Instructions to use mfine/embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mfine/embedding-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mfine/embedding-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mfine/embedding-model") model = AutoModel.from_pretrained("mfine/embedding-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e7c5690fe09738961e588daf93a0d29857be30d6dd9e5b70ee8c0d26954f1df
- Size of remote file:
- 1.34 GB
- SHA256:
- 4fe281702fb8f8eea2ad321aa90a1da772db8f023c2f87a9d52e8351f7102e5e
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