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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 619db987a9d51312175d772334e985175a45f71e045c14dc5b92037f0be88ccb
- Size of remote file:
- 273 MB
- SHA256:
- 1a1f0f45c5d98baf837b990c0ef885cecdf777f45dc738ea0e824f7ba2797dfc
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