|
--- |
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base_model: jeffwan/mmarco-mMiniLMv2-L12-H384-v1 |
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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: 'Aquelles persones (físiques o jurídiques) que es disposin a exercir |
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una de les següents activitats: ... Han de comunicar-ho a l''Ajuntament prèviament |
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a la data prevista de la seva obertura.' |
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sentences: |
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- Quin és el benefici que es pretén obtenir amb aquests ajuts econòmics per a les |
|
empreses d'hostaleria i restauració? |
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- Quin és el benefici del sistema de teleassistència per a les persones que viuen |
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amb altres persones amb discapacitat? |
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- Quin és el propòsit de la comunicació prèvia d'una activitat recreativa o un espectacle |
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públic? |
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- source_sentence: Les persones titulars d’activitats que generin residus comercials |
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o industrials assimilables als municipals, vindran obligats a acreditar davant |
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l’Ajuntament que tenen contractat un gestor autoritzat per la recollida, tractament |
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i eliminació dels residus que produeixi l’activitat corresponent. |
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sentences: |
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- Quin és el paper de l'Ajuntament en l'acreditació de recollida de residus? |
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- Quin és el benefici de les activitats d'animació socio-cultural? |
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- Quin és el benefici de l'ajut per a la creació de noves empreses? |
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- source_sentence: Modificació de sol·licitud de permís d'ocupació de la via pública |
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per filmacions, rodatges o sessions fotogràfiques. |
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sentences: |
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- Quin és el grau de discapacitat mínim per a rebre l'ajut de 300€ anuals? |
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- Quin és el requisit per a la constitució o modificació del règim de propietat |
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horitzontal? |
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- Quin és el tipus de permís que es modifica? |
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- source_sentence: El beneficiari és l'encarregat de complir les condicions de la |
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subvenció i de presentar els informes de seguiment del projecte. |
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sentences: |
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- Quin és el paper del beneficiari en el procés de subvencions? |
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- Quin és el càlcul dels interessos de demora en el fraccionament i l'ajornament? |
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- Quin és el període de temps en què es poden efectuar les despeses mèdiques per |
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a rebre l'ajuda? |
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- source_sentence: Aquest tràmit permet sol·licitar la llicència per a realitzar obres |
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d'excavació a la via pública per a la instal·lació o reparació d'infraestructures |
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de serveis i subministraments. |
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sentences: |
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- Quin és el paper de la via pública en aquest tràmit? |
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- Quin és el requisit principal per obtenir el certificat? |
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- Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística? |
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model-index: |
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- name: SentenceTransformer based on jeffwan/mmarco-mMiniLMv2-L12-H384-v1 |
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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: 0.036637931034482756 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.07974137931034483 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.11206896551724138 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.18318965517241378 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.02658045977011494 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.022413793103448276 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
|
value: 0.018318965517241378 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.07974137931034483 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.11206896551724138 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.18318965517241378 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.09775400592125581 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
|
value: 0.07205545292829774 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.08506410086235629 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
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: 0.036637931034482756 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.07974137931034483 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.11206896551724138 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.18318965517241378 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.02658045977011494 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.022413793103448276 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.018318965517241378 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.036637931034482756 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.07974137931034483 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.11206896551724138 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.18318965517241378 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.09775400592125581 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.07205545292829774 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.08506410086235629 |
|
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: 0.03879310344827586 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.08620689655172414 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.12284482758620689 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.20905172413793102 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.03879310344827586 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.028735632183908042 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.02456896551724138 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.020905172413793104 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.03879310344827586 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.08620689655172414 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.12284482758620689 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.20905172413793102 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.10886950781001367 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.07890325670498083 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.09261787732124524 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
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type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.040948275862068964 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.08836206896551724 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.11637931034482758 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.1961206896551724 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.040948275862068964 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.029454022988505746 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.02327586206896552 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.01961206896551724 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.040948275862068964 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.08836206896551724 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.11637931034482758 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.1961206896551724 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.10531304697568825 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.07822078544061303 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.09314633548904587 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.040948275862068964 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.09051724137931035 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.11206896551724138 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.18318965517241378 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.040948275862068964 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03017241379310345 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.022413793103448276 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.018318965517241378 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.040948275862068964 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.09051724137931035 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.11206896551724138 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.18318965517241378 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1000598523425774 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.07514453338806787 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.09004925619574798 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on jeffwan/mmarco-mMiniLMv2-L12-H384-v1 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jeffwan/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/jeffwan/mmarco-mMiniLMv2-L12-H384-v1). It maps sentences & paragraphs to a 384-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:** [jeffwan/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/jeffwan/mmarco-mMiniLMv2-L12-H384-v1) <!-- at revision 76559e02cfe153b000907ce78044543f132562e9 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 384 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: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 384, '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-rerank") |
|
# Run inference |
|
sentences = [ |
|
"Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments.", |
|
'Quin és el paper de la via pública en aquest tràmit?', |
|
"Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?", |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 384] |
|
|
|
# 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 | 0.0366 | |
|
| cosine_accuracy@3 | 0.0797 | |
|
| cosine_accuracy@5 | 0.1121 | |
|
| cosine_accuracy@10 | 0.1832 | |
|
| cosine_precision@1 | 0.0366 | |
|
| cosine_precision@3 | 0.0266 | |
|
| cosine_precision@5 | 0.0224 | |
|
| cosine_precision@10 | 0.0183 | |
|
| cosine_recall@1 | 0.0366 | |
|
| cosine_recall@3 | 0.0797 | |
|
| cosine_recall@5 | 0.1121 | |
|
| cosine_recall@10 | 0.1832 | |
|
| cosine_ndcg@10 | 0.0978 | |
|
| cosine_mrr@10 | 0.0721 | |
|
| **cosine_map@100** | **0.0851** | |
|
|
|
#### 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 | 0.0366 | |
|
| cosine_accuracy@3 | 0.0797 | |
|
| cosine_accuracy@5 | 0.1121 | |
|
| cosine_accuracy@10 | 0.1832 | |
|
| cosine_precision@1 | 0.0366 | |
|
| cosine_precision@3 | 0.0266 | |
|
| cosine_precision@5 | 0.0224 | |
|
| cosine_precision@10 | 0.0183 | |
|
| cosine_recall@1 | 0.0366 | |
|
| cosine_recall@3 | 0.0797 | |
|
| cosine_recall@5 | 0.1121 | |
|
| cosine_recall@10 | 0.1832 | |
|
| cosine_ndcg@10 | 0.0978 | |
|
| cosine_mrr@10 | 0.0721 | |
|
| **cosine_map@100** | **0.0851** | |
|
|
|
#### 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 | 0.0388 | |
|
| cosine_accuracy@3 | 0.0862 | |
|
| cosine_accuracy@5 | 0.1228 | |
|
| cosine_accuracy@10 | 0.2091 | |
|
| cosine_precision@1 | 0.0388 | |
|
| cosine_precision@3 | 0.0287 | |
|
| cosine_precision@5 | 0.0246 | |
|
| cosine_precision@10 | 0.0209 | |
|
| cosine_recall@1 | 0.0388 | |
|
| cosine_recall@3 | 0.0862 | |
|
| cosine_recall@5 | 0.1228 | |
|
| cosine_recall@10 | 0.2091 | |
|
| cosine_ndcg@10 | 0.1089 | |
|
| cosine_mrr@10 | 0.0789 | |
|
| **cosine_map@100** | **0.0926** | |
|
|
|
#### 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 | 0.0409 | |
|
| cosine_accuracy@3 | 0.0884 | |
|
| cosine_accuracy@5 | 0.1164 | |
|
| cosine_accuracy@10 | 0.1961 | |
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| cosine_precision@1 | 0.0409 | |
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| cosine_precision@3 | 0.0295 | |
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| cosine_precision@5 | 0.0233 | |
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| cosine_precision@10 | 0.0196 | |
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| cosine_recall@1 | 0.0409 | |
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| cosine_recall@3 | 0.0884 | |
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| cosine_recall@5 | 0.1164 | |
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| cosine_recall@10 | 0.1961 | |
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| cosine_ndcg@10 | 0.1053 | |
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| cosine_mrr@10 | 0.0782 | |
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| **cosine_map@100** | **0.0931** | |
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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) |
|
|
|
| Metric | Value | |
|
|:--------------------|:---------| |
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| cosine_accuracy@1 | 0.0409 | |
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| cosine_accuracy@3 | 0.0905 | |
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| cosine_accuracy@5 | 0.1121 | |
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| cosine_accuracy@10 | 0.1832 | |
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| cosine_precision@1 | 0.0409 | |
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| cosine_precision@3 | 0.0302 | |
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| cosine_precision@5 | 0.0224 | |
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| cosine_precision@10 | 0.0183 | |
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| cosine_recall@1 | 0.0409 | |
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| cosine_recall@3 | 0.0905 | |
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| cosine_recall@5 | 0.1121 | |
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| cosine_recall@10 | 0.1832 | |
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| cosine_ndcg@10 | 0.1001 | |
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| cosine_mrr@10 | 0.0751 | |
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| **cosine_map@100** | **0.09** | |
|
|
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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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|
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### Training Dataset |
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#### Unnamed Dataset |
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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 | |
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| details | <ul><li>min: 9 tokens</li><li>mean: 49.38 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.0 tokens</li><li>max: 48 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
|
| <code>Havent-se d'acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d'infants, de l'educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats.</code> | <code>Quin és el requisit per acreditar la llar d'infants?</code> | |
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| <code>El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició.</code> | <code>Quin és el propòsit del volant històric de convivència?</code> | |
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| <code>Instal·lació de tanques sense obra.</code> | <code>Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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384, |
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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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], |
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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`: 5 |
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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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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 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`: 5 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.2 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
|
- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: 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 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
|
- `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 |
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- `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> |
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|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.6130 | 10 | 11.7801 | - | - | - | - | - | |
|
| 0.9808 | 16 | - | 0.0132 | 0.0103 | 0.0105 | 0.0116 | 0.0105 | |
|
| 1.2261 | 20 | 10.5594 | - | - | - | - | - | |
|
| 1.8391 | 30 | 9.0859 | - | - | - | - | - | |
|
| 1.9617 | 32 | - | 0.0337 | 0.0302 | 0.0298 | 0.0323 | 0.0298 | |
|
| 2.4521 | 40 | 7.5747 | - | - | - | - | - | |
|
| 2.9425 | 48 | - | 0.0811 | 0.0765 | 0.0679 | 0.0742 | 0.0679 | |
|
| 3.0651 | 50 | 5.7656 | - | - | - | - | - | |
|
| 3.6782 | 60 | 4.7495 | - | - | - | - | - | |
|
| 3.9847 | 65 | - | 0.0926 | 0.0929 | 0.0822 | 0.0886 | 0.0822 | |
|
| 4.2912 | 70 | 4.1026 | - | - | - | - | - | |
|
| **4.9042** | **80** | **3.8201** | **0.0931** | **0.0926** | **0.0851** | **0.09** | **0.0851** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
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- 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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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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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