|
--- |
|
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:8769 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Aquelles persones que fan un ús regular i continuat de la deixalleria |
|
municipal poden gaudir d’una bonificació del 20% sobre la quota de les taxes per |
|
recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris. |
|
sentences: |
|
- Quin és el contingut dels documents dirigits a l'Ajuntament de Sitges? |
|
- Quin és el benefici de la deixalleria municipal? |
|
- Quin és el mètode de pagament dels ajuts atorgats en cas de normalitat? |
|
- source_sentence: Les subvencions per al desenvolupament i/o consolidació de sectors |
|
econòmics del municipi tenen com a objectiu generar un benefici ambiental per |
|
al municipi, a través de la promoció de pràctiques sostenibles. |
|
sentences: |
|
- Quin és el requisit per a la llicència per a la modificació d'un règim de propietat |
|
horitzontal? |
|
- Quin és el benefici ambiental esperat de les subvencions per al desenvolupament |
|
i/o consolidació de sectors econòmics del municipi? |
|
- Quin és el propòsit de la liquidació de l'import corresponent a l'exercici? |
|
- source_sentence: Aquelles persones que s'hagin inscrit a les estades esportives |
|
organitzades per l'Ajuntament de Sitges i que formin part d'una unitat familiar |
|
amb uns ingressos bruts mensuals, que una vegada dividits pel nombre de membres, |
|
siguin inferiors entre una i dues terceres parts de l'IPREM, poden sol·licitar |
|
una reducció de la quota d'aquestes activitats o l'aplicació de la corresponent |
|
tarifa bonificada establerta en les ordenances dels preus públics. |
|
sentences: |
|
- Quin és el benefici de les subvencions per a les entitats culturals? |
|
- Quin és el paper de l'IPREM en la sol·licitud de reducció de la quota d'una estada |
|
esportiva? |
|
- Quin és el paper de l'Ajuntament en la resolució d'una situació sanitària no adequada |
|
en un domini particular? |
|
- source_sentence: La inscripció al cens municipal facilita la recuperació d’aquests |
|
animals en cas de pèrdua alhora que permet a l’Ajuntament disposar de les dades |
|
necessàries en cas que s’hagin de realitzar campanyes sanitàries. |
|
sentences: |
|
- Quin és el tipus de serveis auxiliars que es consideren despeses elegibles? |
|
- Quin és el benefici d'estacionar a les zones verdes per als residents? |
|
- Quin és el motiu pel qual es crea el cens municipal d’animals de companyia? |
|
- source_sentence: A la nostra vila hi ha veïns i veïnes que els agradaria tornar |
|
a fer de pagès o provar-ho per primera vegada. |
|
sentences: |
|
- Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges? |
|
- Quin és el propòsit del carnet de conductor de taxi? |
|
- Quin és el paper de les persones en relació amb les indemnitzacions? |
|
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: 0.11054852320675106 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2270042194092827 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30548523206751055 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4531645569620253 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11054852320675106 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07566807313642755 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06109704641350212 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04531645569620253 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11054852320675106 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2270042194092827 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30548523206751055 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4531645569620253 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.25622764604771076 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1965350612818966 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21859411055862238 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11561181434599156 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2320675105485232 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.31139240506329113 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.44556962025316454 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11561181434599156 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07735583684950773 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06227848101265824 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.044556962025316456 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11561181434599156 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2320675105485232 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.31139240506329113 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.44556962025316454 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2579660315889156 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20086732301922164 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22344331787470567 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10379746835443038 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2210970464135021 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2970464135021097 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.43966244725738396 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10379746835443038 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07369901547116735 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05940928270042194 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.043966244725738395 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10379746835443038 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2210970464135021 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2970464135021097 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.43966244725738396 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2473619714740055 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.18892840399169497 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21182552044674802 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10042194092827005 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21518987341772153 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2978902953586498 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4438818565400844 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10042194092827005 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07172995780590716 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05957805907172995 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04438818565400844 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10042194092827005 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21518987341772153 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2978902953586498 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4438818565400844 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2479637375723138 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.18831156653941447 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21130848497160895 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10886075949367088 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22616033755274262 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3029535864978903 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4413502109704641 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10886075949367088 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07538677918424753 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.060590717299578066 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04413502109704641 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10886075949367088 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22616033755274262 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3029535864978903 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4413502109704641 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.25366131313332974 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19639441430580665 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2187767008895725 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.09367088607594937 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2742616033755274 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4177215189873418 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.09367088607594937 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06666666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05485232067510549 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04177215189873418 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.09367088607594937 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2742616033755274 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4177215189873418 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.23046340016141767 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1738279418659165 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.19782551958501599 |
|
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-003-5ep") |
|
# Run inference |
|
sentences = [ |
|
'A la nostra vila hi ha veïns i veïnes que els agradaria tornar a fer de pagès o provar-ho per primera vegada.', |
|
"Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?", |
|
'Quin és el paper de les persones en relació amb les indemnitzacions?', |
|
] |
|
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 | 0.1105 | |
|
| cosine_accuracy@3 | 0.227 | |
|
| cosine_accuracy@5 | 0.3055 | |
|
| cosine_accuracy@10 | 0.4532 | |
|
| cosine_precision@1 | 0.1105 | |
|
| cosine_precision@3 | 0.0757 | |
|
| cosine_precision@5 | 0.0611 | |
|
| cosine_precision@10 | 0.0453 | |
|
| cosine_recall@1 | 0.1105 | |
|
| cosine_recall@3 | 0.227 | |
|
| cosine_recall@5 | 0.3055 | |
|
| cosine_recall@10 | 0.4532 | |
|
| cosine_ndcg@10 | 0.2562 | |
|
| cosine_mrr@10 | 0.1965 | |
|
| **cosine_map@100** | **0.2186** | |
|
|
|
#### 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 | 0.1156 | |
|
| cosine_accuracy@3 | 0.2321 | |
|
| cosine_accuracy@5 | 0.3114 | |
|
| cosine_accuracy@10 | 0.4456 | |
|
| cosine_precision@1 | 0.1156 | |
|
| cosine_precision@3 | 0.0774 | |
|
| cosine_precision@5 | 0.0623 | |
|
| cosine_precision@10 | 0.0446 | |
|
| cosine_recall@1 | 0.1156 | |
|
| cosine_recall@3 | 0.2321 | |
|
| cosine_recall@5 | 0.3114 | |
|
| cosine_recall@10 | 0.4456 | |
|
| cosine_ndcg@10 | 0.258 | |
|
| cosine_mrr@10 | 0.2009 | |
|
| **cosine_map@100** | **0.2234** | |
|
|
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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 | 0.1038 | |
|
| cosine_accuracy@3 | 0.2211 | |
|
| cosine_accuracy@5 | 0.297 | |
|
| cosine_accuracy@10 | 0.4397 | |
|
| cosine_precision@1 | 0.1038 | |
|
| cosine_precision@3 | 0.0737 | |
|
| cosine_precision@5 | 0.0594 | |
|
| cosine_precision@10 | 0.044 | |
|
| cosine_recall@1 | 0.1038 | |
|
| cosine_recall@3 | 0.2211 | |
|
| cosine_recall@5 | 0.297 | |
|
| cosine_recall@10 | 0.4397 | |
|
| cosine_ndcg@10 | 0.2474 | |
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| cosine_mrr@10 | 0.1889 | |
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| **cosine_map@100** | **0.2118** | |
|
|
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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 | 0.1004 | |
|
| cosine_accuracy@3 | 0.2152 | |
|
| cosine_accuracy@5 | 0.2979 | |
|
| cosine_accuracy@10 | 0.4439 | |
|
| cosine_precision@1 | 0.1004 | |
|
| cosine_precision@3 | 0.0717 | |
|
| cosine_precision@5 | 0.0596 | |
|
| cosine_precision@10 | 0.0444 | |
|
| cosine_recall@1 | 0.1004 | |
|
| cosine_recall@3 | 0.2152 | |
|
| cosine_recall@5 | 0.2979 | |
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| cosine_recall@10 | 0.4439 | |
|
| cosine_ndcg@10 | 0.248 | |
|
| cosine_mrr@10 | 0.1883 | |
|
| **cosine_map@100** | **0.2113** | |
|
|
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#### 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 | 0.1089 | |
|
| cosine_accuracy@3 | 0.2262 | |
|
| cosine_accuracy@5 | 0.303 | |
|
| cosine_accuracy@10 | 0.4414 | |
|
| cosine_precision@1 | 0.1089 | |
|
| cosine_precision@3 | 0.0754 | |
|
| cosine_precision@5 | 0.0606 | |
|
| cosine_precision@10 | 0.0441 | |
|
| cosine_recall@1 | 0.1089 | |
|
| cosine_recall@3 | 0.2262 | |
|
| cosine_recall@5 | 0.303 | |
|
| cosine_recall@10 | 0.4414 | |
|
| cosine_ndcg@10 | 0.2537 | |
|
| cosine_mrr@10 | 0.1964 | |
|
| **cosine_map@100** | **0.2188** | |
|
|
|
#### 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 | 0.0937 | |
|
| cosine_accuracy@3 | 0.2 | |
|
| cosine_accuracy@5 | 0.2743 | |
|
| cosine_accuracy@10 | 0.4177 | |
|
| cosine_precision@1 | 0.0937 | |
|
| cosine_precision@3 | 0.0667 | |
|
| cosine_precision@5 | 0.0549 | |
|
| cosine_precision@10 | 0.0418 | |
|
| cosine_recall@1 | 0.0937 | |
|
| cosine_recall@3 | 0.2 | |
|
| cosine_recall@5 | 0.2743 | |
|
| cosine_recall@10 | 0.4177 | |
|
| cosine_ndcg@10 | 0.2305 | |
|
| cosine_mrr@10 | 0.1738 | |
|
| **cosine_map@100** | **0.1978** | |
|
|
|
<!-- |
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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: 8,769 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 49.22 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 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 |
|
|
|
- `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 |
|
- `num_train_epochs`: 5 |
|
- `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 |
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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 |
|
- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
|
- `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 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
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- `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 |
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- `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 |
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- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
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- `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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.2914 | 10 | 3.6318 | - | - | - | - | - | - | |
|
| 0.5829 | 20 | 2.329 | - | - | - | - | - | - | |
|
| 0.8743 | 30 | 1.5614 | - | - | - | - | - | - | |
|
| 0.9909 | 34 | - | 0.2055 | 0.1998 | 0.2020 | 0.2001 | 0.1903 | 0.2019 | |
|
| 1.1658 | 40 | 1.2383 | - | - | - | - | - | - | |
|
| 1.4572 | 50 | 0.9323 | - | - | - | - | - | - | |
|
| 1.7486 | 60 | 0.6616 | - | - | - | - | - | - | |
|
| 1.9818 | 68 | - | 0.2244 | 0.2063 | 0.2223 | 0.2166 | 0.2011 | 0.2235 | |
|
| 2.0401 | 70 | 0.5545 | - | - | - | - | - | - | |
|
| 2.3315 | 80 | 0.5043 | - | - | - | - | - | - | |
|
| 2.6230 | 90 | 0.3542 | - | - | - | - | - | - | |
|
| 2.9144 | 100 | 0.3095 | - | - | - | - | - | - | |
|
| 2.9727 | 102 | - | 0.2224 | 0.2046 | 0.2170 | 0.2100 | 0.1986 | 0.2144 | |
|
| 3.2058 | 110 | 0.2863 | - | - | - | - | - | - | |
|
| 3.4973 | 120 | 0.2329 | - | - | - | - | - | - | |
|
| 3.7887 | 130 | 0.2353 | - | - | - | - | - | - | |
|
| 3.9927 | 137 | - | 0.2197 | 0.2112 | 0.2098 | 0.2154 | 0.1949 | 0.2178 | |
|
| 4.0801 | 140 | 0.1759 | - | - | - | - | - | - | |
|
| 4.3716 | 150 | 0.2308 | - | - | - | - | - | - | |
|
| 4.6630 | 160 | 0.1656 | - | - | - | - | - | - | |
|
| **4.9545** | **170** | **0.1812** | **0.2186** | **0.2188** | **0.2113** | **0.2118** | **0.1978** | **0.2234** | |
|
|
|
* 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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## Model Card Authors |
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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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