Instructions to use microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract") model = AutoModelForMaskedLM.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#7 opened 2 months ago
by
vigneshwar234
Move it to Biomedical collection
#6 opened over 1 year ago
by
Katcinskiy
Adding `safetensors` variant of this model
#5 opened over 1 year ago
by
SFconvertbot
add model_max_length
#4 opened over 2 years ago
by
alistairewj
Adding `safetensors` variant of this model
#3 opened over 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened over 3 years ago
by
SFconvertbot
Question about vocab size
1
#1 opened about 4 years ago
by
johnsongwx