bionlp
Datasets
All datasets matching “bionlp”MedThinkVQA
MedThinkVQA
MedThinkVQA is an expert-annotated benchmark for multi-image diagnostic reasoning in radiology. Unlike prior medical VQA benchmarks that typically contain at most one image per case, MedThinkVQA requires models to extract evidence from each image, integrate cross-view information, and perform differential-diagnosis reasoning.
Links
GitHub: https://github.com/benluwang/MedThinkVQA
Leaderboard: https://benluwang.github.io/MedThinkVQA/
Submission Guide:… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/MedThinkVQA.bionlp2004[BioNLP2004 NER dataset](https://aclanthology.org/W04-1213.pdf)bionlp_st_2013_cgthe Cancer Genetics (CG) is a event extraction task and a main task of the BioNLP Shared Task (ST) 2013.
The CG task is an information extraction task targeting the recognition of events in text,
represented as structured n-ary associations of given physical entities. In addition to
addressing the cancer domain, the CG task is differentiated from previous event extraction
tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple
levels of biological organization, ranging from the molecular through the cellular and organ
levels up to whole organisms. Final test set submissions were accepted from six teamsNoteAid-READMEThe Datasets contains the jargon terms, lay definitions, general definitions for different stages in our REAME pipeline. To comply with fair use of law~\footnote{\url{https://www.copyright.gov/fair-use/}}, We used GPT-3.5 to paraphrase the lay definitions as shown in synthetic_data_creation.ipynb. We used GPT-4o-mini to paraphrase the EHRs as shown in synthetic_EHR_creation.ipynb. We asked the LLM(gpt-4o-mimi) to edit the original sentence but make sure to keep the main terms unchanged. Check… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/NoteAid-README.bioinstruct
Dataset Card for BioInstruct
GitHub repo: https://github.com/bio-nlp/BioInstruct
Dataset Summary
BioInstruct is a dataset of 25k instructions and demonstrations generated by OpenAI's GPT-4 engine in July 2023.
This instruction data can be used to conduct instruction-tuning for language models (e.g. Llama) and make the language model follow biomedical instruction better.
Improvements of Llama on 9 common BioMedical tasks are shown in the result section.
Taking… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/bioinstruct.bionlp_st_2011_geThe BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical
documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE.
The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein"
without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components,
are not differentiated from each other, and their type is given as "Entity".