Instructions to use avacaondata/roberta-large-biomedical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avacaondata/roberta-large-biomedical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="avacaondata/roberta-large-biomedical")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("avacaondata/roberta-large-biomedical") model = AutoModelForMaskedLM.from_pretrained("avacaondata/roberta-large-biomedical") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66920c990c3dcfdc1371a05846cb3e71735b8ac149d3aa83c419e3c2b7888af7
- Size of remote file:
- 2.93 kB
- SHA256:
- 8092c8c9a61908127e0c554a01ba25ec7bc19d45a3b4eb9e7bfaf43e23479c61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.