Instructions to use claudios/cbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claudios/cbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="claudios/cbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("claudios/cbert") model = AutoModelForMaskedLM.from_pretrained("claudios/cbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa9ad046cf30f29d37a74dfaa2f99a4e73f84505932381de42c6a238736020c2
- Size of remote file:
- 180 MB
- SHA256:
- 6c1202c812ad281d2ec562ed7975347ffd4b9032c0beb4fbb74965cbe64c765f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.