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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from fresha/e5-large-v2-endpoint: direct link, hf CLI and curl.
- Browser
- Download file 387 Bytes
-
https://huggingface.co/fresha/e5-large-v2-endpoint/resolve/main/modules.json
- Command line
-
hf download hf://fresha/e5-large-v2-endpoint/modules.json
-
curl -L -o modules.json https://huggingface.co/fresha/e5-large-v2-endpoint/resolve/main/modules.json
387 Bytes
| [ | |
| { | |
| "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" | |
| } | |
| ] |