--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 56.314104914464366 energy_consumed: 0.14487732225320263 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.379 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 4 type: NanoMSMARCO_4 metrics: - type: cosine_accuracy@1 value: 0.02 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.12 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.18 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.26 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.02 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.039999999999999994 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.036000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.026000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.26 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.13103120560180764 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.09107936507936508 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.10057358250385884 name: Cosine Map@100 - type: query_active_dims value: 4.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9990234375 name: Query Sparsity Ratio - type: corpus_active_dims value: 4.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9990234375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 4 type: NanoNQ_4 metrics: - type: cosine_accuracy@1 value: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.16 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.26 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.026000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.16 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.24 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1617581884859466 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.13905555555555554 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1454920368793091 name: Cosine Map@100 - type: query_active_dims value: 4.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9990234375 name: Query Sparsity Ratio - type: corpus_active_dims value: 4.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9990234375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 4 type: NanoBEIR_mean_4 metrics: - type: cosine_accuracy@1 value: 0.060000000000000005 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.14 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.19 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.26 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.060000000000000005 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.038000000000000006 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.026000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.060000000000000005 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.185 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.25 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.14639469704387714 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.11506746031746032 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.12303280969158396 name: Cosine Map@100 - type: query_active_dims value: 4.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9990234375 name: Query Sparsity Ratio - type: corpus_active_dims value: 4.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9990234375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 16 type: NanoMSMARCO_16 metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.32 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.44 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.62 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.08800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.062 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.32 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.44 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.62 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.35227434410844155 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.26915873015873015 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2834889322403155 name: Cosine Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 16 type: NanoNQ_16 metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.32 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.42 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.54 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.084 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.054000000000000006 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.31 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.51 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.31588504937958484 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.25840476190476186 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.26639173210026346 name: Cosine Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 16 type: NanoBEIR_mean_16 metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.32 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.43 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5800000000000001 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.08600000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.058 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.315 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.42000000000000004 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.565 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33407969674401317 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.263781746031746 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.27494033217028946 name: Cosine Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 64 type: NanoMSMARCO_64 metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.74 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.78 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14800000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.42 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.74 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.78 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5989097939719981 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5405238095238094 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5485629711673361 name: Cosine Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 64 type: NanoNQ_64 metrics: - type: cosine_accuracy@1 value: 0.36 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.58 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.74 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.78 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15200000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.34 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.54 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.68 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.73 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5401684637852635 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4945238095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4792528475589284 name: Cosine Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 64 type: NanoBEIR_mean_64 metrics: - type: cosine_accuracy@1 value: 0.39 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.59 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.74 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.78 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.39 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.15000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.38 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5700000000000001 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.71 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.755 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5695391288786308 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5175238095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5139079093631322 name: Cosine Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: cosine_accuracy@1 value: 0.44 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.44 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.136 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.44 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.62 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.68 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.82 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6219451051635295 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5601111111111111 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5703043330639237 name: Cosine Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 256 type: NanoNQ_256 metrics: - type: cosine_accuracy@1 value: 0.56 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.72 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.78 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.56 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.092 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.54 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.67 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.72 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.82 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6833794556448974 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6571349206349205 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6380047784658768 name: Cosine Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: cosine_accuracy@1 value: 0.5 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6699999999999999 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.73 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.84 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14800000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.087 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.49 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.645 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.82 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6526622804042135 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6086230158730158 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6041545557649002 name: Cosine Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** CSR Sparse Encoder - **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions) - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-cos-sim-scale-20-gamma-1") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.7062, 0.2414, 0.2065]]) ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_4` and `NanoNQ_4` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 4 } ``` | Metric | NanoMSMARCO_4 | NanoNQ_4 | |:----------------------|:--------------|:-----------| | cosine_accuracy@1 | 0.02 | 0.1 | | cosine_accuracy@3 | 0.12 | 0.16 | | cosine_accuracy@5 | 0.18 | 0.2 | | cosine_accuracy@10 | 0.26 | 0.26 | | cosine_precision@1 | 0.02 | 0.1 | | cosine_precision@3 | 0.04 | 0.0533 | | cosine_precision@5 | 0.036 | 0.04 | | cosine_precision@10 | 0.026 | 0.026 | | cosine_recall@1 | 0.02 | 0.1 | | cosine_recall@3 | 0.12 | 0.16 | | cosine_recall@5 | 0.18 | 0.19 | | cosine_recall@10 | 0.26 | 0.24 | | **cosine_ndcg@10** | **0.131** | **0.1618** | | cosine_mrr@10 | 0.0911 | 0.1391 | | cosine_map@100 | 0.1006 | 0.1455 | | query_active_dims | 4.0 | 4.0 | | query_sparsity_ratio | 0.999 | 0.999 | | corpus_active_dims | 4.0 | 4.0 | | corpus_sparsity_ratio | 0.999 | 0.999 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_4` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nq" ], "max_active_dims": 4 } ``` | Metric | Value | |:----------------------|:-----------| | cosine_accuracy@1 | 0.06 | | cosine_accuracy@3 | 0.14 | | cosine_accuracy@5 | 0.19 | | cosine_accuracy@10 | 0.26 | | cosine_precision@1 | 0.06 | | cosine_precision@3 | 0.0467 | | cosine_precision@5 | 0.038 | | cosine_precision@10 | 0.026 | | cosine_recall@1 | 0.06 | | cosine_recall@3 | 0.14 | | cosine_recall@5 | 0.185 | | cosine_recall@10 | 0.25 | | **cosine_ndcg@10** | **0.1464** | | cosine_mrr@10 | 0.1151 | | cosine_map@100 | 0.123 | | query_active_dims | 4.0 | | query_sparsity_ratio | 0.999 | | corpus_active_dims | 4.0 | | corpus_sparsity_ratio | 0.999 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_16` and `NanoNQ_16` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 16 } ``` | Metric | NanoMSMARCO_16 | NanoNQ_16 | |:----------------------|:---------------|:-----------| | cosine_accuracy@1 | 0.14 | 0.14 | | cosine_accuracy@3 | 0.32 | 0.32 | | cosine_accuracy@5 | 0.44 | 0.42 | | cosine_accuracy@10 | 0.62 | 0.54 | | cosine_precision@1 | 0.14 | 0.14 | | cosine_precision@3 | 0.1067 | 0.1067 | | cosine_precision@5 | 0.088 | 0.084 | | cosine_precision@10 | 0.062 | 0.054 | | cosine_recall@1 | 0.14 | 0.14 | | cosine_recall@3 | 0.32 | 0.31 | | cosine_recall@5 | 0.44 | 0.4 | | cosine_recall@10 | 0.62 | 0.51 | | **cosine_ndcg@10** | **0.3523** | **0.3159** | | cosine_mrr@10 | 0.2692 | 0.2584 | | cosine_map@100 | 0.2835 | 0.2664 | | query_active_dims | 16.0 | 16.0 | | query_sparsity_ratio | 0.9961 | 0.9961 | | corpus_active_dims | 16.0 | 16.0 | | corpus_sparsity_ratio | 0.9961 | 0.9961 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_16` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nq" ], "max_active_dims": 16 } ``` | Metric | Value | |:----------------------|:-----------| | cosine_accuracy@1 | 0.14 | | cosine_accuracy@3 | 0.32 | | cosine_accuracy@5 | 0.43 | | cosine_accuracy@10 | 0.58 | | cosine_precision@1 | 0.14 | | cosine_precision@3 | 0.1067 | | cosine_precision@5 | 0.086 | | cosine_precision@10 | 0.058 | | cosine_recall@1 | 0.14 | | cosine_recall@3 | 0.315 | | cosine_recall@5 | 0.42 | | cosine_recall@10 | 0.565 | | **cosine_ndcg@10** | **0.3341** | | cosine_mrr@10 | 0.2638 | | cosine_map@100 | 0.2749 | | query_active_dims | 16.0 | | query_sparsity_ratio | 0.9961 | | corpus_active_dims | 16.0 | | corpus_sparsity_ratio | 0.9961 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_64` and `NanoNQ_64` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 64 } ``` | Metric | NanoMSMARCO_64 | NanoNQ_64 | |:----------------------|:---------------|:-----------| | cosine_accuracy@1 | 0.42 | 0.36 | | cosine_accuracy@3 | 0.6 | 0.58 | | cosine_accuracy@5 | 0.74 | 0.74 | | cosine_accuracy@10 | 0.78 | 0.78 | | cosine_precision@1 | 0.42 | 0.36 | | cosine_precision@3 | 0.2 | 0.2 | | cosine_precision@5 | 0.148 | 0.152 | | cosine_precision@10 | 0.078 | 0.082 | | cosine_recall@1 | 0.42 | 0.34 | | cosine_recall@3 | 0.6 | 0.54 | | cosine_recall@5 | 0.74 | 0.68 | | cosine_recall@10 | 0.78 | 0.73 | | **cosine_ndcg@10** | **0.5989** | **0.5402** | | cosine_mrr@10 | 0.5405 | 0.4945 | | cosine_map@100 | 0.5486 | 0.4793 | | query_active_dims | 64.0 | 64.0 | | query_sparsity_ratio | 0.9844 | 0.9844 | | corpus_active_dims | 64.0 | 64.0 | | corpus_sparsity_ratio | 0.9844 | 0.9844 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_64` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nq" ], "max_active_dims": 64 } ``` | Metric | Value | |:----------------------|:-----------| | cosine_accuracy@1 | 0.39 | | cosine_accuracy@3 | 0.59 | | cosine_accuracy@5 | 0.74 | | cosine_accuracy@10 | 0.78 | | cosine_precision@1 | 0.39 | | cosine_precision@3 | 0.2 | | cosine_precision@5 | 0.15 | | cosine_precision@10 | 0.08 | | cosine_recall@1 | 0.38 | | cosine_recall@3 | 0.57 | | cosine_recall@5 | 0.71 | | cosine_recall@10 | 0.755 | | **cosine_ndcg@10** | **0.5695** | | cosine_mrr@10 | 0.5175 | | cosine_map@100 | 0.5139 | | query_active_dims | 64.0 | | query_sparsity_ratio | 0.9844 | | corpus_active_dims | 64.0 | | corpus_sparsity_ratio | 0.9844 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_256` and `NanoNQ_256` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` | Metric | NanoMSMARCO_256 | NanoNQ_256 | |:----------------------|:----------------|:-----------| | cosine_accuracy@1 | 0.44 | 0.56 | | cosine_accuracy@3 | 0.62 | 0.72 | | cosine_accuracy@5 | 0.68 | 0.78 | | cosine_accuracy@10 | 0.82 | 0.86 | | cosine_precision@1 | 0.44 | 0.56 | | cosine_precision@3 | 0.2067 | 0.24 | | cosine_precision@5 | 0.136 | 0.16 | | cosine_precision@10 | 0.082 | 0.092 | | cosine_recall@1 | 0.44 | 0.54 | | cosine_recall@3 | 0.62 | 0.67 | | cosine_recall@5 | 0.68 | 0.72 | | cosine_recall@10 | 0.82 | 0.82 | | **cosine_ndcg@10** | **0.6219** | **0.6834** | | cosine_mrr@10 | 0.5601 | 0.6571 | | cosine_map@100 | 0.5703 | 0.638 | | query_active_dims | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | | corpus_sparsity_ratio | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nq" ], "max_active_dims": 256 } ``` | Metric | Value | |:----------------------|:-----------| | cosine_accuracy@1 | 0.5 | | cosine_accuracy@3 | 0.67 | | cosine_accuracy@5 | 0.73 | | cosine_accuracy@10 | 0.84 | | cosine_precision@1 | 0.5 | | cosine_precision@3 | 0.2233 | | cosine_precision@5 | 0.148 | | cosine_precision@10 | 0.087 | | cosine_recall@1 | 0.49 | | cosine_recall@3 | 0.645 | | cosine_recall@5 | 0.7 | | cosine_recall@10 | 0.82 | | **cosine_ndcg@10** | **0.6527** | | cosine_mrr@10 | 0.6086 | | cosine_map@100 | 0.6042 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who played the father in papa don't preach | Alex McArthur Alex McArthur (born March 6, 1957) is an American actor. | | where was the location of the battle of hastings | Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory. | | how many puppies can a dog give birth to | Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22] | * Loss: [CSRLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=20.0, similarity_fct='cos_sim')" } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | where is the tiber river located in italy | Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks. | | what kind of car does jay gatsby drive | Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry. | | who sings if i can dream about you | I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1] | * Loss: [CSRLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=20.0, similarity_fct='cos_sim')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_4_cosine_ndcg@10 | NanoNQ_4_cosine_ndcg@10 | NanoBEIR_mean_4_cosine_ndcg@10 | NanoMSMARCO_16_cosine_ndcg@10 | NanoNQ_16_cosine_ndcg@10 | NanoBEIR_mean_16_cosine_ndcg@10 | NanoMSMARCO_64_cosine_ndcg@10 | NanoNQ_64_cosine_ndcg@10 | NanoBEIR_mean_64_cosine_ndcg@10 | NanoMSMARCO_256_cosine_ndcg@10 | NanoNQ_256_cosine_ndcg@10 | NanoBEIR_mean_256_cosine_ndcg@10 | |:----------:|:-------:|:-------------:|:---------------:|:----------------------------:|:-----------------------:|:------------------------------:|:-----------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------:|:-------------------------------:|:------------------------------:|:-------------------------:|:--------------------------------:| | -1 | -1 | - | - | 0.0850 | 0.1222 | 0.1036 | 0.4256 | 0.3267 | 0.3761 | 0.5827 | 0.5843 | 0.5835 | 0.5987 | 0.7005 | 0.6496 | | 0.0646 | 100 | 0.6568 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.561 | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.1939** | **300** | **0.5248** | **0.4118** | **0.131** | **0.1618** | **0.1464** | **0.3523** | **0.3159** | **0.3341** | **0.5989** | **0.5402** | **0.5695** | **0.6219** | **0.6834** | **0.6527** | | 0.2586 | 400 | 0.4995 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.484 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.4773 | 0.3882 | 0.2023 | 0.1465 | 0.1744 | 0.3397 | 0.3617 | 0.3507 | 0.5710 | 0.5702 | 0.5706 | 0.6091 | 0.6610 | 0.6351 | | 0.4525 | 700 | 0.464 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.4529 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.4524 | 0.3753 | 0.1495 | 0.1179 | 0.1337 | 0.3072 | 0.3473 | 0.3272 | 0.5718 | 0.5525 | 0.5622 | 0.6084 | 0.6660 | 0.6372 | | 0.6464 | 1000 | 0.4486 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.4349 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.4382 | 0.3690 | 0.1815 | 0.0924 | 0.1370 | 0.3328 | 0.3493 | 0.3410 | 0.5311 | 0.5480 | 0.5396 | 0.6086 | 0.6486 | 0.6286 | | 0.8403 | 1300 | 0.4394 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.427 | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.4312 | 0.3666 | 0.1746 | 0.1350 | 0.1548 | 0.3395 | 0.2952 | 0.3174 | 0.5511 | 0.5252 | 0.5381 | 0.6162 | 0.6494 | 0.6328 | | -1 | -1 | - | - | 0.1310 | 0.1618 | 0.1464 | 0.3523 | 0.3159 | 0.3341 | 0.5989 | 0.5402 | 0.5695 | 0.6219 | 0.6834 | 0.6527 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.145 kWh - **Carbon Emitted**: 0.056 kg of CO2 - **Hours Used**: 0.379 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```