datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
task891_gap_coreference_resolution
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task891_gap_coreference_resolution
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task891_gap_coreference_resolution.task893_gap_fill_the_blank_coreference_resolution
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task893_gap_fill_the_blank_coreference_resolution
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task893_gap_fill_the_blank_coreference_resolution.task892_gap_reverse_coreference_resolution
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task892_gap_reverse_coreference_resolution
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task892_gap_reverse_coreference_resolution.id_coreference_resolutionWe built Indonesian coreference resolution that solves not only pronoun referenced to proper noun, but also proper noun to proper noun and pronoun to pronoun.
The differences with the available Indonesian coreference resolution lay on the problem scope and features.
We conducted experiments using various features (lexical and shallow syntactic features) such as appositive feature, nearest candidate feature, direct sentence feature, previous and next word feature, and a lexical feature of first person.
We also modified the method to build the training set by selecting the negative examples by cross pairing every single markable that appear between antecedent and anaphor.
Compared with two available methods to build the training set, we conducted experiments using C45 algorithm.
Using 200 news sentences, the best experiment achieved 71.6% F-Measure score.coreference-resolutionflan_combined_task892_gap_reverse_coreference_resolutionflan_combined_task891_gap_coreference_resolutionflan_combined_task893_gap_fill_the_blank_coreference_resolutionpronominal_coreference_resolutionmultimodal_dialogue_coreference_resolution
멀티 모달 대화 모델을 위한 패션 지식 대화 데이터 셋 :복합대화 연구용 데이터셋 V.2 (데이터)
목적 및 소개
목적 : 텍스트로 이루어진 대화 뿐만 아니라 이미지까지 포함된 대화도 언어모델이 처리할 수 있도록하는 것이 목적.
도메인 : 이미지와 텍스트 모두에 대한 이해가 있어야 대화가 가능한 패션으로 도메인을 설정.
대화 내용 : 패션 주제를 정해 놓고 이에대해 system과 user가 서로 대화를 나누는 내용으로 구성. 주로 user가 패션에 대한 지식이나 이미지를 요청하고 system이 그에대한 답변으로 관련 지식이나 이미지를 찾아주는 형태를 가짐.
특징
의도 및 감정 라벨링 : user 및 system의 발화와 함께 발화가 가지는 의도 (intent), 감정 (sentiment)을 함께 라벨링함.
패션 속성 리스트 라벨링 : user가 어떤 종류의 패션 이미지를 원하는지 패션… See the full description on the dataset page: https://huggingface.co/datasets/KETI-NLP/multimodal_dialogue_coreference_resolution.Korean-CoreferenceResolution