Instructions to use Jordancole21/sufu-splade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jordancole21/sufu-splade with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jordancole21/sufu-splade")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jordancole21/sufu-splade") model = AutoModelForMaskedLM.from_pretrained("Jordancole21/sufu-splade", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e9ce5c81e4cec353f9a3aa035e78b036a294264681907846e3aff11b16c9276
- Size of remote file:
- 1.42 GB
- SHA256:
- bffe9123467595e43523b2948216b164372444fbc5e2f1eacc8a17a4e680a3d7
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