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
File size: 387 Bytes
cc7480b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.models.Normalize"
}
] |