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:
- 781b78ed689bd43a84d586faaafbc1d630744bae559fa039c8b140c68864ae4c
- Size of remote file:
- 3.06 kB
- SHA256:
- 4711a71b9186331cd1e18fac6f3b227f10b844f6ca8c1166fc919684598d857a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.