Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Paper • 2112.07869 • Published
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("kiddothe2b/biomedical-longformer-base")
model = AutoModelForMaskedLM.from_pretrained("kiddothe2b/biomedical-longformer-base")Quick Links
Biomedical Longformer (base)
This is a derivative model based on microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract BERT model developed in the work "Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing" by Tinn et al. (2021). All model parameters where cloned from the original model, while the positional embeddings were extended by cloning the original embeddings multiple times following Beltagy et al. (2020) using a python script similar to this one (https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb).
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kiddothe2b/biomedical-longformer-base")