alabnii/jmedroberta-base-sentencepiece-vocab50000
https://huggingface.co/alabnii/jmedroberta-base-sentencepiece-vocab50000This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).
Sourced from
- HuggingFace β alabnii/jmedroberta-base-sentencepiece-vocab50000
Related resources
doctolib-lab/doctobert-fr-base
by doctolib-labπ€ Blog | π Paper | π» Code | π FineMed | π©Ί DoctoBERT
This model is a lightweight model pre-trained on SELFIES (Self-Referencing Embedded Strings) representations of molecules. It is trained on 2.7M unique and valid molecules taken from COCONUTDB and ChemBL34, with 7.3M total generated masked examples.
In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.
This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie SkΕodowska-Curie Actionsβ¦