Instructions to use mstaron/wolfBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mstaron/wolfBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mstaron/wolfBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mstaron/wolfBERTa") model = AutoModelForMaskedLM.from_pretrained("mstaron/wolfBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25f7ab7ee1eaa0b8611205308209fc6b1485ad49287b7d0b312258455b96d2c0
- Size of remote file:
- 334 MB
- SHA256:
- d76d55d5651271c9525c7185bf071d7ca73c932b6ad086744c7d7ebab2320bda
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