Instructions to use JulesBelveze/labse-bfloat16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JulesBelveze/labse-bfloat16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="JulesBelveze/labse-bfloat16")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JulesBelveze/labse-bfloat16") model = AutoModel.from_pretrained("JulesBelveze/labse-bfloat16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ab64682c29224b45e329f856c16807e5305144fb9f9042e811b0e6a8a592d27
- Size of remote file:
- 942 MB
- SHA256:
- dc4888d48155c57dc29ed9ecf16897e1bdafda7dd46251963b1ada228573bd76
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