https://github.com/nyk510/scdv-python
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
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/nyu-dl/dl4dial-bayesian-calibration
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
https://github.com/nyu-dl/dl4ir-searchQA
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
https://github.com/nyu-mll/squality
https://github.com/obi-ml-public/ehr_deidentification
https://github.com/obss/disgem
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
https://github.com/octanove/grammartagger
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/octanove/shiba
https://github.com/ofa-sys/expertllama
https://github.com/ohadrubin/rpt
https://github.com/okanvk/ENELPI---Soru-Cevap-Sistemi
https://github.com/okanvk/Medical-Electra
PubMedQA: A Dataset for Biomedical Research Question Answering
https://github.com/okanvk/Medical-Specific-Electra-Med-Electra-
PubMedQA: A Dataset for Biomedical Research Question Answering
https://github.com/okanvk/Question-Answering-Project
Latent Retrieval for Weakly Supervised Open Domain Question Answering
https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset
https://github.com/okkteam/Transformer-Transducer
https://github.com/olabknbit/FactChecking
https://github.com/ololo123321/maestro
https://github.com/om00839/machine-suneung
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/oneflow-inc/libai
https://github.com/onue5/VideoQA
https://github.com/open-compass/opencompass
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
https://github.com/open-dataflow/rare
PubMedQA: A Dataset for Biomedical Research Question Answering
https://github.com/open-finance-lab/finlora
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmpose
TRB: A Novel Triplet Representation for Understanding 2D Human Body
https://github.com/open-vision-language/infoseek
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
https://github.com/openaccess-ai-collective/axolotl
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
https://github.com/openai/finetune-transformer-lm
https://github.com/openai/gpt-2
https://github.com/openai/minif2f
Measuring Mathematical Problem Solving With the MATH Dataset
https://github.com/openbiolink/promptsource
https://github.com/openbmb/minicpm-v
https://github.com/opendatalab/charm
https://github.com/openeventdata/PLOVER
Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks
https://github.com/opengptx/lm-evaluation-harness
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.