Instructions to use ngwlh/KBioXLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ngwlh/KBioXLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ngwlh/KBioXLM")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ngwlh/KBioXLM") model = AutoModelForTokenClassification.from_pretrained("ngwlh/KBioXLM") - Notebooks
- Google Colab
- Kaggle
KBioXLM
The aligned corpus constructed using the knowledge-anchored method is combined with a multi task training strategy to continue training XLM-R, thus obtaining KBioXLM. It is the first multilingual biomedical pre-trained language model we know that has cross-lingual understanding capabilities in medical domain. It was introduced in the paper KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model and released in this repository.
Model description
KBioXLM model can be fintuned on downstream tasks. The downstream tasks here refer to biomedical cross-lingual understanding tasks, such as biomedical entity recognition, biomedical relationship extraction and biomedical text classification.
Usage
You can follow the prompts below to load our model parameters:
from transformers import RobertaModel
model=RobertaModel.from_pretrained('ngwlh/KBioXLM')
BibTeX entry and citation info
Coming soon.
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