|
--- |
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base_model: PlanTL-GOB-ES/roberta-base-bne |
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datasets: [] |
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language: [] |
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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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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:4173 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: L'Ajuntament de Sitges ofereix a aquelles famílies que acompleixin |
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els requisits establerts, ajuts per al pagament de la quota del servei i de la |
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quota del menjador dels infants matriculats a les Llars d'Infants Municipals ( |
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0-3 anys). |
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sentences: |
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- Quin és l'objectiu principal de les subvencions per a projectes i activitats de |
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l'àmbit turístic? |
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- Quin és el procediment per a obtenir una llicència per a disposar d'una parada |
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en un mercat setmanal? |
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- Quin és el paper de l'Ajuntament de Sitges en la quota del menjador de les Llars |
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d'Infants Municipals? |
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- source_sentence: Es tracta de la sol·licitud de permís municipal per poder utilitzar |
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de forma privativa una zona de la via pública per instal·lacions d’atraccions |
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i venda en fires, amb independència de les possibles afectacions a la via pública... |
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sentences: |
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- Quin és el tipus de permís que es sol·licita? |
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- Quin és el paper de l'Ajuntament en aquest tràmit? |
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- Quin és el resultat de la llicència per a la constitució d'un règim de propietat |
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horitzontal en relació amb l’escriptura de divisió horitzontal? |
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- source_sentence: Totes les persones que resideixen a Espanya estan obligades a inscriure's |
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en el padró del municipi en el qual resideixen habitualment. |
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sentences: |
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- Quin és el benefici de l'ajut extraordinari per a la família de l'empleat? |
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- Què passa si no es presenta la sol·licitud d'acceptació en el termini establert? |
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- Qui està obligat a inscriure's en el Padró Municipal d'Habitants? |
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- source_sentence: Les persones i entitats beneficiaries hauran de justificar la realització |
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del projecte/activitat subvencionada com a màxim el dia 31 de març de 2023. |
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sentences: |
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- Quin és el termini per presentar la justificació de la realització del projecte/activitat |
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subvencionada? |
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- Quin és el període durant el qual es poden sol·licitar els ajuts? |
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- Quin és el registre on s'inscriuen les entitats d’interès ciutadà de Sitges? |
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- source_sentence: Els establiments locals tenen un paper clau en el projecte de la |
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targeta de fidelització, ja que són els que ofereixen descomptes i ofertes especials |
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als consumidors que utilitzen la targeta. |
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sentences: |
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- Quin és el paper dels establiments locals en el projecte de la targeta de fidelització? |
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- Quin és el paper de la via pública en aquest tràmit? |
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- Quins són els tractaments que beneficien la salut de l'empleat municipal que s'inclouen |
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en l'ajuda? |
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model-index: |
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- name: SentenceTransformer based on PlanTL-GOB-ES/roberta-base-bne |
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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 |
|
value: 0.05172413793103448 |
|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.125 |
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name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
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value: 0.1875 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3857758620689655 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.041666666666666664 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
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value: 0.037500000000000006 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
|
value: 0.038577586206896546 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.125 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.1875 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.3857758620689655 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.18210165785971896 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.12195368089764656 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.14624173028144724 |
|
name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
|
name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11422413793103449 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1810344827586207 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.38146551724137934 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.038074712643678156 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03620689655172414 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03814655172413794 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11422413793103449 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1810344827586207 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.38146551724137934 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.17527880528414544 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1144422208538589 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1390144478189839 |
|
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 256 |
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type: dim_256 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04741379310344827 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12284482758620689 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.20043103448275862 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.39870689655172414 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04741379310344827 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.040948275862068964 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04008620689655173 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03987068965517242 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04741379310344827 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12284482758620689 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.20043103448275862 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.39870689655172414 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.18506006244346174 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.12174072933771223 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1456718436547049 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11637931034482758 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.16810344827586207 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.38146551724137934 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03879310344827586 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03362068965517242 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03814655172413794 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11637931034482758 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.16810344827586207 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.38146551724137934 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.17534824322616613 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11455682129173515 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.139297198421225 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.03879310344827586 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.09698275862068965 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.15301724137931033 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34698275862068967 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.03879310344827586 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03232758620689655 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.030603448275862068 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.034698275862068965 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.03879310344827586 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.09698275862068965 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.15301724137931033 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34698275862068967 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1568993526090112 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10108699370552827 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1268371950835345 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on PlanTL-GOB-ES/roberta-base-bne |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne). 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. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) <!-- at revision 0e598176534f3cf2e30105f8286cf2503d6e4731 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel |
|
(1): Pooling({'word_embedding_dimension': 768, '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}) |
|
) |
|
``` |
|
|
|
## 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("adriansanz/sitges10242608-4ep-rerankv3-sp") |
|
# Run inference |
|
sentences = [ |
|
'Els establiments locals tenen un paper clau en el projecte de la targeta de fidelització, ja que són els que ofereixen descomptes i ofertes especials als consumidors que utilitzen la targeta.', |
|
'Quin és el paper dels establiments locals en el projecte de la targeta de fidelització?', |
|
"Quins són els tractaments que beneficien la salut de l'empleat municipal que s'inclouen en l'ajuda?", |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# 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_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 | 0.0517 | |
|
| cosine_accuracy@3 | 0.125 | |
|
| cosine_accuracy@5 | 0.1875 | |
|
| cosine_accuracy@10 | 0.3858 | |
|
| cosine_precision@1 | 0.0517 | |
|
| cosine_precision@3 | 0.0417 | |
|
| cosine_precision@5 | 0.0375 | |
|
| cosine_precision@10 | 0.0386 | |
|
| cosine_recall@1 | 0.0517 | |
|
| cosine_recall@3 | 0.125 | |
|
| cosine_recall@5 | 0.1875 | |
|
| cosine_recall@10 | 0.3858 | |
|
| cosine_ndcg@10 | 0.1821 | |
|
| cosine_mrr@10 | 0.122 | |
|
| **cosine_map@100** | **0.1462** | |
|
|
|
#### 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 | 0.0453 | |
|
| cosine_accuracy@3 | 0.1142 | |
|
| cosine_accuracy@5 | 0.181 | |
|
| cosine_accuracy@10 | 0.3815 | |
|
| cosine_precision@1 | 0.0453 | |
|
| cosine_precision@3 | 0.0381 | |
|
| cosine_precision@5 | 0.0362 | |
|
| cosine_precision@10 | 0.0381 | |
|
| cosine_recall@1 | 0.0453 | |
|
| cosine_recall@3 | 0.1142 | |
|
| cosine_recall@5 | 0.181 | |
|
| cosine_recall@10 | 0.3815 | |
|
| cosine_ndcg@10 | 0.1753 | |
|
| cosine_mrr@10 | 0.1144 | |
|
| **cosine_map@100** | **0.139** | |
|
|
|
#### 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 | 0.0474 | |
|
| cosine_accuracy@3 | 0.1228 | |
|
| cosine_accuracy@5 | 0.2004 | |
|
| cosine_accuracy@10 | 0.3987 | |
|
| cosine_precision@1 | 0.0474 | |
|
| cosine_precision@3 | 0.0409 | |
|
| cosine_precision@5 | 0.0401 | |
|
| cosine_precision@10 | 0.0399 | |
|
| cosine_recall@1 | 0.0474 | |
|
| cosine_recall@3 | 0.1228 | |
|
| cosine_recall@5 | 0.2004 | |
|
| cosine_recall@10 | 0.3987 | |
|
| cosine_ndcg@10 | 0.1851 | |
|
| cosine_mrr@10 | 0.1217 | |
|
| **cosine_map@100** | **0.1457** | |
|
|
|
#### 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 | 0.0453 | |
|
| cosine_accuracy@3 | 0.1164 | |
|
| cosine_accuracy@5 | 0.1681 | |
|
| cosine_accuracy@10 | 0.3815 | |
|
| cosine_precision@1 | 0.0453 | |
|
| cosine_precision@3 | 0.0388 | |
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| cosine_precision@5 | 0.0336 | |
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| cosine_precision@10 | 0.0381 | |
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| cosine_recall@1 | 0.0453 | |
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| cosine_recall@3 | 0.1164 | |
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| cosine_recall@5 | 0.1681 | |
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| cosine_recall@10 | 0.3815 | |
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| cosine_ndcg@10 | 0.1753 | |
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| cosine_mrr@10 | 0.1146 | |
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| **cosine_map@100** | **0.1393** | |
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|
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.0388 | |
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| cosine_accuracy@3 | 0.097 | |
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| cosine_accuracy@5 | 0.153 | |
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| cosine_accuracy@10 | 0.347 | |
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| cosine_precision@1 | 0.0388 | |
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| cosine_precision@3 | 0.0323 | |
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| cosine_precision@5 | 0.0306 | |
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| cosine_precision@10 | 0.0347 | |
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| cosine_recall@1 | 0.0388 | |
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| cosine_recall@3 | 0.097 | |
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| cosine_recall@5 | 0.153 | |
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| cosine_recall@10 | 0.347 | |
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| cosine_ndcg@10 | 0.1569 | |
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| cosine_mrr@10 | 0.1011 | |
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| **cosine_map@100** | **0.1268** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
|
### Training Dataset |
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|
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#### Unnamed Dataset |
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|
|
|
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* Size: 4,173 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 10 tokens</li><li>mean: 60.84 tokens</li><li>max: 206 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.34 tokens</li><li>max: 53 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------| |
|
| <code>L'objectiu principal de la persona coordinadora de colònia felina és garantir el benestar dels animals de la colònia.</code> | <code>Quin és l'objectiu principal de la persona coordinadora de colònia felina?</code> | |
|
| <code>Es tracta d'una sala amb capacitat per a 125 persones, equipada amb un petit escenari, sistema de sonorització, pantalla per a projeccions, camerins i serveis higiènics (WC).</code> | <code>Quin és el nombre de persones que pot acollir la sala d'actes del Casal Municipal de la Gent Gran de Sitges?</code> | |
|
| <code>Aquest ajut pretén fomentar l’associacionisme empresarial local, per tal de disposar d’agrupacions, gremis o associacions representatives de l’activitat empresarial del municipi.</code> | <code>Quin és el paper de les empreses en aquest ajut?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.2 |
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- `bf16`: True |
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- `tf32`: False |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
|
|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
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- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.2 |
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- `warmup_steps`: 0 |
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- `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 |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: False |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `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 |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
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- `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 |
|
- `eval_on_start`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.6130 | 10 | 10.8464 | - | - | - | - | - | |
|
| 0.9808 | 16 | - | 0.1060 | 0.1088 | 0.1067 | 0.0984 | 0.1074 | |
|
| 1.2261 | 20 | 3.5261 | - | - | - | - | - | |
|
| 1.8391 | 30 | 1.4363 | - | - | - | - | - | |
|
| 1.9617 | 32 | - | 0.1406 | 0.1468 | 0.1356 | 0.1395 | 0.1373 | |
|
| 2.4521 | 40 | 0.5627 | - | - | - | - | - | |
|
| 2.9425 | 48 | - | 0.1377 | 0.1418 | 0.1427 | 0.1322 | 0.1437 | |
|
| 3.0651 | 50 | 0.2727 | - | - | - | - | - | |
|
| 3.6782 | 60 | 0.1297 | - | - | - | - | - | |
|
| **3.9234** | **64** | **-** | **0.1393** | **0.1457** | **0.139** | **0.1268** | **0.1462** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.34.0.dev0 |
|
- Datasets: 2.21.0 |
|
- 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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<!-- |
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*Clearly define terms in order to be accessible across audiences.* |
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<!-- |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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