Papers with Code 아카이브
Question Answering
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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/soujanyarbhat/aNswER_multirc
Repurposing Entailment for Multi-Hop Question Answering Tasks
https://github.com/soumik12345/transformer.pytorch
https://github.com/sourenaKhanzadeh/snakeAi
https://github.com/soutsios/pos-tagger-bert-tensorflow
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/souvikshanku/translit-former
https://github.com/spaditha/biobert
Pre-trained Language Model for Biomedical Question Answering
https://github.com/spokoyny/spokoyny.github.io
Towards Answering Climate Questionnaires from Unstructured Climate Reports
https://github.com/squeezeailab/squeezellm
https://github.com/sreyan88/reclap
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
https://github.com/srgrace/contextual-compression
Extending Context Window of Large Language Models via Semantic Compression
https://github.com/srush/minichain
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/srzer/mod
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization
https://github.com/ssu-humane/hero
AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
https://github.com/stanfordbdhg/llama.cpp
https://github.com/stanfordnlp/coqa-baselines
https://github.com/stanfordvl/atp-video-language
https://github.com/steering-vectors/steering-vectors
Representation Engineering: A Top-Down Approach to AI Transparency
https://github.com/stefan-it/europeana-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/stefan-it/historic-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/stevekgyang/sccl
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
https://github.com/stevinc/Transformer_Timeseries
https://github.com/stikbuf/Language_Modeling
https://github.com/stormieGal/mediLearn
https://github.com/straightAYiJun/vqa-attention-visualize-system
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/studio-ousia/wikipedia2vec
Neural Attentive Bag-of-Entities Model for Text Classification
https://github.com/subercui/pyGConvAT
https://github.com/suinleelab/path_explain
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/sumilergao/sungen
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
https://github.com/sunblaze-ucb/political_leaning_RepE
Representation Engineering: A Top-Down Approach to AI Transparency
https://github.com/sunghyunjun/kaggle-hpa
https://github.com/sunk/qneurons
Adding Gradient Noise Improves Learning for Very Deep Networks
https://github.com/sunlab-osu/understanding-cot
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/sunsiqi26/Entailment-with-TensorFlow
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
https://github.com/sunyilgdx/prompts4keras
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sunzeyeah/rlhf
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.