Embedding Models
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Some embedding models I've trained, finetuned, distilled, converted, or something else entirely • 15 items • Updated
A modified version of Snowflake/snowflake-arctic-embed-m-v2.0, without xformers, so it works on CPU.
from sentence_transformers import SentenceTransformer
import torch
device = torch.device("cpu")
model = SentenceTransformer("cnmoro/snowflake-arctic-embed-m-v2.0-cpu", device=device, trust_remote_code=True)
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported66.687
- f1 on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported55.037
- f1_weighted on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported73.074
- ap on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported18.077
- ap_weighted on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported18.077
- main_score on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported66.687
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported66.194
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported60.854
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/snowflake-arctic-embed-m-v2.0-cpu", trust_remote_code=True) 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]