https://github.com/bionlu-coling2024/biomed-ner-intent_detection
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/birkhoffkiki/gpfm
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
https://github.com/biswa380/t5_multitask_api
https://github.com/bit-ml/anoshift
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bkoch4142/attention-is-all-you-need-paper
https://github.com/blawok/named-entity-recognition
https://github.com/blaze1522/Image-Segmentation-FloodNet
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/bloomsburyai/question-generation
Multi-Perspective Context Matching for Machine Comprehension
https://github.com/blueberryc/pyGAT
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
https://github.com/bojone/tiger
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/boleima/topro
https://github.com/bombdiggity/paper-bag
https://github.com/boschresearch/quite-emnlp24
QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios
https://github.com/bowu/GraphSearch
https://github.com/bplank/teaching-dl4nlp
https://github.com/brainsqueeze/text2vec
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/brandon3964/multimodal-task-vector
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/brandonjaipersaud/piazza-qabot-gpt
Decomposed Prompting to Answer Questions on a Course Discussion Board
https://github.com/brandontrabucco/scaled_dot_product_attention
https://github.com/brightmart/albert_zh
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/brightmart/bert_customized
https://github.com/brightmart/bert_original
https://github.com/brightmart/roberta_zh
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brightmart/text_classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/brmson/dataset-sts
https://github.com/broccolik/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bshantam97/Attention_Based_Networks
https://github.com/btahir/tensorflow-LAMB
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/butsugiri/shape
https://github.com/bwilbertz/kaggle_allen_ai
Moving Beyond the Turing Test with the Allen AI Science Challenge
https://github.com/byahn2/clevr_ref
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/bytedance/neurst
https://github.com/bytetriper/lm4lv
https://github.com/bzhangGo/lrn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.