|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
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:4173 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Les respectives convocatòries determinaran les parades disponibles |
|
així com les seves característiques i descripció. |
|
sentences: |
|
- Quin és l'objectiu secundari dels ajuts per a la creació de noves empreses? |
|
- Qui és responsable de la resolució de la situació en un domini particular? |
|
- Quin és el paper de les convocatòries? |
|
- source_sentence: Cal revisar la informació i els terminis de la convocatòria específica |
|
de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges. |
|
sentences: |
|
- Quin és el paper de la Seu electrònica de l'Ajuntament de Sitges en un procés |
|
de selecció de personal? |
|
- Quin és l'objectiu principal de sol·licitar el certificat d'antiguitat i legalitat |
|
d'una finca? |
|
- Quin és el propòsit dels ajuts a la contractació laboral en relació amb l'ocupació? |
|
- source_sentence: Per a poder rebre les subvencions pel suport educatiu a les escoles |
|
públiques de Sitges, els beneficiaris han de presentar un projecte d'acció que |
|
compleixi els requisits establerts en la convocatòria corresponent. |
|
sentences: |
|
- Quin és el propòsit de la sol·licitud d'ajuts? |
|
- Quin és el paper de l'Ajuntament de Sitges en la Fira de la Vila del Llibre de |
|
Sitges? |
|
- Quin és el requisit per a poder rebre les subvencions pel suport educatiu a les |
|
escoles públiques de Sitges? |
|
- source_sentence: Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar |
|
l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació |
|
prèvia corresponent. |
|
sentences: |
|
- Quin és el paper de la comunicació prèvia en la prevenció d'incendis? |
|
- Quins són els requisits per a presentar una sol·licitud de subvenció? |
|
- Quins animals es consideren animals de companyia? |
|
- source_sentence: El termini per a la presentació de les sol·licituds de modificació |
|
del projecte o activitat subvencionat és de 15 dies naturals abans de la finalització |
|
del projecte o activitat. |
|
sentences: |
|
- Quin és el termini per a la presentació de les sol·licituds de modificació del |
|
projecte o activitat subvencionat? |
|
- Quins són els tres tipus de llicència d'obra? |
|
- Quin és el registre on es troben les dades d'inscripció? |
|
model-index: |
|
- name: BGE base Financial Matryoshka |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11637931034482758 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1810344827586207 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.35560344827586204 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03879310344827586 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036206896551724134 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03556034482758621 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11637931034482758 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1810344827586207 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.35560344827586204 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.17546345429803745 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.12245227832512329 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1487513151351338 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.10991379310344827 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17025862068965517 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.35560344827586204 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.036637931034482756 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03405172413793103 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03556034482758621 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.10991379310344827 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17025862068965517 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.35560344827586204 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.17281692680622274 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11932898877942 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.145505253139907 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05603448275862069 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12284482758620689 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1724137931034483 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.34051724137931033 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05603448275862069 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.040948275862068964 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.034482758620689655 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03405172413793103 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05603448275862069 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12284482758620689 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1724137931034483 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.34051724137931033 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.16797293983212122 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11677955665024647 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14311504496457605 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11422413793103449 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18318965517241378 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.31896551724137934 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.038074712643678156 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036637931034482756 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03189655172413793 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05172413793103448 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11422413793103449 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18318965517241378 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.31896551724137934 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15889833336121337 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11119406814449927 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1376499182467716 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.10560344827586207 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.16594827586206898 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.30603448275862066 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.035201149425287355 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0331896551724138 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.030603448275862068 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04525862068965517 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.10560344827586207 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.16594827586206898 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.30603448275862066 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.14903489989981042 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10241516146688567 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12594670041141745 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(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/sitges2608") |
|
# Run inference |
|
sentences = [ |
|
'El termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat és de 15 dies naturals abans de la finalització del projecte o activitat.', |
|
'Quin és el termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat?', |
|
"Quin és el registre on es troben les dades d'inscripció?", |
|
] |
|
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 | 0.0625 | |
|
| cosine_accuracy@3 | 0.1164 | |
|
| cosine_accuracy@5 | 0.181 | |
|
| cosine_accuracy@10 | 0.3556 | |
|
| cosine_precision@1 | 0.0625 | |
|
| cosine_precision@3 | 0.0388 | |
|
| cosine_precision@5 | 0.0362 | |
|
| cosine_precision@10 | 0.0356 | |
|
| cosine_recall@1 | 0.0625 | |
|
| cosine_recall@3 | 0.1164 | |
|
| cosine_recall@5 | 0.181 | |
|
| cosine_recall@10 | 0.3556 | |
|
| cosine_ndcg@10 | 0.1755 | |
|
| cosine_mrr@10 | 0.1225 | |
|
| **cosine_map@100** | **0.1488** | |
|
|
|
#### 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 | 0.0625 | |
|
| cosine_accuracy@3 | 0.1099 | |
|
| cosine_accuracy@5 | 0.1703 | |
|
| cosine_accuracy@10 | 0.3556 | |
|
| cosine_precision@1 | 0.0625 | |
|
| cosine_precision@3 | 0.0366 | |
|
| cosine_precision@5 | 0.0341 | |
|
| cosine_precision@10 | 0.0356 | |
|
| cosine_recall@1 | 0.0625 | |
|
| cosine_recall@3 | 0.1099 | |
|
| cosine_recall@5 | 0.1703 | |
|
| cosine_recall@10 | 0.3556 | |
|
| cosine_ndcg@10 | 0.1728 | |
|
| cosine_mrr@10 | 0.1193 | |
|
| **cosine_map@100** | **0.1455** | |
|
|
|
#### 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 | 0.056 | |
|
| cosine_accuracy@3 | 0.1228 | |
|
| cosine_accuracy@5 | 0.1724 | |
|
| cosine_accuracy@10 | 0.3405 | |
|
| cosine_precision@1 | 0.056 | |
|
| cosine_precision@3 | 0.0409 | |
|
| cosine_precision@5 | 0.0345 | |
|
| cosine_precision@10 | 0.0341 | |
|
| cosine_recall@1 | 0.056 | |
|
| cosine_recall@3 | 0.1228 | |
|
| cosine_recall@5 | 0.1724 | |
|
| cosine_recall@10 | 0.3405 | |
|
| cosine_ndcg@10 | 0.168 | |
|
| cosine_mrr@10 | 0.1168 | |
|
| **cosine_map@100** | **0.1431** | |
|
|
|
#### 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 | 0.0517 | |
|
| cosine_accuracy@3 | 0.1142 | |
|
| cosine_accuracy@5 | 0.1832 | |
|
| cosine_accuracy@10 | 0.319 | |
|
| cosine_precision@1 | 0.0517 | |
|
| cosine_precision@3 | 0.0381 | |
|
| cosine_precision@5 | 0.0366 | |
|
| cosine_precision@10 | 0.0319 | |
|
| cosine_recall@1 | 0.0517 | |
|
| cosine_recall@3 | 0.1142 | |
|
| cosine_recall@5 | 0.1832 | |
|
| cosine_recall@10 | 0.319 | |
|
| cosine_ndcg@10 | 0.1589 | |
|
| cosine_mrr@10 | 0.1112 | |
|
| **cosine_map@100** | **0.1376** | |
|
|
|
#### 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 | 0.0453 | |
|
| cosine_accuracy@3 | 0.1056 | |
|
| cosine_accuracy@5 | 0.1659 | |
|
| cosine_accuracy@10 | 0.306 | |
|
| cosine_precision@1 | 0.0453 | |
|
| cosine_precision@3 | 0.0352 | |
|
| cosine_precision@5 | 0.0332 | |
|
| cosine_precision@10 | 0.0306 | |
|
| cosine_recall@1 | 0.0453 | |
|
| cosine_recall@3 | 0.1056 | |
|
| cosine_recall@5 | 0.1659 | |
|
| cosine_recall@10 | 0.306 | |
|
| cosine_ndcg@10 | 0.149 | |
|
| cosine_mrr@10 | 0.1024 | |
|
| **cosine_map@100** | **0.1259** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 4,173 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 66.25 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 28.12 tokens</li><li>max: 62 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
| <code>La persona titular d'una llicència de vehicle lleuger per al servei públic (auto-taxi), en produïr-se un canvi de vehicle, ha de notificar a l'Ajuntament les dades del nou vehicle.</code> | <code>Quin és el propòsit de la notificació de les dades del nou vehicle?</code> | |
|
| <code>S'entén per garantia l'ingrés a la Tresoreria de l'Ajuntament d'una quantitat econòmica que garanteix el compliment d'una obligació adquirida amb aquest (garanties de concursos o licitacions, fraccionaments de tributs en via executiva, reposició de paviments per obres, etc.).</code> | <code>Què s'entén per garantia a l'Ajuntament de Sitges?</code> | |
|
| <code>L'ús d'espais del Centre Cultural Miramar per a la realització d'exposicions.</code> | <code>Quin és el centre cultural on es poden realitzar les exposicions d'art?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: False |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: False |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.9771 | 8 | - | 0.1210 | 0.1384 | 0.1341 | 0.1002 | 0.1376 | |
|
| 1.2137 | 10 | 7.5469 | - | - | - | - | - | |
|
| **1.9466** | **16** | **-** | **0.136** | **0.1404** | **0.1443** | **0.1249** | **0.1414** | |
|
| 2.4275 | 20 | 4.0024 | - | - | - | - | - | |
|
| 2.9160 | 24 | - | 0.1388 | 0.1460 | 0.1446 | 0.1278 | 0.1436 | |
|
| 3.6412 | 30 | 3.2149 | - | - | - | - | - | |
|
| 3.8855 | 32 | - | 0.1376 | 0.1431 | 0.1455 | 0.1259 | 0.1488 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## 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.* |
|
--> |
