Feature Extraction
ONNX
sentence-transformers
onnxruntime
xlm-roberta
fp16
text-embeddings-inference
Instructions to use thomasht86/deepset-mxbai-embed-de-large-v1-onnx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thomasht86/deepset-mxbai-embed-de-large-v1-onnx-fp16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thomasht86/deepset-mxbai-embed-de-large-v1-onnx-fp16") 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] - Notebooks
- Google Colab
- Kaggle
deepset-mxbai-embed-de-large-v1-onnx-fp16
ONNX FP16 export of mixedbread-ai/deepset-mxbai-embed-de-large-v1.
Usage
import onnxruntime as ort
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("thomasht86/deepset-mxbai-embed-de-large-v1-onnx-fp16")
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
inputs = dict(tokenizer("Your text here", return_tensors="np"))
outputs = session.run(None, inputs)
embeddings = outputs[0]
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