|
--- |
|
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:6692 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: La inscripció en aquest registre caduca en el termini d'un any, |
|
llevat que sigui renovada abans del transcurs d'aquest termini mitjançant la presentació |
|
d'una declaració responsable sobre el compliment dels requisits exigits. |
|
sentences: |
|
- Quin és el requisit per a la sol·licitud del volant d'empadronament? |
|
- Què passa si no es renova la inscripció en el Registre municipal de sol·licitants? |
|
- Quin és el segon objectiu que han de tenir els projectes/activitats per a rebre |
|
aquesta subvenció? |
|
- source_sentence: 'AVÍS: Places exhaurides de l''activitat de psicomotricitat fins |
|
nou avís. Les persones interessades poden contactar amb el Departament d''Esports, |
|
el qual obrirà un llistat d''espera, si escau.' |
|
sentences: |
|
- Què passa si les places de Psicomotricitat estan exhaurides? |
|
- Quin és el paper del tractament en la declaració? |
|
- Quin és el període de temps que es requereix per a la venda d'articles d'artesania? |
|
- source_sentence: El registre de noves patents en relació a les noves línies d’actuació |
|
és una despesa subvencionable per a la reactivació i adaptació del negoci post |
|
COVID19. |
|
sentences: |
|
- Quins són els tipus de despeses que es poden finançar amb les subvencions? |
|
- Quin és el paper de les organitzacions membres del Consell de Cooperació en els |
|
projectes de cooperació internacional? |
|
- Quin és el propòsit del registre de noves patents en relació a les noves línies |
|
d’actuació? |
|
- source_sentence: 'Justificació de les subvencions atorgades per l''Ajuntament de |
|
Sitges per les activitats culturals incloses dins els següents tipus: Activitats |
|
de difusió cultural. Iniciatives de recuperació i difusió del patrimoni cultural, |
|
tradicional i popular. Activitats de formació no reglada i de recerca. Activitats |
|
d''animació socio-cultural.' |
|
sentences: |
|
- Quins són els residus que es recullen en el servei municipal complementari? |
|
- Quin és el paper de l'expedient d'ajut a la contractació laboral de persones en |
|
la contractació laboral? |
|
- Quin és el paper de les activitats d'animació socio-cultural? |
|
- source_sentence: La comunicació és un element important en la cura dels gats, ja |
|
que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats |
|
competents i amb els altres implicats en la cura dels animals. |
|
sentences: |
|
- Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments |
|
oberts al públic i les activitats recreatives? |
|
- Quin és el paper de la comunicació en la cura dels gats? |
|
- Quin és el benefici de la llicència de gual per a la persona titular? |
|
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: 0.1589958158995816 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.303347280334728 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3723849372384937 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5188284518828452 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1589958158995816 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.101115760111576 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07447698744769873 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05188284518828451 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1589958158995816 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.303347280334728 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3723849372384937 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5188284518828452 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.31740141154907076 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2560196254233912 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.27634436521904066 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.15690376569037656 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.29707112970711297 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3807531380753138 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5083682008368201 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.15690376569037656 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09902370990237098 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07615062761506276 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.050836820083682004 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.15690376569037656 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.29707112970711297 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3807531380753138 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5083682008368201 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3138709871801379 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.25412432755528996 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.27566053318396105 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.17364016736401675 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3138075313807531 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.39539748953974896 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5376569037656904 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.17364016736401675 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.10460251046025104 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07907949790794978 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05376569037656903 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.17364016736401675 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3138075313807531 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.39539748953974896 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5376569037656904 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.33244445391299926 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2700023245002324 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.29010151423672403 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1506276150627615 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2907949790794979 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.401673640167364 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5355648535564853 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1506276150627615 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09693165969316596 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0803347280334728 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05355648535564853 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1506276150627615 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2907949790794979 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.401673640167364 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5355648535564853 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3189819772344188 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.25269392973367877 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2728848917988661 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.16736401673640167 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.3200836820083682 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.41631799163179917 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5481171548117155 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.16736401673640167 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.10669456066945607 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08326359832635982 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05481171548117154 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.16736401673640167 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.3200836820083682 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.41631799163179917 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5481171548117155 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3353691502747181 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.26997077771136346 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2891803614784421 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.15481171548117154 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.28451882845188287 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3514644351464435 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5209205020920502 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.15481171548117154 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09483960948396093 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07029288702928871 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.052092050209205015 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.15481171548117154 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.28451882845188287 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3514644351464435 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5209205020920502 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3116868900381799 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2481885501759978 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2685744617473963 |
|
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-007-5ep") |
|
# Run inference |
|
sentences = [ |
|
'La comunicació és un element important en la cura dels gats, ja que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats competents i amb els altres implicats en la cura dels animals.', |
|
'Quin és el paper de la comunicació en la cura dels gats?', |
|
'Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments oberts al públic i les activitats recreatives?', |
|
] |
|
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 | 0.159 | |
|
| cosine_accuracy@3 | 0.3033 | |
|
| cosine_accuracy@5 | 0.3724 | |
|
| cosine_accuracy@10 | 0.5188 | |
|
| cosine_precision@1 | 0.159 | |
|
| cosine_precision@3 | 0.1011 | |
|
| cosine_precision@5 | 0.0745 | |
|
| cosine_precision@10 | 0.0519 | |
|
| cosine_recall@1 | 0.159 | |
|
| cosine_recall@3 | 0.3033 | |
|
| cosine_recall@5 | 0.3724 | |
|
| cosine_recall@10 | 0.5188 | |
|
| cosine_ndcg@10 | 0.3174 | |
|
| cosine_mrr@10 | 0.256 | |
|
| **cosine_map@100** | **0.2763** | |
|
|
|
#### 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 | 0.1569 | |
|
| cosine_accuracy@3 | 0.2971 | |
|
| cosine_accuracy@5 | 0.3808 | |
|
| cosine_accuracy@10 | 0.5084 | |
|
| cosine_precision@1 | 0.1569 | |
|
| cosine_precision@3 | 0.099 | |
|
| cosine_precision@5 | 0.0762 | |
|
| cosine_precision@10 | 0.0508 | |
|
| cosine_recall@1 | 0.1569 | |
|
| cosine_recall@3 | 0.2971 | |
|
| cosine_recall@5 | 0.3808 | |
|
| cosine_recall@10 | 0.5084 | |
|
| cosine_ndcg@10 | 0.3139 | |
|
| cosine_mrr@10 | 0.2541 | |
|
| **cosine_map@100** | **0.2757** | |
|
|
|
#### 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 | 0.1736 | |
|
| cosine_accuracy@3 | 0.3138 | |
|
| cosine_accuracy@5 | 0.3954 | |
|
| cosine_accuracy@10 | 0.5377 | |
|
| cosine_precision@1 | 0.1736 | |
|
| cosine_precision@3 | 0.1046 | |
|
| cosine_precision@5 | 0.0791 | |
|
| cosine_precision@10 | 0.0538 | |
|
| cosine_recall@1 | 0.1736 | |
|
| cosine_recall@3 | 0.3138 | |
|
| cosine_recall@5 | 0.3954 | |
|
| cosine_recall@10 | 0.5377 | |
|
| cosine_ndcg@10 | 0.3324 | |
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| cosine_mrr@10 | 0.27 | |
|
| **cosine_map@100** | **0.2901** | |
|
|
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#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1506 | |
|
| cosine_accuracy@3 | 0.2908 | |
|
| cosine_accuracy@5 | 0.4017 | |
|
| cosine_accuracy@10 | 0.5356 | |
|
| cosine_precision@1 | 0.1506 | |
|
| cosine_precision@3 | 0.0969 | |
|
| cosine_precision@5 | 0.0803 | |
|
| cosine_precision@10 | 0.0536 | |
|
| cosine_recall@1 | 0.1506 | |
|
| cosine_recall@3 | 0.2908 | |
|
| cosine_recall@5 | 0.4017 | |
|
| cosine_recall@10 | 0.5356 | |
|
| cosine_ndcg@10 | 0.319 | |
|
| cosine_mrr@10 | 0.2527 | |
|
| **cosine_map@100** | **0.2729** | |
|
|
|
#### 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 | 0.1674 | |
|
| cosine_accuracy@3 | 0.3201 | |
|
| cosine_accuracy@5 | 0.4163 | |
|
| cosine_accuracy@10 | 0.5481 | |
|
| cosine_precision@1 | 0.1674 | |
|
| cosine_precision@3 | 0.1067 | |
|
| cosine_precision@5 | 0.0833 | |
|
| cosine_precision@10 | 0.0548 | |
|
| cosine_recall@1 | 0.1674 | |
|
| cosine_recall@3 | 0.3201 | |
|
| cosine_recall@5 | 0.4163 | |
|
| cosine_recall@10 | 0.5481 | |
|
| cosine_ndcg@10 | 0.3354 | |
|
| cosine_mrr@10 | 0.27 | |
|
| **cosine_map@100** | **0.2892** | |
|
|
|
#### 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 | 0.1548 | |
|
| cosine_accuracy@3 | 0.2845 | |
|
| cosine_accuracy@5 | 0.3515 | |
|
| cosine_accuracy@10 | 0.5209 | |
|
| cosine_precision@1 | 0.1548 | |
|
| cosine_precision@3 | 0.0948 | |
|
| cosine_precision@5 | 0.0703 | |
|
| cosine_precision@10 | 0.0521 | |
|
| cosine_recall@1 | 0.1548 | |
|
| cosine_recall@3 | 0.2845 | |
|
| cosine_recall@5 | 0.3515 | |
|
| cosine_recall@10 | 0.5209 | |
|
| cosine_ndcg@10 | 0.3117 | |
|
| cosine_mrr@10 | 0.2482 | |
|
| **cosine_map@100** | **0.2686** | |
|
|
|
<!-- |
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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: 6,692 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: 6 tokens</li><li>mean: 44.83 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.89 tokens</li><li>max: 49 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------| |
|
| <code>Els residus comercials o industrials assimilables als municipals que hauran d'acreditar si disposen d'un gestor autoritzat per a la gestió dels residus.</code> | <code>Quins són els residus que es recullen en el servei municipal complementari?</code> | |
|
| <code>L'Ajuntament de Sitges ofereix ajuts econòmics a famílies amb recursos insuficients per accedir a la realització d'activitats de lleure...</code> | <code>Quin és el paper de l'Ajuntament de Sitges en la promoció de l'educació no formal i de lleure?</code> | |
|
| <code>Permet comunicar les intervencions necessàries per executar una instal·lació/remodelació d’autoconsum amb energia solar fotovoltaica amb una potència instal·lada inferior a 100 kWp en sòl urbà consolidat.</code> | <code>Quin és el propòsit de la remodelació d'una instal·lació d'autoconsum?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"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 |
|
- `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 |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
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- `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 |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `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 |
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- `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 |
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- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.3819 | 10 | 3.3449 | - | - | - | - | - | - | |
|
| 0.7637 | 20 | 2.0557 | - | - | - | - | - | - | |
|
| 0.9928 | 26 | - | 0.2440 | 0.2408 | 0.2590 | 0.2439 | 0.2379 | 0.2512 | |
|
| 1.1456 | 30 | 1.4634 | - | - | - | - | - | - | |
|
| 1.5274 | 40 | 0.8163 | - | - | - | - | - | - | |
|
| 1.9093 | 50 | 0.6103 | - | - | - | - | - | - | |
|
| 1.9857 | 52 | - | 0.2621 | 0.2683 | 0.2483 | 0.2629 | 0.2404 | 0.2472 | |
|
| 2.2912 | 60 | 0.4854 | - | - | - | - | - | - | |
|
| 2.6730 | 70 | 0.2796 | - | - | - | - | - | - | |
|
| 2.9785 | 78 | - | 0.2701 | 0.2697 | 0.2761 | 0.2845 | 0.2673 | 0.2709 | |
|
| 3.0549 | 80 | 0.2458 | - | - | - | - | - | - | |
|
| 3.4368 | 90 | 0.2616 | - | - | - | - | - | - | |
|
| 3.8186 | 100 | 0.174 | - | - | - | - | - | - | |
|
| 3.9714 | 104 | - | 0.2729 | 0.2863 | 0.2858 | 0.2853 | 0.2656 | 0.2752 | |
|
| 4.2005 | 110 | 0.1841 | - | - | - | - | - | - | |
|
| 4.5823 | 120 | 0.1668 | - | - | - | - | - | - | |
|
| **4.9642** | **130** | **0.1484** | **0.2763** | **0.2892** | **0.2729** | **0.2901** | **0.2686** | **0.2757** | |
|
|
|
* 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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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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