|
--- |
|
base_model: BAAI/bge-m3 |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:3814 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Sol·licitud de l'informe d'integració social per a la renovació |
|
o modificació de la residència. |
|
sentences: |
|
- Quin és el propòsit de la renovació o modificació de la residència? |
|
- Quin és el paper de l'Administració en la Declaració responsable d'obertura? |
|
- Quin és el lloc on es pot realitzar l'ocupació de la via pública? |
|
- source_sentence: Aquest tràmit permet obtenir la llicència d'ocupació de la via |
|
pública per a la instal·lació de grues desmuntables. |
|
sentences: |
|
- Quin és el propòsit de la consulta del Cens Electoral? |
|
- Quin és el tràmit necessari per a la instal·lació de grues desmuntables en una |
|
via pública? |
|
- Quines reclamacions es consideren en aquest tràmit? |
|
- source_sentence: 'Bonificacions: Persones amb discapacitat: bonificació 50%. Laboratori |
|
d''art: Preu: 15€/mes' |
|
sentences: |
|
- Quin és el preu del curs de Laboratori d'art per a persones amb discapacitat? |
|
- Quin és el paper de les oficines municipals d'atenció ciutadana en la renovació |
|
de la inscripció padronal? |
|
- Quin és el període en què les entitats i associacions registrades han de notificar |
|
les modificacions produïdes en les dades registrals? |
|
- source_sentence: Es tracta de la sol·licitud d'elaboració del certificat que justifica |
|
l'antiguitat i legalitat d'un immoble, document necessari en el moment de la venda, |
|
per poder-lo inscriure al Registre de la Propietat si no es va fer en finalitzar |
|
l'obra. |
|
sentences: |
|
- Què ha de fer el responsable en relació amb els destinataris quan es limita el |
|
tractament de dades personals? |
|
- Quin és el motiu pel qual es sol·licita el certificat d'antiguitat i legalitat |
|
urbanística en la venda d'un immoble? |
|
- Qui és el destinatari de la comunicació de canvi de titularitat d'activitats? |
|
- source_sentence: 'Laboratori d''art: D''octubre 2024 a maig de 2025. Horari: Dilluns |
|
de 17.30h a 19.00h' |
|
sentences: |
|
- Quin és el dia i hora del curs de Laboratori d'art? |
|
- Quin és el paper dels dipòsits o fiances en la garantia d'abocament controlat |
|
de runes? |
|
- On es pot sol·licitar la reserva especial d'estacionament? |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-m3 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 1024 |
|
type: dim_1024 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2153846153846154 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.27692307692307694 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.48846153846153845 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07179487179487179 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.055384615384615386 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.048846153846153845 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2153846153846154 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.27692307692307694 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.48846153846153845 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2612154031642473 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.193324175824176 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21923866500444808 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11923076923076924 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.23076923076923078 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.31153846153846154 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5307692307692308 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11923076923076924 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07692307692307693 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06230769230769231 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05307692307692307 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11923076923076924 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23076923076923078 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.31153846153846154 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5307692307692308 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2878219714456531 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21504578754578765 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.23782490878695842 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.12692307692307692 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.23846153846153847 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3269230769230769 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5269230769230769 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.12692307692307692 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07948717948717948 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06538461538461539 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05269230769230769 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.12692307692307692 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23846153846153847 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3269230769230769 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5269230769230769 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2920408684487264 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.22163461538461554 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24439125474069504 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2076923076923077 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3076923076923077 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49615384615384617 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06923076923076923 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06153846153846154 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04961538461538462 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2076923076923077 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3076923076923077 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49615384615384617 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.26493374179245505 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.195289987789988 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22019396693132914 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.13076923076923078 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.23076923076923078 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3423076923076923 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.55 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.13076923076923078 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07692307692307693 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06846153846153846 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05499999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.13076923076923078 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23076923076923078 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3423076923076923 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.55 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.30010874813387883 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2253495115995117 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2488774864299421 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2230769230769231 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2846153846153846 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49230769230769234 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07435897435897434 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05692307692307692 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04923076923076923 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10384615384615385 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2230769230769231 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2846153846153846 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49230769230769234 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2636327280635836 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19504273504273517 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21974930573072288 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-m3 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) 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. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 1024 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- json |
|
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): 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("adriansanz/ST-tramits-MONT-001-5ep") |
|
# Run inference |
|
sentences = [ |
|
"Laboratori d'art: D'octubre 2024 a maig de 2025. Horari: Dilluns de 17.30h a 19.00h", |
|
"Quin és el dia i hora del curs de Laboratori d'art?", |
|
"On es pot sol·licitar la reserva especial d'estacionament?", |
|
] |
|
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) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1038 | |
|
| cosine_accuracy@3 | 0.2154 | |
|
| cosine_accuracy@5 | 0.2769 | |
|
| cosine_accuracy@10 | 0.4885 | |
|
| cosine_precision@1 | 0.1038 | |
|
| cosine_precision@3 | 0.0718 | |
|
| cosine_precision@5 | 0.0554 | |
|
| cosine_precision@10 | 0.0488 | |
|
| cosine_recall@1 | 0.1038 | |
|
| cosine_recall@3 | 0.2154 | |
|
| cosine_recall@5 | 0.2769 | |
|
| cosine_recall@10 | 0.4885 | |
|
| cosine_ndcg@10 | 0.2612 | |
|
| cosine_mrr@10 | 0.1933 | |
|
| **cosine_map@100** | **0.2192** | |
|
|
|
#### 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 | 0.1192 | |
|
| cosine_accuracy@3 | 0.2308 | |
|
| cosine_accuracy@5 | 0.3115 | |
|
| cosine_accuracy@10 | 0.5308 | |
|
| cosine_precision@1 | 0.1192 | |
|
| cosine_precision@3 | 0.0769 | |
|
| cosine_precision@5 | 0.0623 | |
|
| cosine_precision@10 | 0.0531 | |
|
| cosine_recall@1 | 0.1192 | |
|
| cosine_recall@3 | 0.2308 | |
|
| cosine_recall@5 | 0.3115 | |
|
| cosine_recall@10 | 0.5308 | |
|
| cosine_ndcg@10 | 0.2878 | |
|
| cosine_mrr@10 | 0.215 | |
|
| **cosine_map@100** | **0.2378** | |
|
|
|
#### 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 | 0.1269 | |
|
| cosine_accuracy@3 | 0.2385 | |
|
| cosine_accuracy@5 | 0.3269 | |
|
| cosine_accuracy@10 | 0.5269 | |
|
| cosine_precision@1 | 0.1269 | |
|
| cosine_precision@3 | 0.0795 | |
|
| cosine_precision@5 | 0.0654 | |
|
| cosine_precision@10 | 0.0527 | |
|
| cosine_recall@1 | 0.1269 | |
|
| cosine_recall@3 | 0.2385 | |
|
| cosine_recall@5 | 0.3269 | |
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| cosine_recall@10 | 0.5269 | |
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| cosine_ndcg@10 | 0.292 | |
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| cosine_mrr@10 | 0.2216 | |
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| **cosine_map@100** | **0.2444** | |
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|
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#### Information Retrieval |
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* 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 | 0.1038 | |
|
| cosine_accuracy@3 | 0.2077 | |
|
| cosine_accuracy@5 | 0.3077 | |
|
| cosine_accuracy@10 | 0.4962 | |
|
| cosine_precision@1 | 0.1038 | |
|
| cosine_precision@3 | 0.0692 | |
|
| cosine_precision@5 | 0.0615 | |
|
| cosine_precision@10 | 0.0496 | |
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| cosine_recall@1 | 0.1038 | |
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| cosine_recall@3 | 0.2077 | |
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| cosine_recall@5 | 0.3077 | |
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| cosine_recall@10 | 0.4962 | |
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| cosine_ndcg@10 | 0.2649 | |
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| cosine_mrr@10 | 0.1953 | |
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| **cosine_map@100** | **0.2202** | |
|
|
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#### Information Retrieval |
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* 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 | 0.1308 | |
|
| cosine_accuracy@3 | 0.2308 | |
|
| cosine_accuracy@5 | 0.3423 | |
|
| cosine_accuracy@10 | 0.55 | |
|
| cosine_precision@1 | 0.1308 | |
|
| cosine_precision@3 | 0.0769 | |
|
| cosine_precision@5 | 0.0685 | |
|
| cosine_precision@10 | 0.055 | |
|
| cosine_recall@1 | 0.1308 | |
|
| cosine_recall@3 | 0.2308 | |
|
| cosine_recall@5 | 0.3423 | |
|
| cosine_recall@10 | 0.55 | |
|
| cosine_ndcg@10 | 0.3001 | |
|
| cosine_mrr@10 | 0.2253 | |
|
| **cosine_map@100** | **0.2489** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1038 | |
|
| cosine_accuracy@3 | 0.2231 | |
|
| cosine_accuracy@5 | 0.2846 | |
|
| cosine_accuracy@10 | 0.4923 | |
|
| cosine_precision@1 | 0.1038 | |
|
| cosine_precision@3 | 0.0744 | |
|
| cosine_precision@5 | 0.0569 | |
|
| cosine_precision@10 | 0.0492 | |
|
| cosine_recall@1 | 0.1038 | |
|
| cosine_recall@3 | 0.2231 | |
|
| cosine_recall@5 | 0.2846 | |
|
| cosine_recall@10 | 0.4923 | |
|
| cosine_ndcg@10 | 0.2636 | |
|
| cosine_mrr@10 | 0.195 | |
|
| **cosine_map@100** | **0.2197** | |
|
|
|
<!-- |
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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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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
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* Size: 3,814 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: 3 tokens</li><li>mean: 39.27 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.67 tokens</li><li>max: 50 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Aquest tràmit permet obtenir la llicència per a ocupació de la via pública per quioscs, casetes o parades (xurreries, gelats,...).</code> | <code>Quins són els requisits per obtenir la llicència d'ocupació de la via pública per a gelats?</code> | |
|
| <code>Aquest tràmit permet obtenir la llicència d'ocupació de la via pública per a la instal·lació de grues desmuntables.</code> | <code>Quin és el lloc on es pot obtenir la llicència d'ocupació de la via pública per a la instal·lació de grues desmuntables en una via pública?</code> | |
|
| <code>L’Espai Jove de Montgat disposa de dues sales, una aula, i una sala chill-out així com jardins i serveis adreçats als joves del municipi.</code> | <code>Quin és el propòsit de l'aula de l'Espai Jove de Montgat?</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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1024, |
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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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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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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.2 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
|
#### All Hyperparameters |
|
<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 |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `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`: 5 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.2 |
|
- `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 |
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- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
|
- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
|
- `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 |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
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- `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 |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.6695 | 10 | 3.4242 | - | - | - | - | - | - | |
|
| 0.9372 | 14 | - | 0.2075 | 0.2165 | 0.2078 | 0.1957 | 0.2050 | 0.1949 | |
|
| 1.3389 | 20 | 1.666 | - | - | - | - | - | - | |
|
| 1.9414 | 29 | - | 0.2145 | 0.2184 | 0.2248 | 0.2144 | 0.2244 | 0.2112 | |
|
| 2.0084 | 30 | 0.7666 | - | - | - | - | - | - | |
|
| 2.6778 | 40 | 0.4859 | - | - | - | - | - | - | |
|
| **2.9456** | **44** | **-** | **0.2263** | **0.2408** | **0.2234** | **0.2274** | **0.252** | **0.2313** | |
|
| 3.3473 | 50 | 0.277 | - | - | - | - | - | - | |
|
| 3.9498 | 59 | - | 0.2107 | 0.2359 | 0.2386 | 0.2275 | 0.2382 | 0.2246 | |
|
| 4.0167 | 60 | 0.2423 | - | - | - | - | - | - | |
|
| 4.6862 | 70 | 0.2281 | 0.2192 | 0.2378 | 0.2444 | 0.2202 | 0.2489 | 0.2197 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.2.0 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 1.1.0.dev0 |
|
- Datasets: 3.0.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
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## Glossary |
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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 |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
