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
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- jhu-clsp/mmbert-pretrain-p2-fineweb2-remaining
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pipeline_tag: fill-mask
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# mmBERT: A Modern Multilingual Encoder
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- jhu-clsp/mmbert-pretrain-p2-fineweb2-remaining
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pipeline_tag: fill-mask
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library_name: transformers
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# mmBERT: A Modern Multilingual Encoder
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