Feature Extraction
Transformers
ONNX
Safetensors
multilingual
bidirectional_pplx_qwen3
sentence-similarity
conteb
contextual-embeddings
custom_code
text-embeddings-inference
Instructions to use perplexity-ai/pplx-embed-context-v1-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use perplexity-ai/pplx-embed-context-v1-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="perplexity-ai/pplx-embed-context-v1-0.6b", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("perplexity-ai/pplx-embed-context-v1-0.6b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
MTEB or other same class models comparision
#7
by adanos - opened
Hello and thanks for the model!
Even taking into account the trust credit you earned by revolutionizing web-search, it's hard to make an opinion about the model quality in blind.
Would you kindly publish MTEB results (or, maybe even publish results to MTEB arena), so it would be obvious how exactly your model is different from, for example jina-embeddings-v5.
Thanks!