https://github.com/expz/quiet-star
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
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/extensive-nlp/TSAI-DeepNLP-END2.0
https://github.com/fabiocorreacordeiro/Elsevier_abstracts-Classification
https://github.com/facebook/MazeBase
https://github.com/facebookarchive/MazeBase
https://github.com/facebookarchive/babi-tasks
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/facebookresearch/EgoTV
https://github.com/facebookresearch/House3D
https://github.com/facebookresearch/ParlAI
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/facebookresearch/anli
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/facebookresearch/chai
https://github.com/facebookresearch/chameleon
https://github.com/facebookresearch/coconut
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
https://github.com/facebookresearch/data2vec_vision
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/facebookresearch/editeval
https://github.com/facebookresearch/fairscale
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/facebookresearch/fairseq
https://github.com/facebookresearch/fairseq2
https://github.com/facebookresearch/grid-feats-vqa
https://github.com/facebookresearch/habitat-challenge
https://github.com/facebookresearch/habitat-lab
https://github.com/facebookresearch/meru
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/facebookresearch/minif2f
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/facebookresearch/multimodal
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/facebookresearch/reconsider
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
https://github.com/facebookresearch/tomi
https://github.com/facebookresearch/xformers
https://github.com/facico/chinese-vicuna
https://github.com/faizansuhail89/bert
https://github.com/fajri91/Multi_SummEval
FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
https://github.com/fajri91/indommlu
https://github.com/fanchenyou/transformer-study
https://github.com/fancyerii/bert
https://github.com/faneshion/DRMM
https://github.com/fanqiwan/fuseai
https://github.com/fanyun-sun/DARNN
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.