|
--- |
|
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:2884 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'P.2 El contingut mínim del projecte és: a) Memòria justificativa, |
|
amb: - La descripció de la finca o finques d''origen amb indicació de les seves |
|
superfícies i llindars. - La descripció de les finques resultants, la seva superfície |
|
i els seus llindars...' |
|
sentences: |
|
- Quin és el format de sortida de la informació sobre aquesta ciutat? |
|
- Quins són els requisits bàsics per sol·licitar la subvenció? |
|
- Quin és el contingut mínim del projecte de parcel·lació? |
|
- source_sentence: 'La Comissió de Garanties té dues funcions: aclarir els dubtes |
|
interpretatius que es plantegin en l''aplicació del mateix.' |
|
sentences: |
|
- Quines són les dues funcions de la Comissió de Garanties? |
|
- Quin és el propòsit d'una llicència d'obres mitjanes en relació amb els moviments |
|
de terres? |
|
- Quin és el nom del conjunt d'habitatges que es troba al terme municipal de Viladecans? |
|
- source_sentence: 'No cal presentar al·legacions en els següents casos: En el cas |
|
que la baixa s’hagués iniciat per manca de confirmació bastarà amb realitzar el |
|
tràmit de confirmació per que l’expedient de baixa s’arxivi, sempre i quan continuï |
|
residint al mateix domicili.' |
|
sentences: |
|
- És necessari que una persona tècnica professional empleni els documents d'autocontrol? |
|
- Quin és el tema principal de la secció d'horari d'obertura i tancament? |
|
- Quan no cal presentar al·legacions en un expedient de baixa d'ofici? |
|
- source_sentence: L'Ajuntament de Sant Boi obre convocatòria de concessió de beques |
|
per col·laborar en el finançament de projectes i activitats dels i de les joves |
|
del municipi en diferents àmbits i promoure i facilitar els processos d'emancipació |
|
juvenils i garantir la igualtat d'oportunitats i la cohesió social entre la població |
|
jove. |
|
sentences: |
|
- Quin és el propòsit del servei de llista d'espera? |
|
- Quin és el problema que es tracta en aquest apartat? |
|
- Quin és l'objectiu de les beques per a joves 2024 de l'Ajuntament de Sant Boi? |
|
- source_sentence: Empadronament d'un/a menor en un domicili diferent al domicili |
|
dels progenitors - Amb autorització de les persones progenitores |
|
sentences: |
|
- Quin és el límit de temps màxim per al període de funcionament en proves? |
|
- Què es necessita per participar en aquest procediment de selecció? |
|
- Quin és el resultat de l'empadronament d'un/a menor en un domicili diferent al |
|
dels progenitors amb autorització? |
|
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: 0.3883495145631068 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6310679611650486 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7198335644937587 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8183079056865464 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3883495145631068 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.21035598705501618 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1439667128987517 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08183079056865464 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3883495145631068 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6310679611650486 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7198335644937587 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8183079056865464 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.596832375022475 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5265262091891769 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5337741877067146 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.37447988904299584 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6227461858529819 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.723994452149792 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8210818307905686 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.37447988904299584 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.207582061950994 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1447988904299584 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08210818307905685 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.37447988904299584 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6227461858529819 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.723994452149792 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8210818307905686 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5927947036265483 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5201010501287889 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5274048711370899 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.37309292649098474 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6213592233009708 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7184466019417476 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.826629680998613 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.37309292649098474 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2071197411003236 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1436893203883495 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08266296809986129 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.37309292649098474 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6213592233009708 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7184466019417476 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.826629680998613 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5933965794382484 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5193294146137418 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5262147141098168 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.39528432732316227 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6185852981969486 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6962552011095701 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8252427184466019 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.39528432732316227 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.20619509939898292 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.139251040221914 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0825242718446602 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.39528432732316227 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6185852981969486 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6962552011095701 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8252427184466019 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5982896106972676 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5270165995200669 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.533875073833905 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3828016643550624 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6033287101248266 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7059639389736477 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8155339805825242 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3828016643550624 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.20110957004160887 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.14119278779472955 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08155339805825243 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3828016643550624 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6033287101248266 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7059639389736477 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8155339805825242 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.589596475804869 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5181840697444022 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5258716600846131 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.37031900138696255 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5686546463245492 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6851595006934813 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7891816920943134 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.37031900138696255 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18955154877484973 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13703190013869623 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07891816920943133 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.37031900138696255 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5686546463245492 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6851595006934813 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7891816920943134 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5679462834016797 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.49845397706007927 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5067836651151116 |
|
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-SB-003-5ep") |
|
# Run inference |
|
sentences = [ |
|
"Empadronament d'un/a menor en un domicili diferent al domicili dels progenitors - Amb autorització de les persones progenitores", |
|
"Quin és el resultat de l'empadronament d'un/a menor en un domicili diferent al dels progenitors amb autorització?", |
|
'Quin és el límit de temps màxim per al període de funcionament en proves?', |
|
] |
|
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 | 0.3883 | |
|
| cosine_accuracy@3 | 0.6311 | |
|
| cosine_accuracy@5 | 0.7198 | |
|
| cosine_accuracy@10 | 0.8183 | |
|
| cosine_precision@1 | 0.3883 | |
|
| cosine_precision@3 | 0.2104 | |
|
| cosine_precision@5 | 0.144 | |
|
| cosine_precision@10 | 0.0818 | |
|
| cosine_recall@1 | 0.3883 | |
|
| cosine_recall@3 | 0.6311 | |
|
| cosine_recall@5 | 0.7198 | |
|
| cosine_recall@10 | 0.8183 | |
|
| cosine_ndcg@10 | 0.5968 | |
|
| cosine_mrr@10 | 0.5265 | |
|
| **cosine_map@100** | **0.5338** | |
|
|
|
#### 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 | 0.3745 | |
|
| cosine_accuracy@3 | 0.6227 | |
|
| cosine_accuracy@5 | 0.724 | |
|
| cosine_accuracy@10 | 0.8211 | |
|
| cosine_precision@1 | 0.3745 | |
|
| cosine_precision@3 | 0.2076 | |
|
| cosine_precision@5 | 0.1448 | |
|
| cosine_precision@10 | 0.0821 | |
|
| cosine_recall@1 | 0.3745 | |
|
| cosine_recall@3 | 0.6227 | |
|
| cosine_recall@5 | 0.724 | |
|
| cosine_recall@10 | 0.8211 | |
|
| cosine_ndcg@10 | 0.5928 | |
|
| cosine_mrr@10 | 0.5201 | |
|
| **cosine_map@100** | **0.5274** | |
|
|
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#### 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 | 0.3731 | |
|
| cosine_accuracy@3 | 0.6214 | |
|
| cosine_accuracy@5 | 0.7184 | |
|
| cosine_accuracy@10 | 0.8266 | |
|
| cosine_precision@1 | 0.3731 | |
|
| cosine_precision@3 | 0.2071 | |
|
| cosine_precision@5 | 0.1437 | |
|
| cosine_precision@10 | 0.0827 | |
|
| cosine_recall@1 | 0.3731 | |
|
| cosine_recall@3 | 0.6214 | |
|
| cosine_recall@5 | 0.7184 | |
|
| cosine_recall@10 | 0.8266 | |
|
| cosine_ndcg@10 | 0.5934 | |
|
| cosine_mrr@10 | 0.5193 | |
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| **cosine_map@100** | **0.5262** | |
|
|
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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 | 0.3953 | |
|
| cosine_accuracy@3 | 0.6186 | |
|
| cosine_accuracy@5 | 0.6963 | |
|
| cosine_accuracy@10 | 0.8252 | |
|
| cosine_precision@1 | 0.3953 | |
|
| cosine_precision@3 | 0.2062 | |
|
| cosine_precision@5 | 0.1393 | |
|
| cosine_precision@10 | 0.0825 | |
|
| cosine_recall@1 | 0.3953 | |
|
| cosine_recall@3 | 0.6186 | |
|
| cosine_recall@5 | 0.6963 | |
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| cosine_recall@10 | 0.8252 | |
|
| cosine_ndcg@10 | 0.5983 | |
|
| cosine_mrr@10 | 0.527 | |
|
| **cosine_map@100** | **0.5339** | |
|
|
|
#### 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 | 0.3828 | |
|
| cosine_accuracy@3 | 0.6033 | |
|
| cosine_accuracy@5 | 0.706 | |
|
| cosine_accuracy@10 | 0.8155 | |
|
| cosine_precision@1 | 0.3828 | |
|
| cosine_precision@3 | 0.2011 | |
|
| cosine_precision@5 | 0.1412 | |
|
| cosine_precision@10 | 0.0816 | |
|
| cosine_recall@1 | 0.3828 | |
|
| cosine_recall@3 | 0.6033 | |
|
| cosine_recall@5 | 0.706 | |
|
| cosine_recall@10 | 0.8155 | |
|
| cosine_ndcg@10 | 0.5896 | |
|
| cosine_mrr@10 | 0.5182 | |
|
| **cosine_map@100** | **0.5259** | |
|
|
|
#### 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 | 0.3703 | |
|
| cosine_accuracy@3 | 0.5687 | |
|
| cosine_accuracy@5 | 0.6852 | |
|
| cosine_accuracy@10 | 0.7892 | |
|
| cosine_precision@1 | 0.3703 | |
|
| cosine_precision@3 | 0.1896 | |
|
| cosine_precision@5 | 0.137 | |
|
| cosine_precision@10 | 0.0789 | |
|
| cosine_recall@1 | 0.3703 | |
|
| cosine_recall@3 | 0.5687 | |
|
| cosine_recall@5 | 0.6852 | |
|
| cosine_recall@10 | 0.7892 | |
|
| cosine_ndcg@10 | 0.5679 | |
|
| cosine_mrr@10 | 0.4985 | |
|
| **cosine_map@100** | **0.5068** | |
|
|
|
<!-- |
|
## 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: 2,884 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: 3 tokens</li><li>mean: 36.18 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.77 tokens</li><li>max: 60 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------| |
|
| <code>I assessorem per l'optimització dels contractes de subministraments energètics.</code> | <code>Quin és el resultat esperat del servei de millora dels contractes de serveis de llum i gas?</code> | |
|
| <code>Retorna en format JSON adequat</code> | <code>Quin és el format de sortida del qüestionari de projectes específics?</code> | |
|
| <code>Aula Mentor és un programa d'ajuda a l'alumne que té com a objectiu principal donar suport als estudiants en la seva formació i desenvolupament personal i professional.</code> | <code>Quin és el format del programa Aula Mentor?</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, |
|
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 |
|
#### Non-Default Hyperparameters |
|
|
|
- `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 |
|
- `warmup_ratio`: 0.2 |
|
- `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 |
|
- `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`: 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 |
|
- `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`: True |
|
- `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 |
|
- `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 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.8840 | 10 | 2.6418 | - | - | - | - | - | - | |
|
| 0.9724 | 11 | - | 0.4986 | 0.5108 | 0.5014 | 0.4934 | 0.4779 | 0.4351 | |
|
| 1.7680 | 20 | 1.1708 | - | - | - | - | - | - | |
|
| 1.9448 | 22 | - | 0.5197 | 0.5248 | 0.5195 | 0.5290 | 0.5052 | 0.4904 | |
|
| 2.6519 | 30 | 0.5531 | - | - | - | - | - | - | |
|
| 2.9171 | 33 | - | 0.5304 | 0.5274 | 0.5196 | 0.5279 | 0.5234 | 0.4947 | |
|
| 3.5359 | 40 | 0.2859 | - | - | - | - | - | - | |
|
| 3.9779 | 45 | - | 0.5256 | 0.5292 | 0.5206 | 0.5313 | 0.5174 | 0.5046 | |
|
| 4.4199 | 50 | 0.2144 | - | - | - | - | - | - | |
|
| **4.8619** | **55** | **-** | **0.5338** | **0.5274** | **0.5262** | **0.5339** | **0.5259** | **0.5068** | |
|
|
|
* 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*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.* |
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--> |
|
|
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<!-- |
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## Model Card Contact |
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|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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--> |
