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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:4247 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: Perry syndrome is a familial parkinsonism associated with central |
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hypoventilation, mental depression, and weight loss. |
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sentences: |
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- List features of the Perry syndrome. |
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- Which is the main abnormality that arises with Sox9 locus duplication? |
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- Was modafinil tested for schizophrenia treatment? |
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- source_sentence: Yes. HDAC1 is required for GATA-1 transcription activity, global |
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chromatin occupancy and hematopoiesis. |
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sentences: |
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- Is HDAC1 required for GATA-1 transcriptional activity? |
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- Which cells are affected in radiation-induced leukemias? |
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- Is phospholamban phosphorylated by Protein kinase A? |
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- source_sentence: Long noncoding RNAs (lncRNAs) constitute the majority of transcripts |
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in the mammalian genomes, and yet, their functions remain largely unknown. As |
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part of the FANTOM6 project, the expression of 285 lncRNAs was systematically |
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knocked down in human dermal fibroblasts. Cellular growth, morphological changes, |
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and transcriptomic responses were quantified using Capped Analysis of Gene Expression |
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(CAGE).The functional annotation of the mammalian genome 6 (FANTOM6) project aims |
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to systematically map all human long noncoding RNAs (lncRNAs) in a gene-dependent |
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manner through dedicated efforts from national and international teams |
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sentences: |
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- What delivery system is used for the Fluzone Intradermal vaccine? |
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- What is dovitinib? |
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- Which class of genomic elements was assessed as part of the FANTOM6 project? |
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- source_sentence: ' The proband had normal molecular analysis of the glypican 6 gene |
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(GPC6), which was recently reported as a candidate for autosomal recessive omodysplasiaThe |
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proband had normal molecular analysis of the glypican 6 gene (GPC6), which was |
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recently reported as a candidate for autosomal recessive omodysplasiaThe glypican |
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6 gene (GPC6), which was recently reported as a candidate for autosomal recessive |
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omodysplasia.Omodysplasia is a rare autosomal recessive disorder with a frequency |
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of 1 in 50,000 newborn, and is associated with mutations in the GPC6 gene on chromosome |
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13.' |
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sentences: |
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- What is the effect of ivabradine in heart failure with preserved ejection fraction? |
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- What rare disease is associated with a mutation in the GPC6 gene on chromosome |
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13? |
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- What is the effect of rHDL-apoE3 on endothelial cell migration? |
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- source_sentence: Yes, numerous whole exome sequencing studies of ALzheimer patients |
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have been conducted. |
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sentences: |
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- Is muscle regeneration possible in mdx mice with the use of induced mesenchymal |
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stem cells? |
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- Has whole exome sequencing been performed in Alzheimer patients? |
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- How is connected "isolated Non-compaction cardiomyopathy" with dilated cardiomyopathy? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE base BioASQ Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.8516949152542372 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.940677966101695 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9576271186440678 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.961864406779661 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.8516949152542372 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.31355932203389825 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19152542372881357 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09618644067796611 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.8516949152542372 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.940677966101695 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9576271186440678 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.961864406779661 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.9149563623470877 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8990348399246703 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8999167242053622 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
|
value: 0.8516949152542372 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9449152542372882 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9555084745762712 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9597457627118644 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8516949152542372 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3149717514124293 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19110169491525428 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09597457627118645 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.8516949152542372 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.9449152542372882 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.9555084745762712 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9597457627118644 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.9136223756024043 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8979166666666664 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8990624087448101 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.8389830508474576 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.934322033898305 |
|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
value: 0.9470338983050848 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
value: 0.9597457627118644 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.8389830508474576 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
|
value: 0.3114406779661017 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.189406779661017 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09597457627118645 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8389830508474576 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.934322033898305 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9470338983050848 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9597457627118644 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9053426368336166 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8872721616895344 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.8879933659912613 |
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name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.8241525423728814 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9110169491525424 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9322033898305084 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9470338983050848 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.8241525423728814 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.30367231638418074 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1864406779661017 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09470338983050848 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.8241525423728814 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9110169491525424 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9322033898305084 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9470338983050848 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8905411432220106 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8719422585418346 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8732028981082185 |
|
name: Cosine Map@100 |
|
--- |
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|
|
# BGE base BioASQ Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
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## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
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- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, '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): Normalize() |
|
) |
|
``` |
|
|
|
## 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 SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("pavanmantha/bge-base-en-bioembed") |
|
# Run inference |
|
sentences = [ |
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'Yes, numerous whole exome sequencing studies of ALzheimer patients have been conducted.', |
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'Has whole exome sequencing been performed in Alzheimer patients?', |
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'How is connected "isolated Non-compaction cardiomyopathy" with dilated cardiomyopathy?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
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# [3, 3] |
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``` |
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|
|
<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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</details> |
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--> |
|
|
|
<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
|
You can finetune this model on your own dataset. |
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|
|
<details><summary>Click to expand</summary> |
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|
|
</details> |
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--> |
|
|
|
<!-- |
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### Out-of-Scope Use |
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|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
|
|
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### Metrics |
|
|
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.8517 | |
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| cosine_accuracy@3 | 0.9407 | |
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| cosine_accuracy@5 | 0.9576 | |
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| cosine_accuracy@10 | 0.9619 | |
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| cosine_precision@1 | 0.8517 | |
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| cosine_precision@3 | 0.3136 | |
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| cosine_precision@5 | 0.1915 | |
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| cosine_precision@10 | 0.0962 | |
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| cosine_recall@1 | 0.8517 | |
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| cosine_recall@3 | 0.9407 | |
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| cosine_recall@5 | 0.9576 | |
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| cosine_recall@10 | 0.9619 | |
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| cosine_ndcg@10 | 0.915 | |
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| cosine_mrr@10 | 0.899 | |
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| **cosine_map@100** | **0.8999** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
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|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8517 | |
|
| cosine_accuracy@3 | 0.9449 | |
|
| cosine_accuracy@5 | 0.9555 | |
|
| cosine_accuracy@10 | 0.9597 | |
|
| cosine_precision@1 | 0.8517 | |
|
| cosine_precision@3 | 0.315 | |
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| cosine_precision@5 | 0.1911 | |
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| cosine_precision@10 | 0.096 | |
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| cosine_recall@1 | 0.8517 | |
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| cosine_recall@3 | 0.9449 | |
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| cosine_recall@5 | 0.9555 | |
|
| cosine_recall@10 | 0.9597 | |
|
| cosine_ndcg@10 | 0.9136 | |
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| cosine_mrr@10 | 0.8979 | |
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| **cosine_map@100** | **0.8991** | |
|
|
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#### Information Retrieval |
|
* Dataset: `dim_256` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
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|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.839 | |
|
| cosine_accuracy@3 | 0.9343 | |
|
| cosine_accuracy@5 | 0.947 | |
|
| cosine_accuracy@10 | 0.9597 | |
|
| cosine_precision@1 | 0.839 | |
|
| cosine_precision@3 | 0.3114 | |
|
| cosine_precision@5 | 0.1894 | |
|
| cosine_precision@10 | 0.096 | |
|
| cosine_recall@1 | 0.839 | |
|
| cosine_recall@3 | 0.9343 | |
|
| cosine_recall@5 | 0.947 | |
|
| cosine_recall@10 | 0.9597 | |
|
| cosine_ndcg@10 | 0.9053 | |
|
| cosine_mrr@10 | 0.8873 | |
|
| **cosine_map@100** | **0.888** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.8242 | |
|
| cosine_accuracy@3 | 0.911 | |
|
| cosine_accuracy@5 | 0.9322 | |
|
| cosine_accuracy@10 | 0.947 | |
|
| cosine_precision@1 | 0.8242 | |
|
| cosine_precision@3 | 0.3037 | |
|
| cosine_precision@5 | 0.1864 | |
|
| cosine_precision@10 | 0.0947 | |
|
| cosine_recall@1 | 0.8242 | |
|
| cosine_recall@3 | 0.911 | |
|
| cosine_recall@5 | 0.9322 | |
|
| cosine_recall@10 | 0.947 | |
|
| cosine_ndcg@10 | 0.8905 | |
|
| cosine_mrr@10 | 0.8719 | |
|
| **cosine_map@100** | **0.8732** | |
|
|
|
<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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|
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<!-- |
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### Recommendations |
|
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|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
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## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 4,247 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 103.25 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.94 tokens</li><li>max: 49 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------| |
|
| <code>Yes, saracatinib is being studied as a treatment against Alzheimer's Disease. A clinical Phase Ib study has been completed, and a clinical Phase IIa study is ongoing.</code> | <code>Was saracatinib being considered as a treatment for Alzheimer's disease in November 2017?</code> | |
|
| <code>TREM2 variants have been found to be associated with early as well as with late onset Alzheimer's disease.</code> | <code>Is TREM2 associated with Alzheimer's disease in humans?</code> | |
|
| <code>Yes, siltuximab , a chimeric human-mouse monoclonal antibody to IL6, is approved for the treatment of patients with multicentric Castleman disease who are human immunodeficiency virus negative and human herpesvirus-8 negative.</code> | <code>Is siltuximab effective for Castleman disease?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: True |
|
- `tf32`: False |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-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`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `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`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: False |
|
- `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_fused |
|
- `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`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `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 |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| |
|
| **0.9624** | **8** | **-** | **0.8794** | **0.8937** | **0.9044** | **0.9018** | |
|
| 1.2030 | 10 | 1.1405 | - | - | - | - | |
|
| 1.9248 | 16 | - | 0.8739 | 0.8866 | 0.8998 | 0.8984 | |
|
| 2.4060 | 20 | 0.4328 | - | - | - | - | |
|
| 2.8872 | 24 | - | 0.8732 | 0.8876 | 0.8987 | 0.8998 | |
|
| 3.6090 | 30 | 0.312 | - | - | - | - | |
|
| 3.8496 | 32 | - | 0.8732 | 0.8880 | 0.8991 | 0.8999 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.13 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```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} |
|
} |
|
``` |
|
|
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