https://github.com/masih4/Skin-lesion-segmentation-effects-of-the-classification-perfromnce
공식 코드The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
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/masoud-monajati/BERTHop
공식 코드BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis
https://github.com/masterai-eam/darwin
공식 코드DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
https://github.com/maszhongming/unieval
공식 코드Towards a Unified Multi-Dimensional Evaluator for Text Generation
https://github.com/mataney/APES
공식 코드Question Answering as an Automatic Evaluation Metric for News Article Summarization
https://github.com/mataney/APES-optimizer
공식 코드Question Answering as an Automatic Evaluation Metric for News Article Summarization
https://github.com/matejulcar/slot5-tools
공식 코드Sequence to sequence pretraining for a less-resourced Slovenian language
https://github.com/mateuszmalinowski/visual_turing_test-tutorial
공식 코드Ask Your Neurons: A Deep Learning Approach to Visual Question Answering
https://github.com/mathllm/mathcoder
공식 코드MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
https://github.com/matnlp/smedbert
공식 코드https://github.com/matthewdm0816/bridgeqa
공식 코드Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA
https://github.com/mattiaspaul/TernaryNet
공식 코드https://github.com/matyasbohacek/sign-language-dictionary
공식 코드https://github.com/maum-ai/koffvqa
공식 코드https://github.com/mausamsion/mfbe
공식 코드MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval
https://github.com/maximrub/diseases-risk-factors
공식 코드Automatic Extraction of Disease Risk Factors from Medical Publications
https://github.com/maxwellyin/mdaqa
공식 코드Source-Free Domain Adaptation for Question Answering with Masked Self-training
https://github.com/mayer123/udt-qa
공식 코드Open Domain Question Answering with A Unified Knowledge Interface
https://github.com/mayhugotong/videoinsta
공식 코드VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs
https://github.com/mbzuai-nlp/lamini-lm
공식 코드LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
https://github.com/mbzuai-nlp/multichoice2extractiveqa
공식 코드From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
https://github.com/mbzuai-nlp/urdufactcheck
공식 코드UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking
https://github.com/mbzuai-oryx/ALM-Bench
공식 코드All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
https://github.com/mbzuai-oryx/bimedix
공식 코드https://github.com/mbzuai-oryx/bimedix2
공식 코드BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities
https://github.com/mbzuai-oryx/geochat
공식 코드GeoChat: Grounded Large Vision-Language Model for Remote Sensing
https://github.com/mbzuai-oryx/groundingLMM
공식 코드https://github.com/mbzuai-oryx/video-chatgpt
공식 코드Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
https://github.com/mbzuai-oryx/video-llava
공식 코드https://github.com/mbzuai-oryx/videogpt-plus
공식 코드VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
https://github.com/mcgill-nlp/chase
공식 코드How to Get Your LLM to Generate Challenging Problems for Evaluation
https://github.com/mcgill-nlp/instruct-qa
공식 코드Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
https://github.com/mcgill-nlp/logicalknowedit
공식 코드Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
https://github.com/mcgill-nlp/retriever-lm-reasoning
공식 코드https://github.com/mcubelab/pdproc
공식 코드Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data
https://github.com/md-mohaiminul/BIMBA
공식 코드BIMBA: Selective-Scan Compression for Long-Range Video Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.