Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
Transformers.js
English
bert
mteb
sentence_embedding
feature_extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use shubham-bgi/UAE-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shubham-bgi/UAE-Large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shubham-bgi/UAE-Large") 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 shubham-bgi/UAE-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="shubham-bgi/UAE-Large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("shubham-bgi/UAE-Large") model = AutoModel.from_pretrained("shubham-bgi/UAE-Large", device_map="auto") - Transformers.js
How to use shubham-bgi/UAE-Large with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'shubham-bgi/UAE-Large'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10a77ae5973c63a2b1eb5c6cb6e4a86d9be6bcb45c2af29e85d88a6e9961d0e1
- Size of remote file:
- 135 Bytes
- SHA256:
- 2b55399b31b1dec003f7047bfe2a3508a1c16c7cc1f61e962f09903fbb0add63
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