|
--- |
|
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:5214 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Pel que fa als avals, la Junta de Govern Local en sessió celebrada |
|
el 4 de juliol de 2006, va aprovar els models d'aval en funció del concepte a |
|
garantir. |
|
sentences: |
|
- Quin és el benefici de la unitat de queixes i suggeriments per a la qualitat dels |
|
serveis de l'Ajuntament de Sitges? |
|
- Quin és el paper de la Junta de Govern Local? |
|
- Quin és el propòsit més important del tràmit de canvi de titular de la llicència |
|
de gual? |
|
- source_sentence: Per a tenir dret a ésser inscrit en el Registre de Sol·licitants |
|
d'Habitatge amb Protecció Oficial s'han de complir els procediments i els requisits |
|
establerts per normativa. |
|
sentences: |
|
- Quin és el paper de la persona sol·licitant en la gestió de les fiances o dipòsits |
|
d'una llicència d'obra? |
|
- Quin és el benefici de complir els procediments i els requisits establerts per |
|
normativa? |
|
- Quin és el centre cultural que es troba a l'Escorxador de Sitges i ofereix activitats |
|
culturals? |
|
- source_sentence: Aquest tràmit permet comunicar a l'Ajuntament de Sitges la finalització |
|
de les obres de nova construcció, o bé aquelles que hagin estat objecte de modificació |
|
substancial o d’ampliació quan per a l’autorització de les obres s’hagi exigit |
|
un projecte tècnic i a l’empara d’una llicència urbanística d’obra major. |
|
sentences: |
|
- Què passa si la modificació no té efectes sobre les persones o el medi ambient? |
|
- Quin és el requisit principal per a la gestió diària d'una colònia felina? |
|
- Quin és el paper del tràmit de comunicació prèvia de primera utilització i ocupació |
|
d'edificis i instal·lacions en el procés d'obtenció de la llicència urbanística |
|
d’obra major? |
|
- source_sentence: Es tracta dels ajuts per a la realització de la Inspecció Tècnica |
|
de l’Edifici (ITE) conjuntament amb l’elaboració dels certificats energètics. |
|
sentences: |
|
- Quins són els tipus de garanties que es poden ingressar? |
|
- Quin és el procés d’elaboració dels certificats energètics? |
|
- Quin és el paper de la consulta prèvia de classificació d'activitat en la tramitació |
|
administrativa municipal? |
|
- source_sentence: Les queixes, observacions i suggeriments són una eina important |
|
per a millorar la qualitat dels serveis municipals. |
|
sentences: |
|
- Quin és el propòsit dels ajuts econòmics? |
|
- Què és el que es busca amb les queixes, observacions i suggeriments? |
|
- Qui són les persones beneficiàries de l'ajut per a la creació de noves empreses? |
|
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: 0.14367088607594936 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2818565400843882 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3930379746835443 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5664556962025317 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14367088607594936 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09395218002812938 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07860759493670887 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05664556962025316 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14367088607594936 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2818565400843882 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3930379746835443 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5664556962025317 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.32426778614918705 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.25066212912731944 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2694799737895368 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1470464135021097 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2871308016877637 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.390084388185654 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5630801687763713 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1470464135021097 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09571026722925456 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07801687763713079 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.056308016877637125 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1470464135021097 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2871308016877637 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.390084388185654 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5630801687763713 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.32549268557195893 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.25325421940928294 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.272264774489146 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.14177215189873418 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.28375527426160335 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3890295358649789 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5620253164556962 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14177215189873418 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09458509142053445 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07780590717299578 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05620253164556962 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14177215189873418 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.28375527426160335 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3890295358649789 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5620253164556962 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.322564230377663 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.24968421405130298 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.26885741426647297 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.14345991561181434 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2831223628691983 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3850210970464135 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5550632911392405 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14345991561181434 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09437412095639944 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0770042194092827 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05550632911392406 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14345991561181434 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2831223628691983 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3850210970464135 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5550632911392405 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3205268083804564 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.24917821981113142 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2685327848764784 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.13924050632911392 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2795358649789029 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3837552742616034 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5533755274261604 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.13924050632911392 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09317862165963431 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07675105485232067 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05533755274261602 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.13924050632911392 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2795358649789029 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3837552742616034 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5533755274261604 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.31759054947613424 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2457681166700155 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2649300065982546 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.14029535864978904 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.27531645569620256 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.369831223628692 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5360759493670886 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.14029535864978904 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09177215189873417 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0739662447257384 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.053607594936708865 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.14029535864978904 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.27531645569620256 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.369831223628692 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5360759493670886 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3099216271465372 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.24117783470631593 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2601649646918979 |
|
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-005-5ep") |
|
# Run inference |
|
sentences = [ |
|
'Les queixes, observacions i suggeriments són una eina important per a millorar la qualitat dels serveis municipals.', |
|
'Què és el que es busca amb les queixes, observacions i suggeriments?', |
|
'Quin és el propòsit dels ajuts econòmics?', |
|
] |
|
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 | 0.1437 | |
|
| cosine_accuracy@3 | 0.2819 | |
|
| cosine_accuracy@5 | 0.393 | |
|
| cosine_accuracy@10 | 0.5665 | |
|
| cosine_precision@1 | 0.1437 | |
|
| cosine_precision@3 | 0.094 | |
|
| cosine_precision@5 | 0.0786 | |
|
| cosine_precision@10 | 0.0566 | |
|
| cosine_recall@1 | 0.1437 | |
|
| cosine_recall@3 | 0.2819 | |
|
| cosine_recall@5 | 0.393 | |
|
| cosine_recall@10 | 0.5665 | |
|
| cosine_ndcg@10 | 0.3243 | |
|
| cosine_mrr@10 | 0.2507 | |
|
| **cosine_map@100** | **0.2695** | |
|
|
|
#### 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 | 0.147 | |
|
| cosine_accuracy@3 | 0.2871 | |
|
| cosine_accuracy@5 | 0.3901 | |
|
| cosine_accuracy@10 | 0.5631 | |
|
| cosine_precision@1 | 0.147 | |
|
| cosine_precision@3 | 0.0957 | |
|
| cosine_precision@5 | 0.078 | |
|
| cosine_precision@10 | 0.0563 | |
|
| cosine_recall@1 | 0.147 | |
|
| cosine_recall@3 | 0.2871 | |
|
| cosine_recall@5 | 0.3901 | |
|
| cosine_recall@10 | 0.5631 | |
|
| cosine_ndcg@10 | 0.3255 | |
|
| cosine_mrr@10 | 0.2533 | |
|
| **cosine_map@100** | **0.2723** | |
|
|
|
#### 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 | 0.1418 | |
|
| cosine_accuracy@3 | 0.2838 | |
|
| cosine_accuracy@5 | 0.389 | |
|
| cosine_accuracy@10 | 0.562 | |
|
| cosine_precision@1 | 0.1418 | |
|
| cosine_precision@3 | 0.0946 | |
|
| cosine_precision@5 | 0.0778 | |
|
| cosine_precision@10 | 0.0562 | |
|
| cosine_recall@1 | 0.1418 | |
|
| cosine_recall@3 | 0.2838 | |
|
| cosine_recall@5 | 0.389 | |
|
| cosine_recall@10 | 0.562 | |
|
| cosine_ndcg@10 | 0.3226 | |
|
| cosine_mrr@10 | 0.2497 | |
|
| **cosine_map@100** | **0.2689** | |
|
|
|
#### 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 | 0.1435 | |
|
| cosine_accuracy@3 | 0.2831 | |
|
| cosine_accuracy@5 | 0.385 | |
|
| cosine_accuracy@10 | 0.5551 | |
|
| cosine_precision@1 | 0.1435 | |
|
| cosine_precision@3 | 0.0944 | |
|
| cosine_precision@5 | 0.077 | |
|
| cosine_precision@10 | 0.0555 | |
|
| cosine_recall@1 | 0.1435 | |
|
| cosine_recall@3 | 0.2831 | |
|
| cosine_recall@5 | 0.385 | |
|
| cosine_recall@10 | 0.5551 | |
|
| cosine_ndcg@10 | 0.3205 | |
|
| cosine_mrr@10 | 0.2492 | |
|
| **cosine_map@100** | **0.2685** | |
|
|
|
#### 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 | 0.1392 | |
|
| cosine_accuracy@3 | 0.2795 | |
|
| cosine_accuracy@5 | 0.3838 | |
|
| cosine_accuracy@10 | 0.5534 | |
|
| cosine_precision@1 | 0.1392 | |
|
| cosine_precision@3 | 0.0932 | |
|
| cosine_precision@5 | 0.0768 | |
|
| cosine_precision@10 | 0.0553 | |
|
| cosine_recall@1 | 0.1392 | |
|
| cosine_recall@3 | 0.2795 | |
|
| cosine_recall@5 | 0.3838 | |
|
| cosine_recall@10 | 0.5534 | |
|
| cosine_ndcg@10 | 0.3176 | |
|
| cosine_mrr@10 | 0.2458 | |
|
| **cosine_map@100** | **0.2649** | |
|
|
|
#### 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 | 0.1403 | |
|
| cosine_accuracy@3 | 0.2753 | |
|
| cosine_accuracy@5 | 0.3698 | |
|
| cosine_accuracy@10 | 0.5361 | |
|
| cosine_precision@1 | 0.1403 | |
|
| cosine_precision@3 | 0.0918 | |
|
| cosine_precision@5 | 0.074 | |
|
| cosine_precision@10 | 0.0536 | |
|
| cosine_recall@1 | 0.1403 | |
|
| cosine_recall@3 | 0.2753 | |
|
| cosine_recall@5 | 0.3698 | |
|
| cosine_recall@10 | 0.5361 | |
|
| cosine_ndcg@10 | 0.3099 | |
|
| cosine_mrr@10 | 0.2412 | |
|
| **cosine_map@100** | **0.2602** | |
|
|
|
<!-- |
|
## 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 |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 5,214 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 3 tokens</li><li>mean: 49.66 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.85 tokens</li><li>max: 48 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| <code>Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19)</code> | <code>Quin és el requisit per a les petites empreses per rebre ajuts?</code> | |
|
| <code>En cas de no poder desenvolupar el projecte o activitat per la qual s'ha sol·licitat la subvenció, l'entitat beneficiària pot renunciar a la subvenció.</code> | <code>Puc renunciar a una subvenció si ja l'he rebut?</code> | |
|
| <code>L’Espai Jove de Sitges és l'equipament municipal on els joves poden dur a terme iniciatives pròpies i on també es desenvolupen d’altres impulsades per la regidoria de Joventut.</code> | <code>Quin és el paper de la regidoria de Joventut a l'Espai Jove de Sitges?</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 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.4908 | 10 | 3.3699 | - | - | - | - | - | - | |
|
| 0.9816 | 20 | 1.8761 | 0.2565 | 0.2430 | 0.2509 | 0.2499 | 0.2301 | 0.2567 | |
|
| 1.4724 | 30 | 1.3111 | - | - | - | - | - | - | |
|
| 1.9632 | 40 | 0.8122 | 0.2636 | 0.2578 | 0.2629 | 0.2639 | 0.2486 | 0.2654 | |
|
| 2.4540 | 50 | 0.5903 | - | - | - | - | - | - | |
|
| 2.9448 | 60 | 0.4306 | - | - | - | - | - | - | |
|
| **2.9939** | **61** | **-** | **0.2661** | **0.2636** | **0.2648** | **0.2659** | **0.2544** | **0.2694** | |
|
| 3.4356 | 70 | 0.3553 | - | - | - | - | - | - | |
|
| 3.9264 | 80 | 0.2925 | - | - | - | - | - | - | |
|
| 3.9755 | 81 | - | 0.2701 | 0.2621 | 0.2663 | 0.2706 | 0.2602 | 0.2709 | |
|
| 4.4172 | 90 | 0.2797 | - | - | - | - | - | - | |
|
| 4.9080 | 100 | 0.267 | 0.2695 | 0.2649 | 0.2685 | 0.2689 | 0.2602 | 0.2723 | |
|
|
|
* 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 |
|
|
|
*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.* |
|
--> |
