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--- |
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base_model: Snowflake/snowflake-arctic-embed-m |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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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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pipeline_tag: sentence-similarity |
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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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- dataset_size:1K<n<10K |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: kim był Steve Yzerman? |
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sentences: |
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- Łazik marsjański Opportunity |
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- w jakim kraju jest przyznawany Order Białego Lotosu? |
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- do powstania jakich instytucji przyczynił się pierwszy biskup Makau? |
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- source_sentence: gdzie rośnie bokkonia? |
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sentences: |
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- jak rozmnażają się Aeolosomatidae? |
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- kto 1 stycznia 2011 został gubernatorem Nowego Jorku? |
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- w której świątyni koronowany był król jerozolimski Baldwin I? |
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- source_sentence: Godło Republiki Ałtaju |
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sentences: |
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- co przedstawia godło Republiki Ałtaju? |
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- w którym kraju w noc sylwestrową je się oliebollen? |
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- który z członków załogi Międzynarodowej Stacji Kosmicznej nie ma nóg? |
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- source_sentence: co to jest meszne? |
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sentences: |
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- co to jest Mammoth Hot Springs? |
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- jak przebiegała kariera sportowa Witolda Sikorskiego? |
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- do uratowania ilu dzieł sztuki przyczynił się Borys Woźnicki? |
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- source_sentence: Chłopiec z Nariokotome |
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sentences: |
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- ile wynosiła objętość mózgu chłopca z Nariokotome? |
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- gdzie znajduje się czwarty polski cmentarz katyński? |
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- w jakich miejscach stał warszawski pomnik Ignacego Jana Paderewskiego? |
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model-index: |
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- name: snowflake-arctic-embed-m-klej-dyk |
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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: 0.18509615384615385 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.4807692307692308 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.625 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7259615384615384 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.18509615384615385 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.16025641025641024 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.125 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07259615384615384 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.18509615384615385 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4807692307692308 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.625 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.7259615384615384 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.44786216254546357 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.358972451159951 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.3672210078826913 |
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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 |
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value: 0.17548076923076922 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.47115384615384615 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6129807692307693 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.7019230769230769 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.17548076923076922 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
|
value: 0.15705128205128205 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.12259615384615384 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07019230769230768 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.17548076923076922 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
|
value: 0.47115384615384615 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.6129807692307693 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
|
value: 0.7019230769230769 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.43344535381311455 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.3473920177045177 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.3563798565478224 |
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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: 0.15625 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.4543269230769231 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5649038461538461 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6730769230769231 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
|
value: 0.15625 |
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name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
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value: 0.15144230769230768 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.11298076923076923 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0673076923076923 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.15625 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.4543269230769231 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.5649038461538461 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.6730769230769231 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.4102597093872519 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.32613324175824177 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.3350744652348361 |
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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 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: 0.16346153846153846 |
|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.3918269230769231 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.5072115384615384 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6057692307692307 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.16346153846153846 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.13060897435897434 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.10144230769230769 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06057692307692307 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.16346153846153846 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3918269230769231 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.5072115384615384 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6057692307692307 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3757626519143444 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.30273962148962136 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.3116992239855167 |
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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 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.14903846153846154 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.3389423076923077 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4182692307692308 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49278846153846156 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14903846153846154 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.11298076923076923 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08365384615384615 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04927884615384615 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14903846153846154 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3389423076923077 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4182692307692308 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49278846153846156 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.31783226267644227 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.26212320665445676 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.27044860532149884 |
|
name: Cosine Map@100 |
|
--- |
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|
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# snowflake-arctic-embed-m-klej-dyk |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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 |
|
|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision 2ca412ec9505022eebd7d10286fbbad4b779f6e0 --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
|
|
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### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
|
```bash |
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pip install -U sentence-transformers |
|
``` |
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|
|
Then you can load this model and run inference. |
|
```python |
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from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
|
sentences = [ |
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'Chłopiec z Nariokotome', |
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'ile wynosiła objętość mózgu chłopca z Nariokotome?', |
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'gdzie znajduje się czwarty polski cmentarz katyński?', |
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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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|
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# Get the similarity scores for the embeddings |
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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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<!-- |
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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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|
|
<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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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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## Evaluation |
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|
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### Metrics |
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|
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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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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.1851 | |
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| cosine_accuracy@3 | 0.4808 | |
|
| cosine_accuracy@5 | 0.625 | |
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| cosine_accuracy@10 | 0.726 | |
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| cosine_precision@1 | 0.1851 | |
|
| cosine_precision@3 | 0.1603 | |
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| cosine_precision@5 | 0.125 | |
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| cosine_precision@10 | 0.0726 | |
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| cosine_recall@1 | 0.1851 | |
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| cosine_recall@3 | 0.4808 | |
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| cosine_recall@5 | 0.625 | |
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| cosine_recall@10 | 0.726 | |
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| cosine_ndcg@10 | 0.4479 | |
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| cosine_mrr@10 | 0.359 | |
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| **cosine_map@100** | **0.3672** | |
|
|
|
#### Information Retrieval |
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* 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 | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1755 | |
|
| cosine_accuracy@3 | 0.4712 | |
|
| cosine_accuracy@5 | 0.613 | |
|
| cosine_accuracy@10 | 0.7019 | |
|
| cosine_precision@1 | 0.1755 | |
|
| cosine_precision@3 | 0.1571 | |
|
| cosine_precision@5 | 0.1226 | |
|
| cosine_precision@10 | 0.0702 | |
|
| cosine_recall@1 | 0.1755 | |
|
| cosine_recall@3 | 0.4712 | |
|
| cosine_recall@5 | 0.613 | |
|
| cosine_recall@10 | 0.7019 | |
|
| cosine_ndcg@10 | 0.4334 | |
|
| cosine_mrr@10 | 0.3474 | |
|
| **cosine_map@100** | **0.3564** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1562 | |
|
| cosine_accuracy@3 | 0.4543 | |
|
| cosine_accuracy@5 | 0.5649 | |
|
| cosine_accuracy@10 | 0.6731 | |
|
| cosine_precision@1 | 0.1562 | |
|
| cosine_precision@3 | 0.1514 | |
|
| cosine_precision@5 | 0.113 | |
|
| cosine_precision@10 | 0.0673 | |
|
| cosine_recall@1 | 0.1562 | |
|
| cosine_recall@3 | 0.4543 | |
|
| cosine_recall@5 | 0.5649 | |
|
| cosine_recall@10 | 0.6731 | |
|
| cosine_ndcg@10 | 0.4103 | |
|
| cosine_mrr@10 | 0.3261 | |
|
| **cosine_map@100** | **0.3351** | |
|
|
|
#### 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 | 0.1635 | |
|
| cosine_accuracy@3 | 0.3918 | |
|
| cosine_accuracy@5 | 0.5072 | |
|
| cosine_accuracy@10 | 0.6058 | |
|
| cosine_precision@1 | 0.1635 | |
|
| cosine_precision@3 | 0.1306 | |
|
| cosine_precision@5 | 0.1014 | |
|
| cosine_precision@10 | 0.0606 | |
|
| cosine_recall@1 | 0.1635 | |
|
| cosine_recall@3 | 0.3918 | |
|
| cosine_recall@5 | 0.5072 | |
|
| cosine_recall@10 | 0.6058 | |
|
| cosine_ndcg@10 | 0.3758 | |
|
| cosine_mrr@10 | 0.3027 | |
|
| **cosine_map@100** | **0.3117** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.149 | |
|
| cosine_accuracy@3 | 0.3389 | |
|
| cosine_accuracy@5 | 0.4183 | |
|
| cosine_accuracy@10 | 0.4928 | |
|
| cosine_precision@1 | 0.149 | |
|
| cosine_precision@3 | 0.113 | |
|
| cosine_precision@5 | 0.0837 | |
|
| cosine_precision@10 | 0.0493 | |
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| cosine_recall@1 | 0.149 | |
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| cosine_recall@3 | 0.3389 | |
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| cosine_recall@5 | 0.4183 | |
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| cosine_recall@10 | 0.4928 | |
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| cosine_ndcg@10 | 0.3178 | |
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| cosine_mrr@10 | 0.2621 | |
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| **cosine_map@100** | **0.2704** | |
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<!-- |
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## Bias, Risks and Limitations |
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### Recommendations |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 3,738 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | positive | anchor | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 94.61 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.71 tokens</li><li>max: 76 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Marsz Ochotników (chin.</code> | <code>kto jest kompozytorem chińskiego hymnu narodowego Marsz Ochotników?</code> | |
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| <code>Wybrane przykłady: Święta Rodzina – Maryja z Dzieciątkiem na ręku, niekiedy obok niej stoi św. Józef Rodzina Marii – przedstawienie w którym pojawia się Święta Rodzina oraz postaci spokrewnione z Marią. Maria w połogu (Maria in puerperio) – leżąca na łożu Maria opiekuje się Dzieciątkiem Maria karmiąca (Maria lactans) – Maria karmiąca swą piersią Dzieciątko Orantka – kobieta modląca się z podniesionymi rękami (częsty motyw ikon wschodnich); Sacra Conversazione – Matka Boska tronująca z Dzieciątkiem, otoczona stojącymi postaciami świętych Pietà – opłakująca Jezusa, trzymając na kolanach jego ciało po śmierci na krzyżu; Hodegetria – ujęcie popiersia Maryi, trzymającej na rękach małego Jezusa, częsty motyw w ikonach Eleusa – formalnie podobne do przedstawienia Hodegetrii lecz Maryja policzkiem przytula się do policzka Jezusa Immaculata – Niepokalane Poczęcie Najświętszej Maryi Panny.</code> | <code>kto zamiast Maryi trzyma nowonarodzonego Jezusa w scenie Bożego Narodzenia przedstawionej na poliptyku z Marią i Dzieciątkiem Jezus?</code> | |
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| <code>Pomnik Josepha von Eichendorffa w Brzeziu Pomnik Josepha von Eichendorffa – odtworzony w 2006 roku pomnik znanego niemieckiego poety epoki romantyzmu związanego z ziemią raciborską, Josepha von Eichendorffa.</code> | <code>po ilu latach odtworzono wysadzony w 1945 roku pomnik Josepha von Eichendorffa w Raciborzu-Brzeziu?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
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|
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### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.0684 | 1 | 9.3155 | - | - | - | - | - | |
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| 0.1368 | 2 | 9.1788 | - | - | - | - | - | |
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| 0.2051 | 3 | 8.8387 | - | - | - | - | - | |
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| 0.2735 | 4 | 8.2961 | - | - | - | - | - | |
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| 0.3419 | 5 | 8.0242 | - | - | - | - | - | |
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| 0.4103 | 6 | 7.2329 | - | - | - | - | - | |
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| 0.4786 | 7 | 5.4386 | - | - | - | - | - | |
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| 0.5470 | 8 | 6.1186 | - | - | - | - | - | |
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| 0.6154 | 9 | 4.9714 | - | - | - | - | - | |
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| 0.6838 | 10 | 5.1958 | - | - | - | - | - | |
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| 0.7521 | 11 | 5.1135 | - | - | - | - | - | |
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| 0.8205 | 12 | 4.6971 | - | - | - | - | - | |
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| 0.8889 | 13 | 4.5559 | - | - | - | - | - | |
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| 0.9573 | 14 | 3.9357 | 0.2842 | 0.3098 | 0.3191 | 0.2238 | 0.3209 | |
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| 1.0256 | 15 | 3.7916 | - | - | - | - | - | |
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| 1.0940 | 16 | 3.6393 | - | - | - | - | - | |
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| 1.1624 | 17 | 3.7733 | - | - | - | - | - | |
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| 1.2308 | 18 | 3.6974 | - | - | - | - | - | |
|
| 1.2991 | 19 | 3.5964 | - | - | - | - | - | |
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| 1.3675 | 20 | 3.4118 | - | - | - | - | - | |
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| 1.4359 | 21 | 3.2022 | - | - | - | - | - | |
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| 1.5043 | 22 | 2.8133 | - | - | - | - | - | |
|
| 1.5726 | 23 | 3.0871 | - | - | - | - | - | |
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| 1.6410 | 24 | 2.9559 | - | - | - | - | - | |
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| 1.7094 | 25 | 2.8192 | - | - | - | - | - | |
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| 1.7778 | 26 | 3.462 | - | - | - | - | - | |
|
| 1.8462 | 27 | 3.1435 | - | - | - | - | - | |
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| 1.9145 | 28 | 2.8001 | - | - | - | - | - | |
|
| 1.9829 | 29 | 2.5643 | 0.3134 | 0.3359 | 0.3563 | 0.2588 | 0.3671 | |
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| 2.0513 | 30 | 2.4295 | - | - | - | - | - | |
|
| 2.1197 | 31 | 2.3892 | - | - | - | - | - | |
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| 2.1880 | 32 | 2.5228 | - | - | - | - | - | |
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| 2.2564 | 33 | 2.4906 | - | - | - | - | - | |
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| 2.3248 | 34 | 2.5358 | - | - | - | - | - | |
|
| 2.3932 | 35 | 2.2806 | - | - | - | - | - | |
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| 2.4615 | 36 | 2.0083 | - | - | - | - | - | |
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| 2.5299 | 37 | 2.5088 | - | - | - | - | - | |
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| 2.5983 | 38 | 2.0628 | - | - | - | - | - | |
|
| 2.6667 | 39 | 2.193 | - | - | - | - | - | |
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| 2.7350 | 40 | 2.4783 | - | - | - | - | - | |
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| 2.8034 | 41 | 2.382 | - | - | - | - | - | |
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| 2.8718 | 42 | 2.2017 | - | - | - | - | - | |
|
| 2.9402 | 43 | 1.9739 | 0.3111 | 0.3392 | 0.3572 | 0.2657 | 0.3659 | |
|
| 3.0085 | 44 | 2.0332 | - | - | - | - | - | |
|
| 3.0769 | 45 | 1.9983 | - | - | - | - | - | |
|
| 3.1453 | 46 | 1.8612 | - | - | - | - | - | |
|
| 3.2137 | 47 | 1.9897 | - | - | - | - | - | |
|
| 3.2821 | 48 | 2.2514 | - | - | - | - | - | |
|
| 3.3504 | 49 | 2.0092 | - | - | - | - | - | |
|
| 3.4188 | 50 | 1.7399 | - | - | - | - | - | |
|
| 3.4872 | 51 | 1.5825 | - | - | - | - | - | |
|
| 3.5556 | 52 | 2.1501 | - | - | - | - | - | |
|
| 3.6239 | 53 | 1.4505 | - | - | - | - | - | |
|
| 3.6923 | 54 | 1.8575 | - | - | - | - | - | |
|
| 3.7607 | 55 | 2.3882 | - | - | - | - | - | |
|
| 3.8291 | 56 | 2.1119 | - | - | - | - | - | |
|
| 3.8974 | 57 | 1.8992 | - | - | - | - | - | |
|
| 3.9658 | 58 | 1.8323 | 0.3117 | 0.3365 | 0.3558 | 0.2683 | 0.3670 | |
|
| 4.0342 | 59 | 1.5938 | - | - | - | - | - | |
|
| 4.1026 | 60 | 1.552 | - | - | - | - | - | |
|
| 4.1709 | 61 | 1.907 | - | - | - | - | - | |
|
| 4.2393 | 62 | 1.8304 | - | - | - | - | - | |
|
| 4.3077 | 63 | 1.8775 | - | - | - | - | - | |
|
| 4.3761 | 64 | 1.8654 | - | - | - | - | - | |
|
| 4.4444 | 65 | 1.7944 | - | - | - | - | - | |
|
| 4.5128 | 66 | 1.8335 | - | - | - | - | - | |
|
| 4.5812 | 67 | 1.8823 | - | - | - | - | - | |
|
| 4.6496 | 68 | 1.6479 | - | - | - | - | - | |
|
| 4.7179 | 69 | 1.5771 | - | - | - | - | - | |
|
| **4.7863** | **70** | **2.1911** | **0.3117** | **0.3351** | **0.3564** | **0.2704** | **0.3672** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.12.2 |
|
- Sentence Transformers: 3.0.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.3.1 |
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- Accelerate: 0.27.2 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
|
|
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## 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", |
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month = "11", |
|
year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
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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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*Clearly define terms in order to be accessible across audiences.* |
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