Instructions to use jhu-clsp/mmBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/mmBERT-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/mmBERT-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmBERT-base") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/mmBERT-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44816671aa47215d98d679ff7ffaf1673401ea52451faa11749ffbdf761d52ad
- Size of remote file:
- 17.5 MB
- SHA256:
- 197d4cc5406ee12cc50c8b5511f2393cc32d9db321545979ce041c1199178356
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