|
--- |
|
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:2844 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: La unió d’aquests dos documents conforma l’Informe d’Avaluació |
|
de l’Edifici (IAE). |
|
sentences: |
|
- Quin és el requisit per a rebre els ajuts econòmics per a les empreses? |
|
- Quin és el resultat de la unió de la Inspecció Tècnica de l’Edifici (ITE) i dels |
|
certificats energètics? |
|
- Quin és el termini per sol·licitar la renovació del carnet de persona cuidadora? |
|
- source_sentence: La Inspecció Tècnica dels Edificis (ITE) permet identificar les |
|
oportunitats de millora de l'eficiència energètica i implementar mesures de rehabilitació. |
|
sentences: |
|
- Quin és el benefici de l'activitat del Viver dels Avis de Sitges per a la qualitat |
|
de vida? |
|
- Com puc saber si puc ser cuidador? |
|
- Quin és el paper de la Inspecció Tècnica dels Edificis (ITE) en la millora de |
|
l'eficiència energètica? |
|
- source_sentence: A les zones blaves els parquímetres i serveis de pagament reconeixen |
|
les matricules dels vehicles acreditats. |
|
sentences: |
|
- Quin és el paper de la mediació en una denúncia? |
|
- Quin és el paper de les persones físiques? |
|
- Quin és el procediment per estacionar a les zones blaves amb l'acreditació de |
|
resident? |
|
- source_sentence: Els establiments oberts al públic destinats a espectacles cinematogràfics. |
|
Els establiments oberts al públic destinats a espectacles públics i activitats |
|
recreatives musicals amb un aforament autoritzat fins a 150 persones. |
|
sentences: |
|
- Quin és el resultat esperat després de la intervenció de l'Ajuntament en les denúncies |
|
sanitàries? |
|
- Quin és el requisit de superfície construïda per als restaurants musicals? |
|
- Quins establiments oberts al públic han de comunicar la seva obertura a l'Ajuntament? |
|
- source_sentence: El Decret 97/2002, de 5 de març, regula la concessió de la targeta |
|
d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar |
|
el desplaçament de les persones amb mobilitat reduïda. |
|
sentences: |
|
- Quin és el benefici de la targeta d'aparcament per a les persones amb disminució? |
|
- Quin és el paper de la Junta de Govern Local? |
|
- Quin és l'organisme que emet el certificat de serveis prestats? |
|
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: 0.11814345991561181 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.23277074542897327 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3129395218002813 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4644163150492264 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11814345991561181 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07759024847632442 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06258790436005626 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.046441631504922636 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11814345991561181 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23277074542897327 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3129395218002813 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4644163150492264 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.26553370933458276 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20527392672962277 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22599508422976106 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11575246132208157 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2289732770745429 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3112517580872011 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.46568213783403656 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11575246132208157 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07632442569151429 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.062250351617440226 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04656821378340366 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11575246132208157 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2289732770745429 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3112517580872011 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.46568213783403656 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.26414039995115557 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20311873507021158 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22355973027797246 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11912798874824192 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.23277074542897327 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.31758087201125174 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.46582278481012657 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11912798874824192 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07759024847632444 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06351617440225035 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04658227848101265 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11912798874824192 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23277074542897327 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.31758087201125174 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.46582278481012657 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.26671990925029193 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20635646194717913 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22673055490318922 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11533052039381153 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22658227848101264 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30857946554149085 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.45668073136427567 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11533052039381153 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07552742616033756 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06171589310829817 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04566807313642757 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11533052039381153 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22658227848101264 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30857946554149085 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.45668073136427567 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.26044811042246035 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20098218471636187 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22169039893772347 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11181434599156118 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22334739803094233 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30253164556962026 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.45288326300984527 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11181434599156118 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07444913267698076 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06050632911392405 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.045288326300984526 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11181434599156118 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22334739803094233 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30253164556962026 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.45288326300984527 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2566428043422134 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19724806331346384 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21784479785600805 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10689170182841069 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21251758087201125 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.28846694796061884 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.42967651195499296 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10689170182841069 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07083919362400375 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05769338959212378 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0429676511954993 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10689170182841069 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21251758087201125 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.28846694796061884 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.42967651195499296 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2438421466584992 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1875642957604982 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2080904354707231 |
|
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-006-5ep") |
|
# Run inference |
|
sentences = [ |
|
'El Decret 97/2002, de 5 de març, regula la concessió de la targeta d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar el desplaçament de les persones amb mobilitat reduïda.', |
|
"Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?", |
|
'Quin és el paper de la Junta de Govern Local?', |
|
] |
|
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 | 0.1181 | |
|
| cosine_accuracy@3 | 0.2328 | |
|
| cosine_accuracy@5 | 0.3129 | |
|
| cosine_accuracy@10 | 0.4644 | |
|
| cosine_precision@1 | 0.1181 | |
|
| cosine_precision@3 | 0.0776 | |
|
| cosine_precision@5 | 0.0626 | |
|
| cosine_precision@10 | 0.0464 | |
|
| cosine_recall@1 | 0.1181 | |
|
| cosine_recall@3 | 0.2328 | |
|
| cosine_recall@5 | 0.3129 | |
|
| cosine_recall@10 | 0.4644 | |
|
| cosine_ndcg@10 | 0.2655 | |
|
| cosine_mrr@10 | 0.2053 | |
|
| **cosine_map@100** | **0.226** | |
|
|
|
#### 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 | 0.1158 | |
|
| cosine_accuracy@3 | 0.229 | |
|
| cosine_accuracy@5 | 0.3113 | |
|
| cosine_accuracy@10 | 0.4657 | |
|
| cosine_precision@1 | 0.1158 | |
|
| cosine_precision@3 | 0.0763 | |
|
| cosine_precision@5 | 0.0623 | |
|
| cosine_precision@10 | 0.0466 | |
|
| cosine_recall@1 | 0.1158 | |
|
| cosine_recall@3 | 0.229 | |
|
| cosine_recall@5 | 0.3113 | |
|
| cosine_recall@10 | 0.4657 | |
|
| cosine_ndcg@10 | 0.2641 | |
|
| cosine_mrr@10 | 0.2031 | |
|
| **cosine_map@100** | **0.2236** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1191 | |
|
| cosine_accuracy@3 | 0.2328 | |
|
| cosine_accuracy@5 | 0.3176 | |
|
| cosine_accuracy@10 | 0.4658 | |
|
| cosine_precision@1 | 0.1191 | |
|
| cosine_precision@3 | 0.0776 | |
|
| cosine_precision@5 | 0.0635 | |
|
| cosine_precision@10 | 0.0466 | |
|
| cosine_recall@1 | 0.1191 | |
|
| cosine_recall@3 | 0.2328 | |
|
| cosine_recall@5 | 0.3176 | |
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| cosine_recall@10 | 0.4658 | |
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| cosine_ndcg@10 | 0.2667 | |
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| cosine_mrr@10 | 0.2064 | |
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| **cosine_map@100** | **0.2267** | |
|
|
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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 | 0.1153 | |
|
| cosine_accuracy@3 | 0.2266 | |
|
| cosine_accuracy@5 | 0.3086 | |
|
| cosine_accuracy@10 | 0.4567 | |
|
| cosine_precision@1 | 0.1153 | |
|
| cosine_precision@3 | 0.0755 | |
|
| cosine_precision@5 | 0.0617 | |
|
| cosine_precision@10 | 0.0457 | |
|
| cosine_recall@1 | 0.1153 | |
|
| cosine_recall@3 | 0.2266 | |
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| cosine_recall@5 | 0.3086 | |
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| cosine_recall@10 | 0.4567 | |
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| cosine_ndcg@10 | 0.2604 | |
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| cosine_mrr@10 | 0.201 | |
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| **cosine_map@100** | **0.2217** | |
|
|
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#### Information Retrieval |
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* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1118 | |
|
| cosine_accuracy@3 | 0.2233 | |
|
| cosine_accuracy@5 | 0.3025 | |
|
| cosine_accuracy@10 | 0.4529 | |
|
| cosine_precision@1 | 0.1118 | |
|
| cosine_precision@3 | 0.0744 | |
|
| cosine_precision@5 | 0.0605 | |
|
| cosine_precision@10 | 0.0453 | |
|
| cosine_recall@1 | 0.1118 | |
|
| cosine_recall@3 | 0.2233 | |
|
| cosine_recall@5 | 0.3025 | |
|
| cosine_recall@10 | 0.4529 | |
|
| cosine_ndcg@10 | 0.2566 | |
|
| cosine_mrr@10 | 0.1972 | |
|
| **cosine_map@100** | **0.2178** | |
|
|
|
#### 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 | 0.1069 | |
|
| cosine_accuracy@3 | 0.2125 | |
|
| cosine_accuracy@5 | 0.2885 | |
|
| cosine_accuracy@10 | 0.4297 | |
|
| cosine_precision@1 | 0.1069 | |
|
| cosine_precision@3 | 0.0708 | |
|
| cosine_precision@5 | 0.0577 | |
|
| cosine_precision@10 | 0.043 | |
|
| cosine_recall@1 | 0.1069 | |
|
| cosine_recall@3 | 0.2125 | |
|
| cosine_recall@5 | 0.2885 | |
|
| cosine_recall@10 | 0.4297 | |
|
| cosine_ndcg@10 | 0.2438 | |
|
| cosine_mrr@10 | 0.1876 | |
|
| **cosine_map@100** | **0.2081** | |
|
|
|
<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
|
### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 2,844 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: 49.45 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 tokens</li><li>max: 45 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>Per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural, les entitats o associacions culturals de Sitges han de tenir una seu social a la ciutat de Sitges i estar inscrites en el Registre d'Entitats de la Generalitat de Catalunya.</code> | <code>Quin és el requisit per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural?</code> | |
|
| <code>La cessió entre tercers, només es contempla en el cas de sepultures de construcció particular que hagin estat donades d'alta amb una anterioritat de 10 anys a la data de sol·licitud de la cessió.</code> | <code>Quin és el paper de la persona que, legalment hi tingui dret, en la cessió entre tercers?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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1024, |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
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- `bf16`: True |
|
- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `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`: 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 |
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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 |
|
- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
|
- `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 |
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- `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 |
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- `skip_memory_metrics`: True |
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- `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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.8989 | 10 | 3.2114 | - | - | - | - | - | - | |
|
| 0.9888 | 11 | - | 0.2144 | 0.2008 | 0.2070 | 0.2126 | 0.1842 | 0.2126 | |
|
| 1.7978 | 20 | 1.5622 | - | - | - | - | - | - | |
|
| 1.9775 | 22 | - | 0.2179 | 0.2101 | 0.2169 | 0.2180 | 0.2012 | 0.2193 | |
|
| 2.6966 | 30 | 0.7882 | - | - | - | - | - | - | |
|
| 2.9663 | 33 | - | 0.2239 | 0.2162 | 0.2220 | 0.2238 | 0.2070 | 0.2222 | |
|
| 3.5955 | 40 | 0.4956 | - | - | - | - | - | - | |
|
| 3.9551 | 44 | - | 0.2270 | 0.2177 | 0.2231 | 0.2278 | 0.2084 | 0.2255 | |
|
| 4.4944 | 50 | 0.392 | - | - | - | - | - | - | |
|
| **4.9438** | **55** | **-** | **0.226** | **0.2178** | **0.2217** | **0.2267** | **0.2081** | **0.2236** | |
|
|
|
* 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} |
|
} |
|
``` |
|
|
|
<!-- |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
|
--> |
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<!-- |
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## Model Card Authors |
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|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
