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") - Inference
- Notebooks
- Google Colab
- Kaggle
safetensors
1
#15 opened 2 months ago
by
bwallima
Adding `safetensors` variant of this model
#14 opened 7 months ago
by
SFconvertbot
Update Idea: Train with Gemma3 Tokenizer?
1
#12 opened 8 months ago
by
mxngjxa
can we have some official training / finetuning recipes for this model ?
3
#11 opened 8 months ago
by
StephennFernandes
feat: Improve mmBERT-base model card with full title, abstract, and updated content
#10 opened 8 months ago
by
nielsr
Adding `safetensors` variant of this model
#9 opened 8 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
👍 1
#5 opened 8 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#4 opened 8 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#3 opened 8 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened 8 months ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened 10 months ago
by
orionweller