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--- |
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language: |
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- es |
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license: apache-2.0 |
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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:14907 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: jinaai/jina-embeddings-v3 |
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widget: |
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- source_sentence: ¿Qué característica especial tenía la escultura del 'Torico' creada |
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por Pedro Blesa? |
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sentences: |
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- 'Después de dorar el conejo en la receta de Conejo escabechado, en la misma sartén |
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se rehogan los ajos, con el laurel y la pimienta. |
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' |
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- Rafael Barcelón se encargaba del servicio de electricidad en Valdeconejos en 1951. |
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- La escultura del 'Torico' creada por Pedro Blesa era un anaglifo, visible en 3D |
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con gafas especiales. |
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- source_sentence: ¿Por qué cantidad adquirió Francisco Santacruz la mina Escuadra |
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en la subasta pública? |
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sentences: |
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- Después de la temporada 1986-87, el equipo descendió, lo que provocó su desaparición |
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del campeonato en la temporada 1987-88. |
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- '''Al bies'' significa en diagonal.' |
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- Francisco Santacruz adquirió la mina Escuadra por la cantidad de 931 pesetas. |
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- source_sentence: ¿Quién se desempeñaba como fiscal en el ayuntamiento de Escucha |
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en el año 1916? |
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sentences: |
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- El autor mencionado para la receta Sopas de ajo es Teo Martin Lafuente. |
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- En Escucha en 1916, D. Joaquín Latorre del Río se desempeñaba como fiscal. |
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- Felipe Mallén era el farmacéutico en Valdeconejos en 1928. |
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- source_sentence: ¿Qué información transmiten los 'toques' en la caña de un pozo |
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durante las operaciones mineras? |
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sentences: |
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- Juan Pedro Martín encontró fragmentos de carbón de piedra en el paraje de El Horcajo. |
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- Se publicó en 1970 por Ediciones Cultura y Acción. CNT. |
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- 'Los ''toques'' son señales que se hacen en la caña del pozo para las distintas |
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operaciones 1: alto 2: arriba 3: abajo 1+2: despacio arriba 1+3: despacio abajo |
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4+2: personal arriba 4+3: personal abajo 4+1+2: señalista en jaula arriba 4+1+3: |
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señalista en jaula abajo 5: jaula libre 6: maniobra' |
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- source_sentence: ¿En qué año se demarcó y reconoció la mina 'El Pilar'? |
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sentences: |
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- Según la quinta demanda del SOMM, todas compañías mineras debían entregar a todos |
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sus obreros un libramiento de liquidación mensual |
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- '''Tontiar'' significa cuando dos jóvenes empiezan con un noviazgo.' |
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- La mina 'El Pilar' se demarcó y reconoció en 1857. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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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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model-index: |
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- name: Lampistero |
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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 1024 |
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type: dim_1024 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7700663850331925 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8925769462884732 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9155099577549789 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9330114665057333 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7700663850331925 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2975256487628244 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.18310199155099577 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09330114665057333 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7700663850331925 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8925769462884732 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9155099577549789 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9330114665057333 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8578914781807897 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8330619976817926 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8343424106284848 |
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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 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: 0.7694628847314424 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8889559444779722 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9124924562462281 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9330114665057333 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7694628847314424 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.29631864815932407 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1824984912492456 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09330114665057332 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7694628847314424 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8889559444779722 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9124924562462281 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9330114665057333 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8571049923900239 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8320899311243306 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8333457816447034 |
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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: 0.7682558841279421 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8865419432709717 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9112854556427278 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9305974652987327 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7682558841279421 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2955139810903239 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.18225709112854557 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09305974652987326 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7682558841279421 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8865419432709717 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9112854556427278 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9305974652987327 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8555277012951626 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8307227155597702 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8321030396467847 |
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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: 0.764031382015691 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8901629450814725 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9082679541339771 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9299939649969825 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.764031382015691 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2967209816938242 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1816535908267954 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09299939649969825 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.764031382015691 |
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|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8901629450814725 |
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|
name: Cosine Recall@3 |
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|
- type: cosine_recall@5 |
|
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value: 0.9082679541339771 |
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|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9299939649969825 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8535167149096011 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8282907530342651 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8296119986031772 |
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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: 0.7447193723596862 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8768859384429692 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9028364514182257 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9215449607724804 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7447193723596862 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2922953128143231 |
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name: Cosine Precision@3 |
|
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- type: cosine_precision@5 |
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value: 0.1805672902836451 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09215449607724803 |
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name: Cosine Precision@10 |
|
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- type: cosine_recall@1 |
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value: 0.7447193723596862 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8768859384429692 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9028364514182257 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9215449607724804 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8402664516336745 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8133905221714518 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8148588407289652 |
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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: 0.7103198551599276 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8491249245624622 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8780929390464696 |
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name: Cosine Accuracy@5 |
|
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- type: cosine_accuracy@10 |
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value: 0.899818949909475 |
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name: Cosine Accuracy@10 |
|
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- type: cosine_precision@1 |
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value: 0.7103198551599276 |
|
|
name: Cosine Precision@1 |
|
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- type: cosine_precision@3 |
|
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value: 0.2830416415208208 |
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|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
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value: 0.1756185878092939 |
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|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
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value: 0.08998189499094747 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
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value: 0.7103198551599276 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.8491249245624622 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.8780929390464696 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.899818949909475 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.8119294706592789 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.7829293234091058 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
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value: 0.7850878407159746 |
|
|
name: Cosine Map@100 |
|
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--- |
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# Lampistero |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) <!-- at revision f1944de8402dcd5f2b03f822a4bc22a7f2de2eb9 --> |
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- **Maximum Sequence Length:** 8194 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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- **Language:** es |
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- **License:** apache-2.0 |
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### Model Sources |
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- **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) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(transformer): Transformer( |
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(auto_model): XLMRobertaLoRA( |
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(roberta): XLMRobertaModel( |
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(embeddings): XLMRobertaEmbeddings( |
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(word_embeddings): ParametrizedEmbedding( |
|
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250002, 1024, padding_idx=1 |
|
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(parametrizations): ModuleDict( |
|
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(weight): ParametrizationList( |
|
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(0): LoRAParametrization() |
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) |
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) |
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) |
|
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(token_type_embeddings): ParametrizedEmbedding( |
|
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1, 1024 |
|
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(parametrizations): ModuleDict( |
|
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(weight): ParametrizationList( |
|
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(0): LoRAParametrization() |
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) |
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) |
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) |
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) |
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(emb_drop): Dropout(p=0.1, inplace=False) |
|
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(emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
|
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(encoder): XLMRobertaEncoder( |
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(layers): ModuleList( |
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(0-23): 24 x Block( |
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(mixer): MHA( |
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(rotary_emb): RotaryEmbedding() |
|
|
(Wqkv): ParametrizedLinearResidual( |
|
|
in_features=1024, out_features=3072, bias=True |
|
|
(parametrizations): ModuleDict( |
|
|
(weight): ParametrizationList( |
|
|
(0): LoRAParametrization() |
|
|
) |
|
|
) |
|
|
) |
|
|
(inner_attn): FlashSelfAttention( |
|
|
(drop): Dropout(p=0.1, inplace=False) |
|
|
) |
|
|
(inner_cross_attn): FlashCrossAttention( |
|
|
(drop): Dropout(p=0.1, inplace=False) |
|
|
) |
|
|
(out_proj): ParametrizedLinear( |
|
|
in_features=1024, out_features=1024, bias=True |
|
|
(parametrizations): ModuleDict( |
|
|
(weight): ParametrizationList( |
|
|
(0): LoRAParametrization() |
|
|
) |
|
|
) |
|
|
) |
|
|
) |
|
|
(dropout1): Dropout(p=0.1, inplace=False) |
|
|
(drop_path1): StochasticDepth(p=0.0, mode=row) |
|
|
(norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
|
|
(mlp): Mlp( |
|
|
(fc1): ParametrizedLinear( |
|
|
in_features=1024, out_features=4096, bias=True |
|
|
(parametrizations): ModuleDict( |
|
|
(weight): ParametrizationList( |
|
|
(0): LoRAParametrization() |
|
|
) |
|
|
) |
|
|
) |
|
|
(fc2): ParametrizedLinear( |
|
|
in_features=4096, out_features=1024, bias=True |
|
|
(parametrizations): ModuleDict( |
|
|
(weight): ParametrizationList( |
|
|
(0): LoRAParametrization() |
|
|
) |
|
|
) |
|
|
) |
|
|
) |
|
|
(dropout2): Dropout(p=0.1, inplace=False) |
|
|
(drop_path2): StochasticDepth(p=0.0, mode=row) |
|
|
(norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True) |
|
|
) |
|
|
) |
|
|
) |
|
|
(pooler): XLMRobertaPooler( |
|
|
(dense): ParametrizedLinear( |
|
|
in_features=1024, out_features=1024, bias=True |
|
|
(parametrizations): ModuleDict( |
|
|
(weight): ParametrizationList( |
|
|
(0): LoRAParametrization() |
|
|
) |
|
|
) |
|
|
) |
|
|
(activation): Tanh() |
|
|
) |
|
|
) |
|
|
) |
|
|
) |
|
|
(pooler): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
|
(normalizer): Normalize() |
|
|
) |
|
|
``` |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
|
|
First install the Sentence Transformers library: |
|
|
|
|
|
```bash |
|
|
pip install -U sentence-transformers |
|
|
``` |
|
|
|
|
|
Then you can load this model and run inference. |
|
|
```python |
|
|
from sentence_transformers import SentenceTransformer |
|
|
|
|
|
# Download from the 🤗 Hub |
|
|
model = SentenceTransformer("csanz91/lampistero_rag_embeddings_2") |
|
|
# Run inference |
|
|
sentences = [ |
|
|
"¿En qué año se demarcó y reconoció la mina 'El Pilar'?", |
|
|
"La mina 'El Pilar' se demarcó y reconoció en 1857.", |
|
|
'Según la quinta demanda del SOMM, todas compañías mineras debían entregar a todos sus obreros un libramiento de liquidación mensual', |
|
|
] |
|
|
embeddings = model.encode(sentences) |
|
|
print(embeddings.shape) |
|
|
# [3, 1024] |
|
|
|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(embeddings, embeddings) |
|
|
print(similarities.shape) |
|
|
# [3, 3] |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### 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_1024` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 1024 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7701 | |
|
|
| cosine_accuracy@3 | 0.8926 | |
|
|
| cosine_accuracy@5 | 0.9155 | |
|
|
| cosine_accuracy@10 | 0.933 | |
|
|
| cosine_precision@1 | 0.7701 | |
|
|
| cosine_precision@3 | 0.2975 | |
|
|
| cosine_precision@5 | 0.1831 | |
|
|
| cosine_precision@10 | 0.0933 | |
|
|
| cosine_recall@1 | 0.7701 | |
|
|
| cosine_recall@3 | 0.8926 | |
|
|
| cosine_recall@5 | 0.9155 | |
|
|
| cosine_recall@10 | 0.933 | |
|
|
| **cosine_ndcg@10** | **0.8579** | |
|
|
| cosine_mrr@10 | 0.8331 | |
|
|
| cosine_map@100 | 0.8343 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_768` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 768 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7695 | |
|
|
| cosine_accuracy@3 | 0.889 | |
|
|
| cosine_accuracy@5 | 0.9125 | |
|
|
| cosine_accuracy@10 | 0.933 | |
|
|
| cosine_precision@1 | 0.7695 | |
|
|
| cosine_precision@3 | 0.2963 | |
|
|
| cosine_precision@5 | 0.1825 | |
|
|
| cosine_precision@10 | 0.0933 | |
|
|
| cosine_recall@1 | 0.7695 | |
|
|
| cosine_recall@3 | 0.889 | |
|
|
| cosine_recall@5 | 0.9125 | |
|
|
| cosine_recall@10 | 0.933 | |
|
|
| **cosine_ndcg@10** | **0.8571** | |
|
|
| cosine_mrr@10 | 0.8321 | |
|
|
| cosine_map@100 | 0.8333 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_512` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 512 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7683 | |
|
|
| cosine_accuracy@3 | 0.8865 | |
|
|
| cosine_accuracy@5 | 0.9113 | |
|
|
| cosine_accuracy@10 | 0.9306 | |
|
|
| cosine_precision@1 | 0.7683 | |
|
|
| cosine_precision@3 | 0.2955 | |
|
|
| cosine_precision@5 | 0.1823 | |
|
|
| cosine_precision@10 | 0.0931 | |
|
|
| cosine_recall@1 | 0.7683 | |
|
|
| cosine_recall@3 | 0.8865 | |
|
|
| cosine_recall@5 | 0.9113 | |
|
|
| cosine_recall@10 | 0.9306 | |
|
|
| **cosine_ndcg@10** | **0.8555** | |
|
|
| cosine_mrr@10 | 0.8307 | |
|
|
| cosine_map@100 | 0.8321 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_256` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 256 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.764 | |
|
|
| cosine_accuracy@3 | 0.8902 | |
|
|
| cosine_accuracy@5 | 0.9083 | |
|
|
| cosine_accuracy@10 | 0.93 | |
|
|
| cosine_precision@1 | 0.764 | |
|
|
| cosine_precision@3 | 0.2967 | |
|
|
| cosine_precision@5 | 0.1817 | |
|
|
| cosine_precision@10 | 0.093 | |
|
|
| cosine_recall@1 | 0.764 | |
|
|
| cosine_recall@3 | 0.8902 | |
|
|
| cosine_recall@5 | 0.9083 | |
|
|
| cosine_recall@10 | 0.93 | |
|
|
| **cosine_ndcg@10** | **0.8535** | |
|
|
| cosine_mrr@10 | 0.8283 | |
|
|
| cosine_map@100 | 0.8296 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_128` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 128 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7447 | |
|
|
| cosine_accuracy@3 | 0.8769 | |
|
|
| cosine_accuracy@5 | 0.9028 | |
|
|
| cosine_accuracy@10 | 0.9215 | |
|
|
| cosine_precision@1 | 0.7447 | |
|
|
| cosine_precision@3 | 0.2923 | |
|
|
| cosine_precision@5 | 0.1806 | |
|
|
| cosine_precision@10 | 0.0922 | |
|
|
| cosine_recall@1 | 0.7447 | |
|
|
| cosine_recall@3 | 0.8769 | |
|
|
| cosine_recall@5 | 0.9028 | |
|
|
| cosine_recall@10 | 0.9215 | |
|
|
| **cosine_ndcg@10** | **0.8403** | |
|
|
| cosine_mrr@10 | 0.8134 | |
|
|
| cosine_map@100 | 0.8149 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_64` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 64 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7103 | |
|
|
| cosine_accuracy@3 | 0.8491 | |
|
|
| cosine_accuracy@5 | 0.8781 | |
|
|
| cosine_accuracy@10 | 0.8998 | |
|
|
| cosine_precision@1 | 0.7103 | |
|
|
| cosine_precision@3 | 0.283 | |
|
|
| cosine_precision@5 | 0.1756 | |
|
|
| cosine_precision@10 | 0.09 | |
|
|
| cosine_recall@1 | 0.7103 | |
|
|
| cosine_recall@3 | 0.8491 | |
|
|
| cosine_recall@5 | 0.8781 | |
|
|
| cosine_recall@10 | 0.8998 | |
|
|
| **cosine_ndcg@10** | **0.8119** | |
|
|
| cosine_mrr@10 | 0.7829 | |
|
|
| cosine_map@100 | 0.7851 | |
|
|
|
|
|
<!-- |
|
|
## 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 |
|
|
|
|
|
#### json |
|
|
|
|
|
* Dataset: json |
|
|
* Size: 14,907 training samples |
|
|
* Columns: <code>query</code> and <code>answer</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | query | answer | |
|
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 9 tokens</li><li>mean: 26.09 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 34.02 tokens</li><li>max: 405 tokens</li></ul> | |
|
|
* Samples: |
|
|
| query | answer | |
|
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>¿Qué tipos de palas se utilizan para cargar el carbón y el mineral?</code> | <code>Se utiliza una pala convencional y una pala hidráulica, esta última descarga sobre un páncer, puede hacerlo lateralmente y se desplaza sobre ruedas u oruga.</code> | |
|
|
| <code>Tras el cierre de la tejería de Florencio Salvador, ¿de dónde procedieron finalmente los ladrillos para las doscientas diez viviendas construidas en Utrillas?</code> | <code>Los ladrillos y material para las doscientas diez viviendas construidas en Utrillas procedieron finalmente de Letux, Zaragoza .</code> | |
|
|
| <code>¿Cuál es el formato de los juegos infantiles que se están preparando para el verano en Escucha en 2021?</code> | <code>Los juegos infantiles que se están preparando para el verano en Escucha en 2021 están en formato revista.</code> | |
|
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"loss": "MultipleNegativesRankingLoss", |
|
|
"matryoshka_dims": [ |
|
|
1024, |
|
|
768, |
|
|
512, |
|
|
256, |
|
|
128, |
|
|
64 |
|
|
], |
|
|
"matryoshka_weights": [ |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1 |
|
|
], |
|
|
"n_dims_per_step": -1 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: epoch |
|
|
- `per_device_train_batch_size`: 64 |
|
|
- `per_device_eval_batch_size`: 16 |
|
|
- `gradient_accumulation_steps`: 32 |
|
|
- `learning_rate`: 2e-05 |
|
|
- `num_train_epochs`: 8 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `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`: 64 |
|
|
- `per_device_eval_batch_size`: 16 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 32 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_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`: 8 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 0 |
|
|
- `log_level`: passive |
|
|
- `log_level_replica`: warning |
|
|
- `log_on_each_node`: True |
|
|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
|
|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
|
|
- `restore_callback_states_from_checkpoint`: False |
|
|
- `no_cuda`: False |
|
|
- `use_cpu`: False |
|
|
- `use_mps_device`: False |
|
|
- `seed`: 42 |
|
|
- `data_seed`: None |
|
|
- `jit_mode_eval`: False |
|
|
- `use_ipex`: False |
|
|
- `bf16`: False |
|
|
- `fp16`: 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} |
|
|
- `tp_size`: 0 |
|
|
- `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`: None |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|
|:------:|:----:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| 1.0 | 8 | - | 0.7841 | 0.7835 | 0.7836 | 0.7791 | 0.7665 | 0.7226 | |
|
|
| 1.2747 | 10 | 58.1187 | - | - | - | - | - | - | |
|
|
| 2.0 | 16 | - | 0.8348 | 0.8366 | 0.8345 | 0.8301 | 0.8184 | 0.7861 | |
|
|
| 2.5494 | 20 | 24.4181 | - | - | - | - | - | - | |
|
|
| 3.0 | 24 | - | 0.8521 | 0.8504 | 0.8503 | 0.8457 | 0.8319 | 0.8007 | |
|
|
| 3.8240 | 30 | 16.1488 | - | - | - | - | - | - | |
|
|
| 4.0 | 32 | - | 0.8561 | 0.8548 | 0.8555 | 0.8509 | 0.8387 | 0.8073 | |
|
|
| 5.0 | 40 | 13.4897 | 0.8585 | 0.8556 | 0.8545 | 0.8528 | 0.8397 | 0.8111 | |
|
|
| 6.0 | 48 | - | 0.8578 | 0.8563 | 0.8550 | 0.8535 | 0.8410 | 0.8110 | |
|
|
| 6.2747 | 50 | 13.7469 | - | - | - | - | - | - | |
|
|
| 7.0 | 56 | - | 0.8579 | 0.8571 | 0.8555 | 0.8535 | 0.8403 | 0.8119 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.10 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.51.3 |
|
|
- PyTorch: 2.7.0+cu126 |
|
|
- Accelerate: 1.7.0 |
|
|
- Datasets: 3.6.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### 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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