Instructions to use lgrobol/BERTrade-camemBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lgrobol/BERTrade-camemBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lgrobol/BERTrade-camemBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lgrobol/BERTrade-camemBERT") model = AutoModelForMaskedLM.from_pretrained("lgrobol/BERTrade-camemBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57d2beb07207a35b55bc6eedd16cd8c11992d807e423fa5a2fbc8eac5c7abde0
- Size of remote file:
- 811 kB
- SHA256:
- 988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
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