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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:282883 |
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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: Mwanamke anashona. |
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sentences: |
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- Mwanamke akishona blanketi pamoja. |
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- Lakini Sir James alimkatiza. |
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- Kwa kuongezea, wabunifu mashuhuri sasa wana maduka ya rejareja katika hoteli kadhaa |
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za ununuzi. |
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- source_sentence: Mtandao huwawezesha watu kununua vitu. |
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sentences: |
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- Mwanamke fulani anakata kipande cha jibini. |
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- Hakuna hata mmoja wa wafadhili hawa anayeweza kuruhusu kuacha sasa, haswa na uchumi |
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unaoteseka ndani na kitaifa. |
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- Kwa vyovyote vile, kama ningekuwa na nia ya kununua kitabu hicho, ningekuwa na |
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nafasi nzuri zaidi ya kujadili bei nzuri. |
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- source_sentence: Je, kweli wewe ni hivyo gullible, Dave Hanson? |
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sentences: |
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- Mwanamume amesimama katika mashua paddling kuelekea pwani lined na fanicha na |
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vitu vingine kubwa. |
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- Dave Hanson, je, unaamini kila kitu wanachosema? |
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- Wasichana watatu wakifanya mchezo wa kuigiza jukwaani. |
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- source_sentence: Wanandoa wakitembea pamoja. |
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sentences: |
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- Mwanamume aliyevalia koti la manjano anatembea kando ya gari la kubebea watu. |
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- Wenzi wa ndoa wazee wanatembea barabarani wakishikamana mikono. |
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- Msichana mdogo anapanda kwenye kifaa cha kamba. |
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- source_sentence: Kuna masuala ya sera. |
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sentences: |
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- Mwanamke mwenye makunyanzi sana akishikilia miwani yake na kutembea kwenye barabara |
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ya jiji. |
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- Mwanamume anayeigiza kwa ajili ya umati wa watu. |
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- Masuala ya sera ya mbinu nyingi na maombi. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE base Swahili 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: 0.26803894120641386 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.3499618223466531 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.3858806312038687 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.43318910664291166 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.26803894120641386 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.11665394078221768 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.07717612624077373 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.043318910664291166 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.26803894120641386 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.3499618223466531 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.3858806312038687 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.43318910664291166 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.34611891078942064 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.3188061049905684 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.3251959746415499 |
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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: 0.26552557902774243 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.34601679816747266 |
|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
value: 0.3810766098243828 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
value: 0.4290850089081191 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
|
value: 0.26552557902774243 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.11533893272249085 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07621532196487656 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04290850089081191 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
|
value: 0.26552557902774243 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
|
value: 0.34601679816747266 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.3810766098243828 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
|
value: 0.4290850089081191 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.3425120728009226 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
|
value: 0.31538232445349546 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.32174207802147353 |
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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: 0.2576355306693815 |
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name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.33790404683125475 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.37165945533214556 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.41950878086026977 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.2576355306693815 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1126346822770849 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07433189106642912 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.041950878086026974 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.2576355306693815 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.33790404683125475 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.37165945533214556 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.41950878086026977 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.3338740008089949 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.30705069547968683 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.3134101334652913 |
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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: 0.24494146093153474 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3218694324255536 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3557202850598117 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.402901501654365 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.24494146093153474 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.10728981080851785 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07114405701196233 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0402901501654365 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.24494146093153474 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3218694324255536 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3557202850598117 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.402901501654365 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3191027723891013 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2928823673781056 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.299205934269314 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
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 |
|
value: 0.21936243318910664 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2918045304148638 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3234601679816747 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3698778315092899 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.21936243318910664 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0972681768049546 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06469203359633495 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03698778315092899 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.21936243318910664 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2918045304148638 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3234601679816747 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3698778315092899 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2897472677253453 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.26472963050495346 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2710377326397304 |
|
name: Cosine Map@100 |
|
--- |
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|
|
# BGE base Swahili Matryoshka |
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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 |
|
|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **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 |
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<!-- - **Training Dataset:** Unknown --> |
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- **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( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) 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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``` |
|
|
|
## 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("sartifyllc/bge-base-swahili-matryoshka") |
|
# Run inference |
|
sentences = [ |
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'Kuna masuala ya sera.', |
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'Masuala ya sera ya mbinu nyingi na maombi.', |
|
'Mwanamke mwenye makunyanzi sana akishikilia miwani yake na kutembea kwenye barabara ya jiji.', |
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] |
|
embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
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<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
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</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
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You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
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--> |
|
|
|
<!-- |
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### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.268 | |
|
| cosine_accuracy@3 | 0.35 | |
|
| cosine_accuracy@5 | 0.3859 | |
|
| cosine_accuracy@10 | 0.4332 | |
|
| cosine_precision@1 | 0.268 | |
|
| cosine_precision@3 | 0.1167 | |
|
| cosine_precision@5 | 0.0772 | |
|
| cosine_precision@10 | 0.0433 | |
|
| cosine_recall@1 | 0.268 | |
|
| cosine_recall@3 | 0.35 | |
|
| cosine_recall@5 | 0.3859 | |
|
| cosine_recall@10 | 0.4332 | |
|
| cosine_ndcg@10 | 0.3461 | |
|
| cosine_mrr@10 | 0.3188 | |
|
| **cosine_map@100** | **0.3252** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.2655 | |
|
| cosine_accuracy@3 | 0.346 | |
|
| cosine_accuracy@5 | 0.3811 | |
|
| cosine_accuracy@10 | 0.4291 | |
|
| cosine_precision@1 | 0.2655 | |
|
| cosine_precision@3 | 0.1153 | |
|
| cosine_precision@5 | 0.0762 | |
|
| cosine_precision@10 | 0.0429 | |
|
| cosine_recall@1 | 0.2655 | |
|
| cosine_recall@3 | 0.346 | |
|
| cosine_recall@5 | 0.3811 | |
|
| cosine_recall@10 | 0.4291 | |
|
| cosine_ndcg@10 | 0.3425 | |
|
| cosine_mrr@10 | 0.3154 | |
|
| **cosine_map@100** | **0.3217** | |
|
|
|
#### 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 | 0.2576 | |
|
| cosine_accuracy@3 | 0.3379 | |
|
| cosine_accuracy@5 | 0.3717 | |
|
| cosine_accuracy@10 | 0.4195 | |
|
| cosine_precision@1 | 0.2576 | |
|
| cosine_precision@3 | 0.1126 | |
|
| cosine_precision@5 | 0.0743 | |
|
| cosine_precision@10 | 0.042 | |
|
| cosine_recall@1 | 0.2576 | |
|
| cosine_recall@3 | 0.3379 | |
|
| cosine_recall@5 | 0.3717 | |
|
| cosine_recall@10 | 0.4195 | |
|
| cosine_ndcg@10 | 0.3339 | |
|
| cosine_mrr@10 | 0.3071 | |
|
| **cosine_map@100** | **0.3134** | |
|
|
|
#### 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 | 0.2449 | |
|
| cosine_accuracy@3 | 0.3219 | |
|
| cosine_accuracy@5 | 0.3557 | |
|
| cosine_accuracy@10 | 0.4029 | |
|
| cosine_precision@1 | 0.2449 | |
|
| cosine_precision@3 | 0.1073 | |
|
| cosine_precision@5 | 0.0711 | |
|
| cosine_precision@10 | 0.0403 | |
|
| cosine_recall@1 | 0.2449 | |
|
| cosine_recall@3 | 0.3219 | |
|
| cosine_recall@5 | 0.3557 | |
|
| cosine_recall@10 | 0.4029 | |
|
| cosine_ndcg@10 | 0.3191 | |
|
| cosine_mrr@10 | 0.2929 | |
|
| **cosine_map@100** | **0.2992** | |
|
|
|
#### 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 | 0.2194 | |
|
| cosine_accuracy@3 | 0.2918 | |
|
| cosine_accuracy@5 | 0.3235 | |
|
| cosine_accuracy@10 | 0.3699 | |
|
| cosine_precision@1 | 0.2194 | |
|
| cosine_precision@3 | 0.0973 | |
|
| cosine_precision@5 | 0.0647 | |
|
| cosine_precision@10 | 0.037 | |
|
| cosine_recall@1 | 0.2194 | |
|
| cosine_recall@3 | 0.2918 | |
|
| cosine_recall@5 | 0.3235 | |
|
| cosine_recall@10 | 0.3699 | |
|
| cosine_ndcg@10 | 0.2897 | |
|
| cosine_mrr@10 | 0.2647 | |
|
| **cosine_map@100** | **0.271** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 282,883 training samples |
|
* 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: 5 tokens</li><li>mean: 20.1 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 38.64 tokens</li><li>max: 184 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Alingoja mtu huyo mwingine arudi.</code> | <code>Ca'daan alingoja hadi alasiri yote mtu huyo atoke tena.</code> | |
|
| <code>Sheria hiyo huanzisha mfululizo wa ukaguzi wa majaribio.</code> | <code>Sheria hiyo pia inatoa sheria ya kudhibiti kwa ajili ya mashirika fulani ambayo yanahitaji kuandaa taarifa za kifedha za mashirika yote na kuzisimamisha kwa wakaguzi wa jumla.</code> | |
|
| <code>Mbwa anakimbia na kuruka nje.</code> | <code>Mbwa mwenye rangi ya kahawia anaruka na kukimbia shambani.</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, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
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 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `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`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `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 |
|
<details><summary>Click to expand</summary> |
|
|
|
| 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 | |
|
|:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.0181 | 10 | 9.7089 | - | - | - | - | - | |
|
| 0.0362 | 20 | 9.2806 | - | - | - | - | - | |
|
| 0.0543 | 30 | 8.8905 | - | - | - | - | - | |
|
| 0.0724 | 40 | 7.9651 | - | - | - | - | - | |
|
| 0.0905 | 50 | 7.4201 | - | - | - | - | - | |
|
| 0.1086 | 60 | 6.8346 | - | - | - | - | - | |
|
| 0.1267 | 70 | 6.515 | - | - | - | - | - | |
|
| 0.1448 | 80 | 6.2009 | - | - | - | - | - | |
|
| 0.1629 | 90 | 5.8256 | - | - | - | - | - | |
|
| 0.1810 | 100 | 5.549 | - | - | - | - | - | |
|
| 0.1991 | 110 | 5.1667 | - | - | - | - | - | |
|
| 0.2172 | 120 | 5.2684 | - | - | - | - | - | |
|
| 0.2353 | 130 | 5.0678 | - | - | - | - | - | |
|
| 0.2534 | 140 | 4.9183 | - | - | - | - | - | |
|
| 0.2715 | 150 | 4.844 | - | - | - | - | - | |
|
| 0.2896 | 160 | 4.5427 | - | - | - | - | - | |
|
| 0.3077 | 170 | 4.3324 | - | - | - | - | - | |
|
| 0.3258 | 180 | 4.4963 | - | - | - | - | - | |
|
| 0.3439 | 190 | 4.1704 | - | - | - | - | - | |
|
| 0.3620 | 200 | 4.1285 | - | - | - | - | - | |
|
| 0.3800 | 210 | 4.0235 | - | - | - | - | - | |
|
| 0.3981 | 220 | 4.0738 | - | - | - | - | - | |
|
| 0.4162 | 230 | 3.9132 | - | - | - | - | - | |
|
| 0.4343 | 240 | 3.9682 | - | - | - | - | - | |
|
| 0.4524 | 250 | 3.7542 | - | - | - | - | - | |
|
| 0.4705 | 260 | 3.6508 | - | - | - | - | - | |
|
| 0.4886 | 270 | 3.7596 | - | - | - | - | - | |
|
| 0.5067 | 280 | 3.5596 | - | - | - | - | - | |
|
| 0.5248 | 290 | 3.5077 | - | - | - | - | - | |
|
| 0.5429 | 300 | 3.3831 | - | - | - | - | - | |
|
| 0.5610 | 310 | 3.4 | - | - | - | - | - | |
|
| 0.5791 | 320 | 3.296 | - | - | - | - | - | |
|
| 0.5972 | 330 | 3.3646 | - | - | - | - | - | |
|
| 0.6153 | 340 | 3.3533 | - | - | - | - | - | |
|
| 0.6334 | 350 | 3.2171 | - | - | - | - | - | |
|
| 0.6515 | 360 | 3.2324 | - | - | - | - | - | |
|
| 0.6696 | 370 | 3.1544 | - | - | - | - | - | |
|
| 0.6877 | 380 | 3.3393 | - | - | - | - | - | |
|
| 0.7058 | 390 | 3.0864 | - | - | - | - | - | |
|
| 0.7239 | 400 | 3.1069 | - | - | - | - | - | |
|
| 0.7420 | 410 | 3.0722 | - | - | - | - | - | |
|
| 0.7601 | 420 | 3.1446 | - | - | - | - | - | |
|
| 0.7782 | 430 | 3.0847 | - | - | - | - | - | |
|
| 0.7963 | 440 | 3.0331 | - | - | - | - | - | |
|
| 0.8144 | 450 | 3.0197 | - | - | - | - | - | |
|
| 0.8325 | 460 | 2.9667 | - | - | - | - | - | |
|
| 0.8506 | 470 | 2.8331 | - | - | - | - | - | |
|
| 0.8687 | 480 | 2.9333 | - | - | - | - | - | |
|
| 0.8868 | 490 | 2.8714 | - | - | - | - | - | |
|
| 0.9049 | 500 | 2.8578 | - | - | - | - | - | |
|
| 0.9230 | 510 | 2.9689 | - | - | - | - | - | |
|
| 0.9411 | 520 | 2.7977 | - | - | - | - | - | |
|
| 0.9592 | 530 | 2.9832 | - | - | - | - | - | |
|
| 0.9773 | 540 | 2.9761 | - | - | - | - | - | |
|
| 0.9954 | 550 | 2.7711 | - | - | - | - | - | |
|
| 0.9990 | 552 | - | 0.2772 | 0.2954 | 0.3052 | 0.2445 | 0.3080 | |
|
| 1.0135 | 560 | 2.7194 | - | - | - | - | - | |
|
| 1.0316 | 570 | 2.8489 | - | - | - | - | - | |
|
| 1.0497 | 580 | 2.6559 | - | - | - | - | - | |
|
| 1.0678 | 590 | 2.6239 | - | - | - | - | - | |
|
| 1.0859 | 600 | 2.7081 | - | - | - | - | - | |
|
| 1.1039 | 610 | 2.6581 | - | - | - | - | - | |
|
| 1.1220 | 620 | 2.7709 | - | - | - | - | - | |
|
| 1.1401 | 630 | 2.6191 | - | - | - | - | - | |
|
| 1.1582 | 640 | 2.6712 | - | - | - | - | - | |
|
| 1.1763 | 650 | 2.5445 | - | - | - | - | - | |
|
| 1.1944 | 660 | 2.5264 | - | - | - | - | - | |
|
| 1.2125 | 670 | 2.5782 | - | - | - | - | - | |
|
| 1.2306 | 680 | 2.5652 | - | - | - | - | - | |
|
| 1.2487 | 690 | 2.6229 | - | - | - | - | - | |
|
| 1.2668 | 700 | 2.5557 | - | - | - | - | - | |
|
| 1.2849 | 710 | 2.5251 | - | - | - | - | - | |
|
| 1.3030 | 720 | 2.4555 | - | - | - | - | - | |
|
| 1.3211 | 730 | 2.5335 | - | - | - | - | - | |
|
| 1.3392 | 740 | 2.5027 | - | - | - | - | - | |
|
| 1.3573 | 750 | 2.3569 | - | - | - | - | - | |
|
| 1.3754 | 760 | 2.4255 | - | - | - | - | - | |
|
| 1.3935 | 770 | 2.4626 | - | - | - | - | - | |
|
| 1.4116 | 780 | 2.363 | - | - | - | - | - | |
|
| 1.4297 | 790 | 2.4 | - | - | - | - | - | |
|
| 1.4478 | 800 | 2.3317 | - | - | - | - | - | |
|
| 1.4659 | 810 | 2.2922 | - | - | - | - | - | |
|
| 1.4840 | 820 | 2.4086 | - | - | - | - | - | |
|
| 1.5021 | 830 | 2.3166 | - | - | - | - | - | |
|
| 1.5202 | 840 | 2.3401 | - | - | - | - | - | |
|
| 1.5383 | 850 | 2.1951 | - | - | - | - | - | |
|
| 1.5564 | 860 | 2.214 | - | - | - | - | - | |
|
| 1.5745 | 870 | 2.1859 | - | - | - | - | - | |
|
| 1.5926 | 880 | 2.3605 | - | - | - | - | - | |
|
| 1.6107 | 890 | 2.2528 | - | - | - | - | - | |
|
| 1.6288 | 900 | 2.2759 | - | - | - | - | - | |
|
| 1.6469 | 910 | 2.1458 | - | - | - | - | - | |
|
| 1.6650 | 920 | 2.187 | - | - | - | - | - | |
|
| 1.6831 | 930 | 2.3406 | - | - | - | - | - | |
|
| 1.7012 | 940 | 2.2151 | - | - | - | - | - | |
|
| 1.7193 | 950 | 2.2971 | - | - | - | - | - | |
|
| 1.7374 | 960 | 2.2736 | - | - | - | - | - | |
|
| 1.7555 | 970 | 2.2329 | - | - | - | - | - | |
|
| 1.7736 | 980 | 2.2602 | - | - | - | - | - | |
|
| 1.7917 | 990 | 2.2402 | - | - | - | - | - | |
|
| 1.8098 | 1000 | 2.1971 | - | - | - | - | - | |
|
| 1.8278 | 1010 | 2.1642 | - | - | - | - | - | |
|
| 1.8459 | 1020 | 2.1274 | - | - | - | - | - | |
|
| 1.8640 | 1030 | 2.1833 | - | - | - | - | - | |
|
| 1.8821 | 1040 | 2.156 | - | - | - | - | - | |
|
| 1.9002 | 1050 | 2.1252 | - | - | - | - | - | |
|
| 1.9183 | 1060 | 2.161 | - | - | - | - | - | |
|
| 1.9364 | 1070 | 2.1267 | - | - | - | - | - | |
|
| 1.9545 | 1080 | 2.2017 | - | - | - | - | - | |
|
| 1.9726 | 1090 | 2.3044 | - | - | - | - | - | |
|
| 1.9907 | 1100 | 2.161 | - | - | - | - | - | |
|
| 1.9998 | 1105 | - | 0.2928 | 0.3085 | 0.3165 | 0.2632 | 0.3204 | |
|
| 2.0088 | 1110 | 2.0594 | - | - | - | - | - | |
|
| 2.0269 | 1120 | 2.2277 | - | - | - | - | - | |
|
| 2.0450 | 1130 | 2.1591 | - | - | - | - | - | |
|
| 2.0631 | 1140 | 2.0396 | - | - | - | - | - | |
|
| 2.0812 | 1150 | 2.1007 | - | - | - | - | - | |
|
| 2.0993 | 1160 | 2.0705 | - | - | - | - | - | |
|
| 2.1174 | 1170 | 2.0894 | - | - | - | - | - | |
|
| 2.1355 | 1180 | 2.0677 | - | - | - | - | - | |
|
| 2.1536 | 1190 | 2.0893 | - | - | - | - | - | |
|
| 2.1717 | 1200 | 1.984 | - | - | - | - | - | |
|
| 2.1898 | 1210 | 1.9206 | - | - | - | - | - | |
|
| 2.2079 | 1220 | 2.132 | - | - | - | - | - | |
|
| 2.2260 | 1230 | 2.0457 | - | - | - | - | - | |
|
| 2.2441 | 1240 | 2.1428 | - | - | - | - | - | |
|
| 2.2622 | 1250 | 2.1116 | - | - | - | - | - | |
|
| 2.2803 | 1260 | 1.993 | - | - | - | - | - | |
|
| 2.2984 | 1270 | 2.0181 | - | - | - | - | - | |
|
| 2.3165 | 1280 | 1.9742 | - | - | - | - | - | |
|
| 2.3346 | 1290 | 2.081 | - | - | - | - | - | |
|
| 2.3527 | 1300 | 1.9107 | - | - | - | - | - | |
|
| 2.3708 | 1310 | 1.9507 | - | - | - | - | - | |
|
| 2.3889 | 1320 | 1.9844 | - | - | - | - | - | |
|
| 2.4070 | 1330 | 2.0035 | - | - | - | - | - | |
|
| 2.4251 | 1340 | 1.9121 | - | - | - | - | - | |
|
| 2.4432 | 1350 | 2.0057 | - | - | - | - | - | |
|
| 2.4613 | 1360 | 1.9323 | - | - | - | - | - | |
|
| 2.4794 | 1370 | 1.9216 | - | - | - | - | - | |
|
| 2.4975 | 1380 | 1.995 | - | - | - | - | - | |
|
| 2.5156 | 1390 | 1.9285 | - | - | - | - | - | |
|
| 2.5337 | 1400 | 1.8886 | - | - | - | - | - | |
|
| 2.5517 | 1410 | 1.8298 | - | - | - | - | - | |
|
| 2.5698 | 1420 | 1.8452 | - | - | - | - | - | |
|
| 2.5879 | 1430 | 1.9488 | - | - | - | - | - | |
|
| 2.6060 | 1440 | 1.8928 | - | - | - | - | - | |
|
| 2.6241 | 1450 | 2.0101 | - | - | - | - | - | |
|
| 2.6422 | 1460 | 1.7591 | - | - | - | - | - | |
|
| 2.6603 | 1470 | 1.9177 | - | - | - | - | - | |
|
| 2.6784 | 1480 | 1.9329 | - | - | - | - | - | |
|
| 2.6965 | 1490 | 1.8978 | - | - | - | - | - | |
|
| 2.7146 | 1500 | 1.9589 | - | - | - | - | - | |
|
| 2.7327 | 1510 | 1.9744 | - | - | - | - | - | |
|
| 2.7508 | 1520 | 1.9272 | - | - | - | - | - | |
|
| 2.7689 | 1530 | 1.9234 | - | - | - | - | - | |
|
| 2.7870 | 1540 | 1.9667 | - | - | - | - | - | |
|
| 2.8051 | 1550 | 1.853 | - | - | - | - | - | |
|
| 2.8232 | 1560 | 1.9191 | - | - | - | - | - | |
|
| 2.8413 | 1570 | 1.8083 | - | - | - | - | - | |
|
| 2.8594 | 1580 | 1.8543 | - | - | - | - | - | |
|
| 2.8775 | 1590 | 1.9091 | - | - | - | - | - | |
|
| 2.8956 | 1600 | 1.8079 | - | - | - | - | - | |
|
| 2.9137 | 1610 | 1.8992 | - | - | - | - | - | |
|
| 2.9318 | 1620 | 1.8742 | - | - | - | - | - | |
|
| 2.9499 | 1630 | 1.9313 | - | - | - | - | - | |
|
| 2.9680 | 1640 | 1.9832 | - | - | - | - | - | |
|
| 2.9861 | 1650 | 1.9037 | - | - | - | - | - | |
|
| 2.9988 | 1657 | - | 0.2982 | 0.3130 | 0.3211 | 0.2697 | 0.3247 | |
|
| 3.0042 | 1660 | 1.7924 | - | - | - | - | - | |
|
| 3.0223 | 1670 | 1.9677 | - | - | - | - | - | |
|
| 3.0404 | 1680 | 1.9123 | - | - | - | - | - | |
|
| 3.0585 | 1690 | 1.7691 | - | - | - | - | - | |
|
| 3.0766 | 1700 | 1.8822 | - | - | - | - | - | |
|
| 3.0947 | 1710 | 1.8543 | - | - | - | - | - | |
|
| 3.1128 | 1720 | 1.8127 | - | - | - | - | - | |
|
| 3.1309 | 1730 | 1.8844 | - | - | - | - | - | |
|
| 3.1490 | 1740 | 1.911 | - | - | - | - | - | |
|
| 3.1671 | 1750 | 1.7695 | - | - | - | - | - | |
|
| 3.1852 | 1760 | 1.8134 | - | - | - | - | - | |
|
| 3.2033 | 1770 | 1.7794 | - | - | - | - | - | |
|
| 3.2214 | 1780 | 1.8851 | - | - | - | - | - | |
|
| 3.2395 | 1790 | 1.8381 | - | - | - | - | - | |
|
| 3.2576 | 1800 | 1.9184 | - | - | - | - | - | |
|
| 3.2756 | 1810 | 1.8074 | - | - | - | - | - | |
|
| 3.2937 | 1820 | 1.8236 | - | - | - | - | - | |
|
| 3.3118 | 1830 | 1.8203 | - | - | - | - | - | |
|
| 3.3299 | 1840 | 1.8874 | - | - | - | - | - | |
|
| 3.3480 | 1850 | 1.7457 | - | - | - | - | - | |
|
| 3.3661 | 1860 | 1.7933 | - | - | - | - | - | |
|
| 3.3842 | 1870 | 1.759 | - | - | - | - | - | |
|
| 3.4023 | 1880 | 1.8514 | - | - | - | - | - | |
|
| 3.4204 | 1890 | 1.8163 | - | - | - | - | - | |
|
| 3.4385 | 1900 | 1.8299 | - | - | - | - | - | |
|
| 3.4566 | 1910 | 1.8112 | - | - | - | - | - | |
|
| 3.4747 | 1920 | 1.7446 | - | - | - | - | - | |
|
| 3.4928 | 1930 | 1.8314 | - | - | - | - | - | |
|
| 3.5109 | 1940 | 1.742 | - | - | - | - | - | |
|
| 3.5290 | 1950 | 1.7519 | - | - | - | - | - | |
|
| 3.5471 | 1960 | 1.722 | - | - | - | - | - | |
|
| 3.5652 | 1970 | 1.7454 | - | - | - | - | - | |
|
| 3.5833 | 1980 | 1.7875 | - | - | - | - | - | |
|
| 3.6014 | 1990 | 1.7596 | - | - | - | - | - | |
|
| 3.6195 | 2000 | 1.8348 | - | - | - | - | - | |
|
| 3.6376 | 2010 | 1.6954 | - | - | - | - | - | |
|
| 3.6557 | 2020 | 1.7334 | - | - | - | - | - | |
|
| 3.6738 | 2030 | 1.8318 | - | - | - | - | - | |
|
| 3.6919 | 2040 | 1.7982 | - | - | - | - | - | |
|
| 3.7100 | 2050 | 1.7987 | - | - | - | - | - | |
|
| 3.7281 | 2060 | 1.8402 | - | - | - | - | - | |
|
| 3.7462 | 2070 | 1.8569 | - | - | - | - | - | |
|
| 3.7643 | 2080 | 1.8285 | - | - | - | - | - | |
|
| 3.7824 | 2090 | 1.8652 | - | - | - | - | - | |
|
| 3.8005 | 2100 | 1.7731 | - | - | - | - | - | |
|
| 3.8186 | 2110 | 1.8697 | - | - | - | - | - | |
|
| 3.8367 | 2120 | 1.6953 | - | - | - | - | - | |
|
| 3.8548 | 2130 | 1.7493 | - | - | - | - | - | |
|
| 3.8729 | 2140 | 1.8031 | - | - | - | - | - | |
|
| 3.8910 | 2150 | 1.7053 | - | - | - | - | - | |
|
| 3.9091 | 2160 | 1.8436 | - | - | - | - | - | |
|
| 3.9272 | 2170 | 1.7572 | - | - | - | - | - | |
|
| 3.9453 | 2180 | 1.7797 | - | - | - | - | - | |
|
| 3.9634 | 2190 | 1.8827 | - | - | - | - | - | |
|
| 3.9815 | 2200 | 1.8678 | - | - | - | - | - | |
|
| **3.9959** | **2208** | **-** | **0.2992** | **0.3134** | **0.3217** | **0.271** | **0.3252** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- 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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