Instructions to use austin/Austin-MeDeBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use austin/Austin-MeDeBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="austin/Austin-MeDeBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("austin/Austin-MeDeBERTa") model = AutoModelForMaskedLM.from_pretrained("austin/Austin-MeDeBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update README.md
Christopher McMaster2197dd8
-
add tokenizer
Christopher McMastere8f87f2 -
update model card README.md
Christopher McMaster4e125cb -
End of training
Christopher McMaster9330bef -
Training in progress, step 360000
Christopher McMastera10d2cb
-
Training in progress, step 320000
Christopher McMaster99b0f3c -
Training in progress, step 280000
Christopher McMaster4c74f75
-
Training in progress, step 240000
Christopher McMasterb4debd4 -
Training in progress, step 200000
Christopher McMasteraa095f0 -
Training in progress, step 160000
Christopher McMastere154bab
-
Training in progress, step 120000
Christopher McMaster37a52da -
Training in progress, step 80000
Christopher McMastera56633e
-
Training in progress, step 40000
Christopher McMasterb07d922 -
initial commit
95f379d