Instructions to use Oblix/multilingual-e5-small-optimized_ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oblix/multilingual-e5-small-optimized_ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Oblix/multilingual-e5-small-optimized_ONNX")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Oblix/multilingual-e5-small-optimized_ONNX") model = AutoModel.from_pretrained("Oblix/multilingual-e5-small-optimized_ONNX") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Oblix/multilingual-e5-small-optimized_ONNX")
model = AutoModel.from_pretrained("Oblix/multilingual-e5-small-optimized_ONNX")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
https://huggingface.co/elastic/multilingual-e5-small-optimized with ONNX weights to be compatible with Transformers.js.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Oblix/multilingual-e5-small-optimized_ONNX")