffine
Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers
https://github.com/anindyasarkarIITH/Structure_data_to_summary
https://github.com/anirudharc/transformer
https://github.com/anish-lu-yihe/SVRT-by-GAT
https://github.com/anisha2102/docvqa
https://github.com/anishnarkar/experiments_deeplearning_models
https://github.com/ankurbanga/Language-Models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.