bge-large-en-v1.5-quant
DeepSparse is able to improve latency performance on a 10 core laptop by 4.8X and up to 3.5X on a 16 core AWS instance.
Usage
This is the quantized (INT8) ONNX variant of the bge-large-en-v1.5 embeddings model accelerated with Sparsify for quantization and DeepSparseSentenceTransformers for inference.
pip install -U deepsparse-nightly[sentence_transformers]
from deepsparse.sentence_transformers import DeepSparseSentenceTransformer
model = DeepSparseSentenceTransformer('neuralmagic/bge-large-en-v1.5-quant', export=False)
# Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
# Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
# Print the embeddings
for sentence, embedding in zip(sentences, embeddings):
print("Sentence:", sentence)
print("Embedding:", embedding.shape)
print("")
For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported75.537
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported38.306
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported69.428
- cos_sim_pearson on MTEB BIOSSEStest set self-reported89.273
- cos_sim_spearman on MTEB BIOSSEStest set self-reported88.365
- euclidean_pearson on MTEB BIOSSEStest set self-reported86.831
- euclidean_spearman on MTEB BIOSSEStest set self-reported87.562
- manhattan_pearson on MTEB BIOSSEStest set self-reported86.593
- manhattan_spearman on MTEB BIOSSEStest set self-reported87.707
- cos_sim_pearson on MTEB SICK-Rtest set self-reported86.190