Instructions to use cabrooks/LOGION-50k_wordpiece with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cabrooks/LOGION-50k_wordpiece with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cabrooks/LOGION-50k_wordpiece")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cabrooks/LOGION-50k_wordpiece") model = AutoModelForMaskedLM.from_pretrained("cabrooks/LOGION-50k_wordpiece") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f7db9adea50b6273e17f6e010e507a75537480c8041b30b1ae5c57400dcd39d
- Size of remote file:
- 498 MB
- SHA256:
- 76afbf0ad54e517e22fc3e2119a33b33d6ed1c73d131541cb3635ae3c220ee8e
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