Instructions to use raphaelsty/neural-cherche-sparse-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raphaelsty/neural-cherche-sparse-embed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="raphaelsty/neural-cherche-sparse-embed")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("raphaelsty/neural-cherche-sparse-embed") model = AutoModelForMaskedLM.from_pretrained("raphaelsty/neural-cherche-sparse-embed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fbc66ca080dc373156081089a109c0964dd70822f106d2e773a0efb8a14c582
- Size of remote file:
- 394 kB
- SHA256:
- 2cd1265d36bedccc9ef3e4329d46510010a908257867a21840e46ec8c3a45f9f
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