Instructions to use cmarkea/distilcamembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmarkea/distilcamembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cmarkea/distilcamembert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cmarkea/distilcamembert-base") model = AutoModelForMaskedLM.from_pretrained("cmarkea/distilcamembert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 512b2d5dd33472f945bfb851f114ba2f345f706371d470e91d157be889f60bf1
- Size of remote file:
- 373 MB
- SHA256:
- c0e478c95bbacfb57d210b453d3be6b44ab158be7f09fda9d502c00a71f3d965
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