Instructions to use InfiniFlow/bce-embedding-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InfiniFlow/bce-embedding-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InfiniFlow/bce-embedding-base_v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("InfiniFlow/bce-embedding-base_v1") model = AutoModel.from_pretrained("InfiniFlow/bce-embedding-base_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83538cdf18605ed223e4594557b744e69c6633dbc9bf6fa2e7e33b6cb642f446
- Size of remote file:
- 1.11 GB
- SHA256:
- ac079810acaca8a00ac1abef5c5d2e2d746a9b99fdefc98d0bf5031a2748482f
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