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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 241e9c70043d8d737e632cc15add589228f4dce77cd175912d8d13b5e6b6d62f
- Size of remote file:
- 443 MB
- SHA256:
- 55e615bffcd7b36b7ec5f6e51a31edf2b4a3a0554ae4c779f53534cfa826d2d2
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