Instructions to use wanderer2k1/e5-base-mnr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wanderer2k1/e5-base-mnr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wanderer2k1/e5-base-mnr")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("wanderer2k1/e5-base-mnr") model = AutoModel.from_pretrained("wanderer2k1/e5-base-mnr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0069817c8c6359f9932cfa3df61a32e6de5740cf2c57a8860e9d84bb134c3b0
- Size of remote file:
- 1.11 GB
- SHA256:
- 2f366b6e9581faa48f3ac480e7bf35a324cb65be43a78a713b9af428dfc882a3
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