After converting the hugging face model "jinaai/jina-embeddings-v3" via djl-convert and patching the missing model type to "xml-roberta" it still barf on the unknown position embedding type.
Rust conversion didn't crash but didn't yield an usable model.
PyTorch crashed
OnnxRuntime crashed
version of djl-convert tested : 0.30.0 and 0.31.0
Will this change the current api? : absolutely no idea.
Who will benefit from this enhancement?
any person using xml-roberta with "rotary" position embedding type
also this model is VERY efficient at embedding multilanguages and cross language semantic, i tested it on 20k docs on 13 different languages and the semantic matching was awesome, it'll be very good to support it.
References
works using the following code on python langchain.
from langchain_huggingface import HuggingFaceEmbeddings
- [blurb of the model](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/)
- [hugging face model card](https://huggingface.co/jinaai/jina-embeddings-v3)
i'm pretty new on AI models so feel free to fix any of my mistakes :)
Description
After converting the hugging face model "jinaai/jina-embeddings-v3" via djl-convert and patching the missing model type to "xml-roberta" it still barf on the unknown position embedding type.
version of djl-convert tested : 0.30.0 and 0.31.0
Will this change the current api? : absolutely no idea.
Who will benefit from this enhancement? any person using xml-roberta with "rotary" position embedding type
also this model is VERY efficient at embedding multilanguages and cross language semantic, i tested it on 20k docs on 13 different languages and the semantic matching was awesome, it'll be very good to support it.
References
model_name = "jinaai/jina-embeddings-v3" model_kwargs = {"device": "cuda","trust_remote_code":True}
hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)