https://github.com/ielab/characterbert-dr
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Papers with Code 아카이브
Question Answering
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- 평가표
- 153
- 보존된 평가 기록
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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/iinemo/lm-polygraph
https://github.com/iit-nlp-research/chatgpt-crawler
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/ilhamfp/indonesian-text-classification-multilingual
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/iliaschalkidis/ELMo-keras
https://github.com/imcaspar/gpt2-ml
https://github.com/imgarylai/bert-embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/imnearth/coat
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/inclusionai/aworld
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
https://github.com/indobenchmark/indobenchmark-toolkit
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
https://github.com/inejc/paragraph-vectors
https://github.com/infiagent/infiagent
https://github.com/infini-ai-lab/sirius
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/ink-usc/request
Indirect Supervision for Relation Extraction using Question-Answer Pairs
https://github.com/insait-institute/lm-evaluation-harness-bg
https://github.com/insigh/THUMT
https://github.com/intel/neural-compressor
https://github.com/intellabs/fastrag
https://github.com/irgroup/clef2023-longeval-irc
https://github.com/ishalyminov/memn2n
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/isotlaboratory/ml4vrp
https://github.com/issam9/finetuning-bert-models-for-arabic-dialect-detection
AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/itsShnik/adaptively-finetuning-transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/itspreeti25/Q-A-implementation-using-BERT-finetuned-on-multiple-datasets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ivabojic/sleepqa
https://github.com/j30206868/numnet-chinese
A Simple and Effective Model for Answering Multi-span Questions
https://github.com/jacopotagliabue/dag-card-is-the-new-model-card
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
https://github.com/jacquesboitreaud/DeepFRED
https://github.com/jacquesboitreaud/rna_ne
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jaehyunnn/RelationalNetwork_pytorch
https://github.com/jaemuzzin/plagnn
https://github.com/jageshmaharjan/BERT_Service
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.