Feature Extraction
sentence-transformers
ONNX
Safetensors
OpenVINO
Transformers
Transformers.js
English
bert
mteb
sentence_embedding
feature_extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use WhereIsAI/UAE-Large-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use WhereIsAI/UAE-Large-V1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WhereIsAI/UAE-Large-V1") 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] - Transformers
How to use WhereIsAI/UAE-Large-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="WhereIsAI/UAE-Large-V1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("WhereIsAI/UAE-Large-V1") model = AutoModel.from_pretrained("WhereIsAI/UAE-Large-V1") - Transformers.js
How to use WhereIsAI/UAE-Large-V1 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'WhereIsAI/UAE-Large-V1'); - Notebooks
- Google Colab
- Kaggle
How to quantize
#10
by supercharge19 - opened
Is there an example, code, to use quantize this model or is there a quantized version available?
I found this: https://huggingface.co/WhereIsAI/UAE-Large-V1/blob/main/onnx/model_quantized.onnx
But how to use it, please give some example.
@supercharge19 hi, you can use optimum to load the quantized onnx model, as follows:
from optimum.onnxruntime import ORTModelForFeatureExtraction
from optimum.pipelines import pipeline
model = ORTModelForFeatureExtraction.from_pretrained('WhereIsAI/UAE-Large-V1', file_name="onnx/model_quantized.onnx")
extractor = pipeline('feature-extraction', model=model)
output = extractor('hello world')
@supercharge19 hi, you can use optimum to load the quantized onnx model, as follows:
from optimum.onnxruntime import ORTModelForFeatureExtraction from optimum.pipelines import pipeline model = ORTModelForFeatureExtraction.from_pretrained('WhereIsAI/UAE-Large-V1', file_name="onnx/model_quantized.onnx") extractor = pipeline('feature-extraction', model=model) output = extractor('hello world')
Thanks man.