Feature Extraction
sentence-transformers
Safetensors
Model2Vec
static-embeddings
moderation
safety
abuse-detection
lf2
2bit-quantization
cpu-optimized
Instructions to use VTXAI/VTX-MOD-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use VTXAI/VTX-MOD-1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VTXAI/VTX-MOD-1") 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-MOD-1 with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("VTXAI/VTX-MOD-1") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from VTXAI/VTX-MOD-1: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/VTXAI/VTX-MOD-1/resolve/main/tokenizer.json
- Command line
-
hf download hf://VTXAI/VTX-MOD-1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/VTXAI/VTX-MOD-1/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- 023e86619c147d8c7d99af18523f96f9616c796a1d0f09fbcd5bc6d7d8472ec3
- Size of remote file:
- 13.6 MB
- SHA256:
- ce9936fce165782c51893821332063897271ba0e81b90b6a41548fcea48bf85e
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