Model: tomaarsen/splade-co-condenser-marco-msmarco-hard-negatives-v5
Author: tomaarsen
Pipeline: feature-extraction
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sparse-encoder, sparse, splade, generated_from_trainer, dataset_size:10000, loss:SpladeLoss, loss:SparseMarginMSELoss, loss:FlopsLoss, feature-extraction, en, dataset:tomaarsen/msmarco-Qwen3-Reranker-0.6B, arxiv:1908.10084, arxiv:2205.04733, arxiv:2010.02666, arxiv:2004.05665, base_model:Luyu/co-condenser-marco, base_model:finetune:Luyu/co-condenser-marco, license:apache-2.0, model-index, co2_eq_emissions, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Luyu/co-condenser-marco
Card metadata: language: en
license: apache-2.0
tags: sentence-transformers, sparse-encoder, sparse, splade, generated_from_trainer, dataset_size:10000, loss:SpladeLoss, loss:SparseMarginMSELoss, loss:FlopsLoss
base_model: Luyu/co-condenser-marco
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datasets: tomaarsen/msmarco-Qwen3-Reranker-0.6B
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics: dot_accuracy@1, dot_accuracy@3, dot_accuracy@5, dot_accuracy@10, dot_precision@1, dot_precision@3, dot_precision@5, dot_precision@10, dot_recall@1, dot_recall@3, dot_recall@5, dot_recall@10, dot_ndcg@10, dot_mrr@10, dot_map@100, query_active_dims, query_sparsity_ratio, corpus_active_dims, corpus_sparsity_ratio
co2_eq_emissions: emissions: 33.256828829604686
energy_consumed: 0.08555867690314094
source: codecarbon
training_type: fine-tuning
on_cloud: no
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.276
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index: name: splade-co-condenser-marco trained on MS MARCO hard negatives with distillation
results: task: type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset: name: msmarco eval 1kq 1kd
type: msmarco-eval-1kq-1kd
metrics: type: dot_accuracy@1
value: 0.946
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.981
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.987
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.993
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.946
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.32699999999999996
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19740000000000005
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09930000000000001
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.946
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.981
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.987
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.993
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9716808947138706
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9646123015873015
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9647768153143154
name: Dot Map@100
verified: no, type: query_active_dims
value: 20.368000030517578
name: Query Active Dims
verified: no, type: query_sparsity_ratio
value: 0.9993326780672788
name: Query Sparsity Ratio
verified: no, type: corpus_active_dims
value: 105.83899688720703
name: Corpus Active Dims
verified: no, type: corpus_sparsity_ratio
value: 0.9965323701956881
name: Corpus Sparsity Ratio
verified: no
Likes: 1 | All-time downloads: 37
Created: 2025-08-08 15:37:59