Feature Extraction
sentence-transformers
Safetensors
Model2Vec
static-embeddings
decision-engine
jev
lf2
2bit-quantization
cpu-optimized
Instructions to use VTXAI/VTX-JEV-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use VTXAI/VTX-JEV-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VTXAI/VTX-JEV-2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Model2Vec
How to use VTXAI/VTX-JEV-2 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("VTXAI/VTX-JEV-2") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download modules.json from VTXAI/VTX-JEV-2: direct link, hf CLI and curl.
- Browser
- Download file 278 Bytes
-
https://huggingface.co/VTXAI/VTX-JEV-2/resolve/main/modules.json
- Command line
-
hf download hf://VTXAI/VTX-JEV-2/modules.json
-
curl -L -o modules.json https://huggingface.co/VTXAI/VTX-JEV-2/resolve/main/modules.json
278 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": ".", | |
| "type": "sentence_transformers.models.StaticEmbedding" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |