|
--- |
|
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:5175 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Caldrà executar l'obra comunicada prèviament d'acord amb les condicions |
|
específiques que es contenen en el model normalitzat CT02. |
|
sentences: |
|
- Quin és el propòsit de la instal·lació d'un circ sense animals a la via pública? |
|
- Quin és el destinatari de les dades bloquejades? |
|
- Quin és el format de presentació de la comunicació prèvia? |
|
- source_sentence: Armes utilitzables en activitats lúdico-esportives d’airsoft i |
|
paintball... |
|
sentences: |
|
- Quin és el paper de l'AFA en la venda de llibres? |
|
- Quin és el benefici de tenir dades personals correctes? |
|
- Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria? |
|
- source_sentence: En les activitats sotmeses al règim d’autorització ambiental o |
|
llicència municipal d’activitat (Annex I o Annex II de la Llei 20/2009) cal demanar |
|
aquest certificat previ a la presentació de la sol·licitud d’autorització ambiental |
|
o llicència municipal. |
|
sentences: |
|
- Quin és el benefici de tenir el certificat de compatibilitat urbanística en les |
|
activitats sotmeses a llicència municipal d’activitat? |
|
- Com puc controlar la recepció de propaganda electoral per correu? |
|
- Quin és el benefici de la cessió d'un compostador domèstic per a l'entorn? |
|
- source_sentence: La persona interessada posa en coneixement de l’Administració, |
|
les actuacions urbanístiques que pretén dur a terme consistents en l'apuntalament |
|
o reforç provisional d'estructures existents fins a la intervenció definitiva. |
|
sentences: |
|
- Qui pot participar en el Consell d'Adolescents? |
|
- Quin és el resultat de la presentació de la comunicació prèvia? |
|
- Quin és el paper de la persona interessada en relació amb la presentació de la |
|
comunicació prèvia? |
|
- source_sentence: La persona consumidora presenti la reclamació davant de l'entitat |
|
acreditada en un termini superior a un any des de la data en què va presentar |
|
la reclamació a l'empresa. |
|
sentences: |
|
- Quin és el tràmit per inscriure'm al Padró d'Habitants sense tenir constància |
|
de la meva anterior residència? |
|
- Quin és el resultat de la modificació substancial de la llicència d'obres en relació |
|
a les autoritzacions administratives? |
|
- Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació? |
|
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: 0.057391304347826085 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1791304347826087 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2539130434782609 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.42434782608695654 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.057391304347826085 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05971014492753622 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05078260869565218 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.042434782608695654 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.057391304347826085 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1791304347826087 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2539130434782609 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.42434782608695654 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.21132731792814036 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1471621808143548 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.16876601661954835 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.059130434782608696 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.16695652173913045 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2417391304347826 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.41739130434782606 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.059130434782608696 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05565217391304348 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04834782608695652 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04173913043478261 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.059130434782608696 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.16695652173913045 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2417391304347826 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.41739130434782606 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2073596053307957 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.14417184265010352 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.16633232312496227 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.06434782608695652 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1617391304347826 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2417391304347826 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4052173913043478 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.06434782608695652 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05391304347826086 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04834782608695652 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04052173913043479 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.06434782608695652 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1617391304347826 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2417391304347826 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4052173913043478 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.20633605278226078 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1464064872325742 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.16999201443118514 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05565217391304348 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.1565217391304348 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.22782608695652173 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.391304347826087 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05565217391304348 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05217391304347826 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.045565217391304355 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0391304347826087 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05565217391304348 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.1565217391304348 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.22782608695652173 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.391304347826087 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.19646870519287135 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.13765838509316777 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.16205285151749863 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.06782608695652174 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.17043478260869566 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.2573913043478261 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.41739130434782606 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.06782608695652174 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.05681159420289853 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.05147826086956522 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04173913043478261 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.06782608695652174 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.17043478260869566 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.2573913043478261 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.41739130434782606 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2141738525949419 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.15279848171152532 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.17543729180964374 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.05217391304347826 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.14608695652173914 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.23304347826086957 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.40347826086956523 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.05217391304347826 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.04869565217391304 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.04660869565217392 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04034782608695652 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.05217391304347826 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.14608695652173914 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.23304347826086957 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.40347826086956523 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.19611597970227643 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.133929606625259 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.15637789403585464 |
|
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/sqv-v4") |
|
# Run inference |
|
sentences = [ |
|
"La persona consumidora presenti la reclamació davant de l'entitat acreditada en un termini superior a un any des de la data en què va presentar la reclamació a l'empresa.", |
|
"Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?", |
|
"Quin és el resultat de la modificació substancial de la llicència d'obres en relació a les autoritzacions administratives?", |
|
] |
|
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 | 0.0574 | |
|
| cosine_accuracy@3 | 0.1791 | |
|
| cosine_accuracy@5 | 0.2539 | |
|
| cosine_accuracy@10 | 0.4243 | |
|
| cosine_precision@1 | 0.0574 | |
|
| cosine_precision@3 | 0.0597 | |
|
| cosine_precision@5 | 0.0508 | |
|
| cosine_precision@10 | 0.0424 | |
|
| cosine_recall@1 | 0.0574 | |
|
| cosine_recall@3 | 0.1791 | |
|
| cosine_recall@5 | 0.2539 | |
|
| cosine_recall@10 | 0.4243 | |
|
| cosine_ndcg@10 | 0.2113 | |
|
| cosine_mrr@10 | 0.1472 | |
|
| **cosine_map@100** | **0.1688** | |
|
|
|
#### 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 | 0.167 | |
|
| cosine_accuracy@5 | 0.2417 | |
|
| cosine_accuracy@10 | 0.4174 | |
|
| cosine_precision@1 | 0.0591 | |
|
| cosine_precision@3 | 0.0557 | |
|
| cosine_precision@5 | 0.0483 | |
|
| cosine_precision@10 | 0.0417 | |
|
| cosine_recall@1 | 0.0591 | |
|
| cosine_recall@3 | 0.167 | |
|
| cosine_recall@5 | 0.2417 | |
|
| cosine_recall@10 | 0.4174 | |
|
| cosine_ndcg@10 | 0.2074 | |
|
| cosine_mrr@10 | 0.1442 | |
|
| **cosine_map@100** | **0.1663** | |
|
|
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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 | 0.0643 | |
|
| cosine_accuracy@3 | 0.1617 | |
|
| cosine_accuracy@5 | 0.2417 | |
|
| cosine_accuracy@10 | 0.4052 | |
|
| cosine_precision@1 | 0.0643 | |
|
| cosine_precision@3 | 0.0539 | |
|
| cosine_precision@5 | 0.0483 | |
|
| cosine_precision@10 | 0.0405 | |
|
| cosine_recall@1 | 0.0643 | |
|
| cosine_recall@3 | 0.1617 | |
|
| cosine_recall@5 | 0.2417 | |
|
| cosine_recall@10 | 0.4052 | |
|
| cosine_ndcg@10 | 0.2063 | |
|
| cosine_mrr@10 | 0.1464 | |
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| **cosine_map@100** | **0.17** | |
|
|
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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 | 0.0557 | |
|
| cosine_accuracy@3 | 0.1565 | |
|
| cosine_accuracy@5 | 0.2278 | |
|
| cosine_accuracy@10 | 0.3913 | |
|
| cosine_precision@1 | 0.0557 | |
|
| cosine_precision@3 | 0.0522 | |
|
| cosine_precision@5 | 0.0456 | |
|
| cosine_precision@10 | 0.0391 | |
|
| cosine_recall@1 | 0.0557 | |
|
| cosine_recall@3 | 0.1565 | |
|
| cosine_recall@5 | 0.2278 | |
|
| cosine_recall@10 | 0.3913 | |
|
| cosine_ndcg@10 | 0.1965 | |
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| cosine_mrr@10 | 0.1377 | |
|
| **cosine_map@100** | **0.1621** | |
|
|
|
#### 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 | 0.0678 | |
|
| cosine_accuracy@3 | 0.1704 | |
|
| cosine_accuracy@5 | 0.2574 | |
|
| cosine_accuracy@10 | 0.4174 | |
|
| cosine_precision@1 | 0.0678 | |
|
| cosine_precision@3 | 0.0568 | |
|
| cosine_precision@5 | 0.0515 | |
|
| cosine_precision@10 | 0.0417 | |
|
| cosine_recall@1 | 0.0678 | |
|
| cosine_recall@3 | 0.1704 | |
|
| cosine_recall@5 | 0.2574 | |
|
| cosine_recall@10 | 0.4174 | |
|
| cosine_ndcg@10 | 0.2142 | |
|
| cosine_mrr@10 | 0.1528 | |
|
| **cosine_map@100** | **0.1754** | |
|
|
|
#### 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 | 0.0522 | |
|
| cosine_accuracy@3 | 0.1461 | |
|
| cosine_accuracy@5 | 0.233 | |
|
| cosine_accuracy@10 | 0.4035 | |
|
| cosine_precision@1 | 0.0522 | |
|
| cosine_precision@3 | 0.0487 | |
|
| cosine_precision@5 | 0.0466 | |
|
| cosine_precision@10 | 0.0403 | |
|
| cosine_recall@1 | 0.0522 | |
|
| cosine_recall@3 | 0.1461 | |
|
| cosine_recall@5 | 0.233 | |
|
| cosine_recall@10 | 0.4035 | |
|
| cosine_ndcg@10 | 0.1961 | |
|
| cosine_mrr@10 | 0.1339 | |
|
| **cosine_map@100** | **0.1564** | |
|
|
|
<!-- |
|
## 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: 5,175 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: 43.23 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.25 tokens</li><li>max: 46 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| |
|
| <code>Aquest tràmit us permet consultar informació de les anotacions d'entrada i sortida que hi consten al registre de l'Ajuntament de Sant Quirze del Vallès.</code> | <code>Quin és el format de les dades de sortida del tràmit?</code> | |
|
| <code>Tràmit a través del qual la persona interessada posa en coneixement de l’Ajuntament la voluntat de: ... Renunciar a una llicència prèviament atorgada.</code> | <code>Quin és el resultat de la renúncia a una llicència urbanística prèviament atorgada?</code> | |
|
| <code>D’acord amb el plànol d'ubicació de parades: Mercat de diumenges a Les Fonts</code> | <code>Quin és el plànol d'ubicació de parades del mercat de diumenges a Les Fonts?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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1024, |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
|
1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
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- `bf16`: True |
|
- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
|
- `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 |
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- `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 |
|
- `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 |
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- `hub_strategy`: every_save |
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- `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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.4938 | 10 | 3.936 | - | - | - | - | - | - | |
|
| 0.9877 | 20 | 2.7857 | 0.1550 | 0.1522 | 0.1557 | 0.1507 | 0.1344 | 0.1503 | |
|
| 1.4815 | 30 | 1.4901 | - | - | - | - | - | - | |
|
| 1.9753 | 40 | 1.3464 | 0.1580 | 0.1654 | 0.1695 | 0.1580 | 0.1510 | 0.1624 | |
|
| 2.4691 | 50 | 0.7755 | - | - | - | - | - | - | |
|
| 2.9630 | 60 | 0.8553 | 0.1608 | 0.1705 | 0.1647 | 0.1661 | 0.1564 | 0.1689 | |
|
| 3.4568 | 70 | 0.5817 | - | - | - | - | - | - | |
|
| 3.9506 | 80 | 0.6587 | - | - | - | - | - | - | |
|
| 4.0 | 81 | - | 0.1672 | 0.1657 | 0.1620 | 0.1689 | 0.1556 | 0.1669 | |
|
| 4.4444 | 90 | 0.4847 | - | - | - | - | - | - | |
|
| **4.9383** | **100** | **0.6024** | **0.1688** | **0.1754** | **0.1621** | **0.17** | **0.1564** | **0.1663** | |
|
|
|
* 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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--> |
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<!-- |
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## Model Card Authors |
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|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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|
|
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<!-- |
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## Model Card Contact |
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|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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--> |
