Instructions to use billfass/camembert-base-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use billfass/camembert-base-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="billfass/camembert-base-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("billfass/camembert-base-sentiment-analysis") model = AutoModelForMaskedLM.from_pretrained("billfass/camembert-base-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d07f23a37c03b2e379fb68fe23bb606fe94580505dce438f1da40fd485edda03
- Size of remote file:
- 443 MB
- SHA256:
- ff1a3037dcb617d34e998a4ab7f521ed13aa3cb707aaee0fc70003cfb370b123
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