|
--- |
|
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:6399 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Instal·lació de tendals. |
|
sentences: |
|
- Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit? |
|
- Quin és el període en què es produeix la comunicació de tancament puntual d’una |
|
activitat? |
|
- Quin és el benefici del volant històric de convivència? |
|
- source_sentence: Ajuts econòmics destinats a reforçar les activitats econòmiques |
|
amb suspensió o limitació d’obertura al públic i per finançar les despeses de |
|
lloguer o hipoteca per empreses i/o establiments comercials |
|
sentences: |
|
- Quin és el tràmit per a realitzar una obra que canvia la distribució d’un local |
|
comercial? |
|
- Quan cal sol·licitar l'informe previ en matèria d'incendis? |
|
- Quin és el benefici dels ajuts econòmics per als treballadors? |
|
- source_sentence: L'Ajuntament concedirà als empleats municipals que tinguin al seu |
|
càrrec familiars amb discapacitat física, psíquica o sensorial, un ajut especial |
|
que es reportarà mensualment segons el grau de discapacitat. |
|
sentences: |
|
- Quin és el benefici que es reporta mensualment? |
|
- Quin és el resultat de la comunicació de canvi de titularitat a l'Ajuntament? |
|
- Quin és el requisit per renovar la inscripció en el Registre municipal de sol·licitants |
|
d'habitatge amb protecció oficial de Sitges? |
|
- source_sentence: 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. |
|
sentences: |
|
- Quin és el límit de potència instal·lada per a les instal·lacions de plaques solars |
|
en sòl urbà? |
|
- Quin és el contingut del Padró Municipal d'Habitants? |
|
- Quin és el resultat esperat de la gestió de les colònies felines? |
|
- source_sentence: Els comerços locals obtenen un benefici principal de la implementació |
|
del projecte d'implantació i ús de la targeta de fidelització del comerç local |
|
de Sitges, que és la possibilitat d'augmentar les vendes i la fidelització dels |
|
clients. |
|
sentences: |
|
- Quin és el benefici que els comerços locals obtenen de la implementació del projecte |
|
d'implantació i ús de la targeta de fidelització? |
|
- Quin és el pla d'ordenació urbanística municipal que regula l'ús d'habitatges |
|
d'ús turístic de Sitges? |
|
- Quin és el propòsit de la deixalleria municipal per a l’ambient? |
|
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: 0.13305203938115331 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.26244725738396624 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.35358649789029534 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5243319268635724 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.13305203938115331 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08748241912798875 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07071729957805907 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05243319268635724 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.13305203938115331 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.26244725738396624 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.35358649789029534 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5243319268635724 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2985567963545146 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23013316812894896 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2512708543031996 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.13220815752461323 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2630098452883263 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3541490857946554 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5285513361462728 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.13220815752461323 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08766994842944209 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07082981715893108 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05285513361462728 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.13220815752461323 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2630098452883263 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3541490857946554 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5285513361462728 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.30111353887210784 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2321642890630236 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2529696660722769 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1341772151898734 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.26554149085794654 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3589310829817159 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5257383966244725 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1341772151898734 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08851383028598217 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07178621659634317 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05257383966244726 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1341772151898734 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.26554149085794654 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3589310829817159 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5257383966244725 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3010502512929789 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23285647310963767 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.25376075028724965 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.12658227848101267 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.26329113924050634 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3563994374120956 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5229254571026722 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.12658227848101267 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08776371308016878 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07127988748241912 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05229254571026722 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.12658227848101267 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.26329113924050634 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3563994374120956 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5229254571026722 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2971826978005507 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.22852298350188655 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24963995627964844 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.12742616033755275 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2683544303797468 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.35527426160337555 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5209563994374121 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.12742616033755275 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08945147679324894 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0710548523206751 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05209563994374121 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.12742616033755275 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2683544303797468 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.35527426160337555 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5209563994374121 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2973178953118737 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.22926059875426977 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2507076323664793 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.12236286919831224 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2545710267229255 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3440225035161744 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5164556962025316 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.12236286919831224 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0848570089076418 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06880450070323489 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05164556962025317 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.12236286919831224 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2545710267229255 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3440225035161744 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5164556962025316 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.29092273297262244 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.22250820440693853 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2429016668571107 |
|
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-sitges-003-10ep") |
|
# Run inference |
|
sentences = [ |
|
"Els comerços locals obtenen un benefici principal de la implementació del projecte d'implantació i ús de la targeta de fidelització del comerç local de Sitges, que és la possibilitat d'augmentar les vendes i la fidelització dels clients.", |
|
"Quin és el benefici que els comerços locals obtenen de la implementació del projecte d'implantació i ús de la targeta de fidelització?", |
|
'Quin és el propòsit de la deixalleria municipal per a l’ambient?', |
|
] |
|
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 | 0.1331 | |
|
| cosine_accuracy@3 | 0.2624 | |
|
| cosine_accuracy@5 | 0.3536 | |
|
| cosine_accuracy@10 | 0.5243 | |
|
| cosine_precision@1 | 0.1331 | |
|
| cosine_precision@3 | 0.0875 | |
|
| cosine_precision@5 | 0.0707 | |
|
| cosine_precision@10 | 0.0524 | |
|
| cosine_recall@1 | 0.1331 | |
|
| cosine_recall@3 | 0.2624 | |
|
| cosine_recall@5 | 0.3536 | |
|
| cosine_recall@10 | 0.5243 | |
|
| cosine_ndcg@10 | 0.2986 | |
|
| cosine_mrr@10 | 0.2301 | |
|
| **cosine_map@100** | **0.2513** | |
|
|
|
#### 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 | 0.1322 | |
|
| cosine_accuracy@3 | 0.263 | |
|
| cosine_accuracy@5 | 0.3541 | |
|
| cosine_accuracy@10 | 0.5286 | |
|
| cosine_precision@1 | 0.1322 | |
|
| cosine_precision@3 | 0.0877 | |
|
| cosine_precision@5 | 0.0708 | |
|
| cosine_precision@10 | 0.0529 | |
|
| cosine_recall@1 | 0.1322 | |
|
| cosine_recall@3 | 0.263 | |
|
| cosine_recall@5 | 0.3541 | |
|
| cosine_recall@10 | 0.5286 | |
|
| cosine_ndcg@10 | 0.3011 | |
|
| cosine_mrr@10 | 0.2322 | |
|
| **cosine_map@100** | **0.253** | |
|
|
|
#### 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 | 0.1342 | |
|
| cosine_accuracy@3 | 0.2655 | |
|
| cosine_accuracy@5 | 0.3589 | |
|
| cosine_accuracy@10 | 0.5257 | |
|
| cosine_precision@1 | 0.1342 | |
|
| cosine_precision@3 | 0.0885 | |
|
| cosine_precision@5 | 0.0718 | |
|
| cosine_precision@10 | 0.0526 | |
|
| cosine_recall@1 | 0.1342 | |
|
| cosine_recall@3 | 0.2655 | |
|
| cosine_recall@5 | 0.3589 | |
|
| cosine_recall@10 | 0.5257 | |
|
| cosine_ndcg@10 | 0.3011 | |
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| cosine_mrr@10 | 0.2329 | |
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| **cosine_map@100** | **0.2538** | |
|
|
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#### 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 | 0.1266 | |
|
| cosine_accuracy@3 | 0.2633 | |
|
| cosine_accuracy@5 | 0.3564 | |
|
| cosine_accuracy@10 | 0.5229 | |
|
| cosine_precision@1 | 0.1266 | |
|
| cosine_precision@3 | 0.0878 | |
|
| cosine_precision@5 | 0.0713 | |
|
| cosine_precision@10 | 0.0523 | |
|
| cosine_recall@1 | 0.1266 | |
|
| cosine_recall@3 | 0.2633 | |
|
| cosine_recall@5 | 0.3564 | |
|
| cosine_recall@10 | 0.5229 | |
|
| cosine_ndcg@10 | 0.2972 | |
|
| cosine_mrr@10 | 0.2285 | |
|
| **cosine_map@100** | **0.2496** | |
|
|
|
#### 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 | 0.1274 | |
|
| cosine_accuracy@3 | 0.2684 | |
|
| cosine_accuracy@5 | 0.3553 | |
|
| cosine_accuracy@10 | 0.521 | |
|
| cosine_precision@1 | 0.1274 | |
|
| cosine_precision@3 | 0.0895 | |
|
| cosine_precision@5 | 0.0711 | |
|
| cosine_precision@10 | 0.0521 | |
|
| cosine_recall@1 | 0.1274 | |
|
| cosine_recall@3 | 0.2684 | |
|
| cosine_recall@5 | 0.3553 | |
|
| cosine_recall@10 | 0.521 | |
|
| cosine_ndcg@10 | 0.2973 | |
|
| cosine_mrr@10 | 0.2293 | |
|
| **cosine_map@100** | **0.2507** | |
|
|
|
#### 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 | 0.1224 | |
|
| cosine_accuracy@3 | 0.2546 | |
|
| cosine_accuracy@5 | 0.344 | |
|
| cosine_accuracy@10 | 0.5165 | |
|
| cosine_precision@1 | 0.1224 | |
|
| cosine_precision@3 | 0.0849 | |
|
| cosine_precision@5 | 0.0688 | |
|
| cosine_precision@10 | 0.0516 | |
|
| cosine_recall@1 | 0.1224 | |
|
| cosine_recall@3 | 0.2546 | |
|
| cosine_recall@5 | 0.344 | |
|
| cosine_recall@10 | 0.5165 | |
|
| cosine_ndcg@10 | 0.2909 | |
|
| cosine_mrr@10 | 0.2225 | |
|
| **cosine_map@100** | **0.2429** | |
|
|
|
<!-- |
|
## 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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<!-- |
|
### 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 |
|
* Size: 6,399 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: 9 tokens</li><li>mean: 49.44 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.17 tokens</li><li>max: 48 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges.</code> | <code>Quin és el benefici de les subvencions per a les entitats esportives?</code> | |
|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el període d'execució dels projectes i activitats esportives?</code> | |
|
| <code>Certificat on s'indica el nombre d'habitatges que configuren el padró de l'Impost sobre Béns Immobles del municipi o bé d'una part d'aquest.</code> | <code>Quin és el contingut del certificat del nombre d'habitatges?</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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|
|
- `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`: 10 |
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- `lr_scheduler_type`: cosine |
|
- `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 |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
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- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 10 |
|
- `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 |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: 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 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
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- `label_names`: None |
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- `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 |
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- `adafactor`: False |
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- `group_by_length`: False |
|
- `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 |
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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 |
|
- `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 |
|
- `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> |
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|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | 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 | |
|
|:--------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.4 | 10 | 3.5464 | - | - | - | - | - | - | |
|
| 0.8 | 20 | 2.3861 | - | - | - | - | - | - | |
|
| 1.0 | 25 | - | 0.2327 | 0.2144 | 0.2252 | 0.2286 | 0.1938 | 0.2329 | |
|
| 1.1975 | 30 | 1.8712 | - | - | - | - | - | - | |
|
| 1.5975 | 40 | 1.3322 | - | - | - | - | - | - | |
|
| 1.9975 | 50 | 0.9412 | 0.2410 | 0.2310 | 0.2383 | 0.2415 | 0.2236 | 0.2436 | |
|
| 2.395 | 60 | 0.806 | - | - | - | - | - | - | |
|
| 2.795 | 70 | 0.5024 | - | - | - | - | - | - | |
|
| 2.995 | 75 | - | 0.2451 | 0.2384 | 0.2455 | 0.2487 | 0.2323 | 0.2423 | |
|
| 3.1925 | 80 | 0.4259 | - | - | - | - | - | - | |
|
| 3.5925 | 90 | 0.3556 | - | - | - | - | - | - | |
|
| 3.9925 | 100 | 0.2555 | 0.2477 | 0.2443 | 0.2417 | 0.2485 | 0.2369 | 0.2470 | |
|
| 4.39 | 110 | 0.2611 | - | - | - | - | - | - | |
|
| 4.79 | 120 | 0.1939 | - | - | - | - | - | - | |
|
| 4.99 | 125 | - | 0.2490 | 0.2425 | 0.2479 | 0.2485 | 0.2386 | 0.2495 | |
|
| 5.1875 | 130 | 0.2021 | - | - | - | - | - | - | |
|
| 5.5875 | 140 | 0.1537 | - | - | - | - | - | - | |
|
| 5.9875 | 150 | 0.1277 | 0.2535 | 0.2491 | 0.2491 | 0.2534 | 0.2403 | 0.2541 | |
|
| 6.385 | 160 | 0.1213 | - | - | - | - | - | - | |
|
| 6.785 | 170 | 0.1035 | - | - | - | - | - | - | |
|
| 6.985 | 175 | - | 0.2513 | 0.2493 | 0.2435 | 0.2515 | 0.2380 | 0.2528 | |
|
| 7.1825 | 180 | 0.0965 | - | - | - | - | - | - | |
|
| 7.5825 | 190 | 0.0861 | - | - | - | - | - | - | |
|
| 7.9825 | 200 | 0.0794 | 0.2529 | 0.2536 | 0.2526 | 0.2545 | 0.2438 | 0.2570 | |
|
| 8.38 | 210 | 0.0734 | - | - | - | - | - | - | |
|
| 8.78 | 220 | 0.066 | - | - | - | - | - | - | |
|
| **8.98** | **225** | **-** | **0.2538** | **0.2523** | **0.2519** | **0.2542** | **0.2457** | **0.2572** | |
|
| 9.1775 | 230 | 0.0731 | - | - | - | - | - | - | |
|
| 9.5775 | 240 | 0.0726 | - | - | - | - | - | - | |
|
| 9.9775 | 250 | 0.0632 | 0.2513 | 0.2507 | 0.2496 | 0.2538 | 0.2429 | 0.2530 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 0.35.0.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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
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|
*Clearly define terms in order to be accessible across audiences.* |
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