|
--- |
|
base_model: BAAI/bge-m3 |
|
datasets: [] |
|
language: [] |
|
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:9593 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Aquestes parades estaran ocupades per empreses del sector, entitats |
|
socials i culturals i centres escolars amb seu a Sitges, o empreses del sector |
|
amb activitat a Sitges, que prèviament han fet la sol·licitud, se'ls ha autoritzat |
|
i, si escau, han abonat la taxa corresponent. |
|
sentences: |
|
- Quin és el paper de les petites empreses i persones autònomes en aquests ajuts? |
|
- Quin és el tràmit que permet sol·licitar una nova placa de gual? |
|
- Quin és el requisit per a l'ocupació de les parades de la Fira de Sant Jordi? |
|
- source_sentence: L'Ajuntament de Sitges atorga subvencions pels projectes educatius |
|
que realitzen les escoles de Sitges que tinguin com a finalitat augmentar la qualitat |
|
educativa dels infants d'infantil i primària al llarg de l’exercici pel qual es |
|
sol·licita la subvenció. |
|
sentences: |
|
- Quin és el paper de la targeta 'smart Sitges' en la gestió de residus? |
|
- Quin és el requisit per rebre ajuts econòmics per la meva empresa en dificultats |
|
econòmiques? |
|
- Quin és el resultat esperat de les subvencions per a les escoles? |
|
- source_sentence: ocupades per empreses del sector i entitats culturals, amb activitat |
|
editorial acreditada |
|
sentences: |
|
- Quin és el percentatge de bonificació per als carrers i locals afectats indirectament? |
|
- Quin és el propòsit de presentar documents en un procés de selecció de personal |
|
de l'Ajuntament de Sitges? |
|
- Quin és el lloc on es troben les empreses del sector que participen en la Fira |
|
de la Vila del Llibre de Sitges? |
|
- source_sentence: Aquest tràmit permet a les persones interessades la presentació |
|
d'al·legacions i/o la interposició de recursos contra actes administratius dictats |
|
per l'Ajuntament de Sitges. |
|
sentences: |
|
- Quin és el tràmit per presentar una al·legació contra una decisió de l'Ajuntament |
|
de Sitges? |
|
- Quin és el benefici de la llicència per a obres a la via pública |
|
- Com puc promoure l'esport a la ciutat? |
|
- source_sentence: 'Per valorar l’interès de la proposta es tindrà en compte: Tipus |
|
d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme |
|
des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana.' |
|
sentences: |
|
- Quin és el benefici de la realització d'exposicions al Centre Cultural Miramar? |
|
- Quin és el paper de les accions de promoció en les subvencions per a projectes |
|
i activitats de l'àmbit turístic? |
|
- Quins són els productes que es venen al Mercat setmanal dels dijous? |
|
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: 0.05909943714821764 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1275797373358349 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17354596622889307 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.2861163227016886 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04252657911194496 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03470919324577861 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.028611632270168854 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1275797373358349 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17354596622889307 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.2861163227016886 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1537318058278305 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.11394435510289168 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1397865116884934 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12570356472795496 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1801125703564728 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.2945590994371482 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04190118824265165 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.036022514071294566 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.02945590994371482 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12570356472795496 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1801125703564728 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.2945590994371482 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15635010592942117 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1149472140325799 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.14049204491324296 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12570356472795496 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17073170731707318 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.29831144465290804 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04190118824265165 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03414634146341463 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.029831144465290803 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05909943714821764 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12570356472795496 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17073170731707318 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.29831144465290804 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.1571277123670345 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1149557759313857 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1397328880376811 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.051594746716697934 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.12101313320825516 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.16791744840525327 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.28893058161350843 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.051594746716697934 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.040337711069418386 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03358348968105066 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.028893058161350845 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.051594746716697934 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.12101313320825516 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.16791744840525327 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.28893058161350843 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.14978486884903933 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1081955984395009 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13375931969408872 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.051594746716697934 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11726078799249531 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17166979362101314 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.28893058161350843 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.051594746716697934 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.039086929330831764 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.034333958724202626 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.028893058161350845 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.051594746716697934 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11726078799249531 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17166979362101314 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.28893058161350843 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.14877654954358344 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1068536138658091 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.13283061923015374 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05065666041275797 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1125703564727955 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.16416510318949343 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.28236397748592873 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05065666041275797 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0375234521575985 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03283302063789869 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.02823639774859287 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05065666041275797 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1125703564727955 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.16416510318949343 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.28236397748592873 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.14493487779487546 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10395931981297837 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1306497575595095 |
|
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). 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:** Unknown --> |
|
<!-- - **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/sitgrsBAAIbge-m3-300824") |
|
# Run inference |
|
sentences = [ |
|
'Per valorar l’interès de la proposta es tindrà en compte: Tipus d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana.', |
|
"Quin és el paper de les accions de promoció en les subvencions per a projectes i activitats de l'àmbit turístic?", |
|
"Quin és el benefici de la realització d'exposicions al Centre Cultural Miramar?", |
|
] |
|
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 | 0.0591 | |
|
| cosine_accuracy@3 | 0.1276 | |
|
| cosine_accuracy@5 | 0.1735 | |
|
| cosine_accuracy@10 | 0.2861 | |
|
| cosine_precision@1 | 0.0591 | |
|
| cosine_precision@3 | 0.0425 | |
|
| cosine_precision@5 | 0.0347 | |
|
| cosine_precision@10 | 0.0286 | |
|
| cosine_recall@1 | 0.0591 | |
|
| cosine_recall@3 | 0.1276 | |
|
| cosine_recall@5 | 0.1735 | |
|
| cosine_recall@10 | 0.2861 | |
|
| cosine_ndcg@10 | 0.1537 | |
|
| cosine_mrr@10 | 0.1139 | |
|
| **cosine_map@100** | **0.1398** | |
|
|
|
#### 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 | 0.0591 | |
|
| cosine_accuracy@3 | 0.1257 | |
|
| cosine_accuracy@5 | 0.1801 | |
|
| cosine_accuracy@10 | 0.2946 | |
|
| cosine_precision@1 | 0.0591 | |
|
| cosine_precision@3 | 0.0419 | |
|
| cosine_precision@5 | 0.036 | |
|
| cosine_precision@10 | 0.0295 | |
|
| cosine_recall@1 | 0.0591 | |
|
| cosine_recall@3 | 0.1257 | |
|
| cosine_recall@5 | 0.1801 | |
|
| cosine_recall@10 | 0.2946 | |
|
| cosine_ndcg@10 | 0.1564 | |
|
| cosine_mrr@10 | 0.1149 | |
|
| **cosine_map@100** | **0.1405** | |
|
|
|
#### 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 | 0.0591 | |
|
| cosine_accuracy@3 | 0.1257 | |
|
| cosine_accuracy@5 | 0.1707 | |
|
| cosine_accuracy@10 | 0.2983 | |
|
| cosine_precision@1 | 0.0591 | |
|
| cosine_precision@3 | 0.0419 | |
|
| cosine_precision@5 | 0.0341 | |
|
| cosine_precision@10 | 0.0298 | |
|
| cosine_recall@1 | 0.0591 | |
|
| cosine_recall@3 | 0.1257 | |
|
| cosine_recall@5 | 0.1707 | |
|
| cosine_recall@10 | 0.2983 | |
|
| cosine_ndcg@10 | 0.1571 | |
|
| cosine_mrr@10 | 0.115 | |
|
| **cosine_map@100** | **0.1397** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.0516 | |
|
| cosine_accuracy@3 | 0.121 | |
|
| cosine_accuracy@5 | 0.1679 | |
|
| cosine_accuracy@10 | 0.2889 | |
|
| cosine_precision@1 | 0.0516 | |
|
| cosine_precision@3 | 0.0403 | |
|
| cosine_precision@5 | 0.0336 | |
|
| cosine_precision@10 | 0.0289 | |
|
| cosine_recall@1 | 0.0516 | |
|
| cosine_recall@3 | 0.121 | |
|
| cosine_recall@5 | 0.1679 | |
|
| cosine_recall@10 | 0.2889 | |
|
| cosine_ndcg@10 | 0.1498 | |
|
| cosine_mrr@10 | 0.1082 | |
|
| **cosine_map@100** | **0.1338** | |
|
|
|
#### 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 | 0.0516 | |
|
| cosine_accuracy@3 | 0.1173 | |
|
| cosine_accuracy@5 | 0.1717 | |
|
| cosine_accuracy@10 | 0.2889 | |
|
| cosine_precision@1 | 0.0516 | |
|
| cosine_precision@3 | 0.0391 | |
|
| cosine_precision@5 | 0.0343 | |
|
| cosine_precision@10 | 0.0289 | |
|
| cosine_recall@1 | 0.0516 | |
|
| cosine_recall@3 | 0.1173 | |
|
| cosine_recall@5 | 0.1717 | |
|
| cosine_recall@10 | 0.2889 | |
|
| cosine_ndcg@10 | 0.1488 | |
|
| cosine_mrr@10 | 0.1069 | |
|
| **cosine_map@100** | **0.1328** | |
|
|
|
#### 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 | 0.0507 | |
|
| cosine_accuracy@3 | 0.1126 | |
|
| cosine_accuracy@5 | 0.1642 | |
|
| cosine_accuracy@10 | 0.2824 | |
|
| cosine_precision@1 | 0.0507 | |
|
| cosine_precision@3 | 0.0375 | |
|
| cosine_precision@5 | 0.0328 | |
|
| cosine_precision@10 | 0.0282 | |
|
| cosine_recall@1 | 0.0507 | |
|
| cosine_recall@3 | 0.1126 | |
|
| cosine_recall@5 | 0.1642 | |
|
| cosine_recall@10 | 0.2824 | |
|
| cosine_ndcg@10 | 0.1449 | |
|
| cosine_mrr@10 | 0.104 | |
|
| **cosine_map@100** | **0.1306** | |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 9,593 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: 5 tokens</li><li>mean: 49.28 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.16 tokens</li><li>max: 41 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament l’inici o modificació substancial d’una activitat econòmica, i hi adjunta el certificat tècnic acreditatiu del compliment dels requisits necessaris que estableix la normativa vigent per a l‘exercici de l’activitat.</code> | <code>Quin és el resultat esperat després de presentar el certificat tècnic en el tràmit de comunicació d'inici d'activitat?</code> | |
|
| <code>L'Ajuntament de Sitges ofereix a aquelles famílies que acompleixin els requisits establerts, ajuts per al pagament de la quota del servei i de la quota del menjador dels infants matriculats a les Llars d'Infants Municipals ( 0-3 anys).</code> | <code>Quins són els requisits per a beneficiar-se dels ajuts de l'Ajuntament de Sitges?</code> | |
|
| <code>Les entitats o associacions culturals han de presentar la sol·licitud de subvenció dins del termini establert per l'Ajuntament de Sitges.</code> | <code>Quin és el termini per a presentar una sol·licitud de subvenció per a un projecte cultural?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
1024, |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 5 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.2667 | 10 | 3.5318 | - | - | - | - | - | - | |
|
| 0.5333 | 20 | 2.3744 | - | - | - | - | - | - | |
|
| 0.8 | 30 | 1.6587 | - | - | - | - | - | - | |
|
| 0.9867 | 37 | - | 0.1350 | 0.1317 | 0.1349 | 0.1341 | 0.1207 | 0.1322 | |
|
| 1.0667 | 40 | 1.1513 | - | - | - | - | - | - | |
|
| 1.3333 | 50 | 1.0055 | - | - | - | - | - | - | |
|
| 1.6 | 60 | 0.7369 | - | - | - | - | - | - | |
|
| 1.8667 | 70 | 0.4855 | - | - | - | - | - | - | |
|
| 2.0 | 75 | - | 0.1366 | 0.1370 | 0.1376 | 0.1345 | 0.1290 | 0.1355 | |
|
| 2.1333 | 80 | 0.4362 | - | - | - | - | - | - | |
|
| 2.4 | 90 | 0.3943 | - | - | - | - | - | - | |
|
| 2.6667 | 100 | 0.3495 | - | - | - | - | - | - | |
|
| 2.9333 | 110 | 0.2138 | - | - | - | - | - | - | |
|
| **2.9867** | **112** | **-** | **0.1364** | **0.1342** | **0.1374** | **0.1361** | **0.1256** | **0.1367** | |
|
| 3.2 | 120 | 0.2176 | - | - | - | - | - | - | |
|
| 3.4667 | 130 | 0.2513 | - | - | - | - | - | - | |
|
| 3.7333 | 140 | 0.2163 | - | - | - | - | - | - | |
|
| 4.0 | 150 | 0.15 | 0.1401 | 0.1308 | 0.1332 | 0.1396 | 0.1279 | 0.1396 | |
|
| 4.2667 | 160 | 0.1613 | - | - | - | - | - | - | |
|
| 4.5333 | 170 | 0.1955 | - | - | - | - | - | - | |
|
| 4.8 | 180 | 0.1514 | - | - | - | - | - | - | |
|
| 4.9333 | 185 | - | 0.1398 | 0.1328 | 0.1338 | 0.1397 | 0.1306 | 0.1405 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.34.0.dev0 |
|
- Datasets: 2.21.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
