Instructions to use almanach/camembertv2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camembertv2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="almanach/camembertv2-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("almanach/camembertv2-base") model = AutoModelForMaskedLM.from_pretrained("almanach/camembertv2-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01a8fd8586b3081b96de6baea376e2cee7bf06e3b1ab69884410551b4d83073c
- Size of remote file:
- 447 MB
- SHA256:
- 632617234a6fa64239b427be9a5325c930a26f3f3c11ede90d3040cf53d6b6dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.