Instructions to use jplu/tf-camembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jplu/tf-camembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jplu/tf-camembert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jplu/tf-camembert-base") model = AutoModelForMaskedLM.from_pretrained("jplu/tf-camembert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38302744893bfbd842640be561815d69e043fa9839b3e075a9397474138e00f0
- Size of remote file:
- 545 MB
- SHA256:
- bc744197d804d8f02c90f514fb5ce1506cd3487bcc1190977c9889d8fdcc3ee5
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