Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:10000
loss:SpladeLoss
loss:SparseMarginMSELoss
loss:FlopsLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/splade-co-condenser-marco-greedy-msmarco-hard-negatives-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/splade-co-condenser-marco-greedy-msmarco-hard-negatives-v5 with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("tomaarsen/splade-co-condenser-marco-greedy-msmarco-hard-negatives-v5") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Add exported openvino model 'openvino_model_qint8_quantized.xml'
#6 opened about 1 year ago
by
tomaarsen
Add exported openvino model 'openvino_model.xml'
#5 opened about 1 year ago
by
tomaarsen
Add exported onnx model 'model_O3.onnx'
#4 opened about 1 year ago
by
tomaarsen
Add exported onnx model 'model_O4.onnx'
#3 opened about 1 year ago
by
tomaarsen
Add exported onnx model 'model_qint8_avx512_vnni.onnx'
#2 opened about 1 year ago
by
tomaarsen
Add exported onnx model 'model.onnx'
#1 opened about 1 year ago
by
tomaarsen