Instructions to use miugod/mbert_trim_ende with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miugod/mbert_trim_ende with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="miugod/mbert_trim_ende")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("miugod/mbert_trim_ende") model = AutoModelForMaskedLM.from_pretrained("miugod/mbert_trim_ende", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fcca6e9fee473a7a99dece9b8357c1a6bb1755f6c63f5111bfa18d24323a8c65
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size 410064792
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