Feature Extraction
sentence-transformers
Safetensors
Transformers.js
Transformers
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use PotatoOff/mxbai-embed-large-safetensors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PotatoOff/mxbai-embed-large-safetensors with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PotatoOff/mxbai-embed-large-safetensors") 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.js
How to use PotatoOff/mxbai-embed-large-safetensors with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'PotatoOff/mxbai-embed-large-safetensors'); - Transformers
How to use PotatoOff/mxbai-embed-large-safetensors with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PotatoOff/mxbai-embed-large-safetensors")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PotatoOff/mxbai-embed-large-safetensors") model = AutoModel.from_pretrained("PotatoOff/mxbai-embed-large-safetensors") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3969367860065158ab33421b027fdcd74f88570c33f37448b6f4286de83b06e4
- Size of remote file:
- 670 MB
- SHA256:
- 36bfa45da00eb762ef9feebe4cff315ec779efadd08da11846bef5ba5b59b8f8
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