Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use fresha/e5-large-v2-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fresha/e5-large-v2-endpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fresha/e5-large-v2-endpoint")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fresha/e5-large-v2-endpoint") model = AutoModel.from_pretrained("fresha/e5-large-v2-endpoint") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c5975281edf5c14867215636f67e5277b53fb3a6a8481d746f99ea2c4f26f3a
- Size of remote file:
- 1.34 GB
- SHA256:
- 7d3fda35853349a026a61027d93bbfd65d7658287a2043f95af3e4397a4e9c5e
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