Feature Extraction
sentence-transformers
Safetensors
Transformers.js
Transformers
MLX
English
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use mlx-community/mxbai-embed-large-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlx-community/mxbai-embed-large-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/mxbai-embed-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.js
How to use mlx-community/mxbai-embed-large-v1 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'mlx-community/mxbai-embed-large-v1'); - Transformers
How to use mlx-community/mxbai-embed-large-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mlx-community/mxbai-embed-large-v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mlx-community/mxbai-embed-large-v1") model = AutoModel.from_pretrained("mlx-community/mxbai-embed-large-v1") - MLX
How to use mlx-community/mxbai-embed-large-v1 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mxbai-embed-large-v1 mlx-community/mxbai-embed-large-v1
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/mxbai-embed-large-v1
The Model mlx-community/mxbai-embed-large-v1 was converted to MLX format from mixedbread-ai/mxbai-embed-large-v1 using mlx-lm version 0.0.3.
Use with mlx
pip install mlx-embeddings
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("mlx-community/mxbai-embed-large-v1")
# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds # Normalized embeddings
# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)
print("Similarity matrix between texts:")
print(similarity_matrix)
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Model tree for mlx-community/mxbai-embed-large-v1
Base model
mixedbread-ai/mxbai-embed-large-v1Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported75.045
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported37.736
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported68.927
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported93.840
- ap on MTEB AmazonPolarityClassificationtest set self-reported90.932
- f1 on MTEB AmazonPolarityClassificationtest set self-reported93.830
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported49.184
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported48.742
- map_at_1 on MTEB ArguAnatest set self-reported41.252
- map_at_10 on MTEB ArguAnatest set self-reported57.778
- map_at_100 on MTEB ArguAnatest set self-reported58.233
- map_at_1000 on MTEB ArguAnatest set self-reported58.237
- map_at_3 on MTEB ArguAnatest set self-reported53.450
- map_at_5 on MTEB ArguAnatest set self-reported56.376
- mrr_at_1 on MTEB ArguAnatest set self-reported41.679
- mrr_at_10 on MTEB ArguAnatest set self-reported57.927