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/trusthlt/privacy-legal-nlp-lm
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
https://github.com/tsandefer/capstone_2
https://github.com/tsandefer/dsi_capstone_2
https://github.com/tshu-w/dbcopilot
https://github.com/tshu-w/llm4em
https://github.com/tsinghuac3i/ultramedical
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
https://github.com/tsujuifu/pytorch_lstm-shuttle
https://github.com/ttok0s7u2n5/ML2_proj
https://github.com/tugstugi/mongolian-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/turkunlp/megatron-deepspeed
https://github.com/turningpoint-ai/visualthinker-r1-zero
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
https://github.com/tvergara/tracr-injection
https://github.com/tvinith/bert
https://github.com/twobooks/intro-aws-training
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/txsun1997/metric-fairness
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/tychen-SJTU/MECD-Benchmark
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
https://github.com/tyroneli/cua_o3d
https://github.com/tyxr/bert
https://github.com/u7javed/Transformer-Multi-Language-Translator
https://github.com/uabinf/nlp-fall-2019-project-anuradha_shinjitha
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/uabinf/nlp-fall-2019-project-shuhui_shuqu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uakarsh/latr
https://github.com/ucinlp/bibere
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
https://github.com/uclnlp/jack
https://github.com/udnet96/BART-various-finetune
https://github.com/uds-lsv/pypremise
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
https://github.com/ufal/multilexnorm2021
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/ufal/wembedding_service
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ukplab/arxiv2025-inherent-limits-plms
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/um2ii/openjphpy
https://github.com/uma-pi1/OPIEC-pipeline
https://github.com/uma-pi1/minie
https://github.com/umass-foundation-model/3d-llm
https://github.com/umass-foundation-model/genome
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.