|
--- |
|
base_model: BAAI/bge-m3 |
|
datasets: [] |
|
language: [] |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:5750 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: El seu objecte és que -prèviament a la seva execució material- |
|
l'Ajuntament comprovi l'adequació de l'actuació a la normativa i planejament, |
|
així com a les ordenances municipals. |
|
sentences: |
|
- Quin és el paper de la normativa en la llicència de tala de masses arbòries? |
|
- Com puc actualitzar les meves dades de naixement al Padró? |
|
- Quin és el paper de la persona tècnica competent en la llicència per a la primera |
|
utilització i ocupació parcial de l'edifici? |
|
- source_sentence: El seu objecte és que -prèviament a la seva execució material- |
|
l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, |
|
així com a les ordenances municipals sobre l’ús del sòl i edificació. |
|
sentences: |
|
- Quin és el propòsit del tràmit CA05? |
|
- Quin és el propòsit del tràmit de llicència d'instal·lació de producció d'energia |
|
elèctrica? |
|
- Quin és el paper de l'Ajuntament de Sant Quirze del Vallès en la notificació electrònica |
|
de procediments? |
|
- source_sentence: 'PROFESSIONALS: Assistència jurídica, traducció/interpretació, |
|
psicologia, o qualsevol professió o habilitat que vulgueu posar a disposició del |
|
banc de recursos.' |
|
sentences: |
|
- Quin és el propòsit del tràmit de comunicació prèvia d'obertura d'activitat de |
|
baix risc? |
|
- Quin és el tipus d’autorització que es necessita per a talls de carrers? |
|
- Quin és el paper dels professionals en el banc de recursos? |
|
- source_sentence: No està especificat |
|
sentences: |
|
- Quin és el percentatge de bonificació per a una família nombrosa amb 3 membres |
|
i una renda màxima anual bruta de 25.815,45 euros? |
|
- Quin és el propòsit del tràmit de baixa del Padró d'Habitants per defunció? |
|
- Quin és el procediment per a cancel·lar les concessions de drets funeraris de |
|
nínxols? |
|
- source_sentence: 'Import En cas de renovació per caducitat, pèrdua, sostracció o |
|
deteriorament: 12,00 € (en metàl·lic i preferiblement import exacte).' |
|
sentences: |
|
- Quin és el procediment per a la renovació del DNI en cas de sostracció? |
|
- Quin és el paper del motiu legítim en l'oposició de dades personals en cas de |
|
motiu legítim i situació personal concreta? |
|
- Vull fer una activitat a l'espai públic, quin és el tràmit que debo seguir? |
|
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: 0.0406885758998435 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11737089201877934 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18153364632237873 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3302034428794992 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03912363067292644 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03630672926447575 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03302034428794992 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11737089201877934 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18153364632237873 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3302034428794992 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15804646538595332 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10652433117221861 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12794271910761573 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.03912363067292645 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.107981220657277 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18153364632237873 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3286384976525822 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.03912363067292645 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03599374021909233 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03630672926447575 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03286384976525822 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.03912363067292645 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.107981220657277 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18153364632237873 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3286384976525822 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15506867908727437 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10328203790645119 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12470788174358402 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.10172143974960876 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.16588419405320814 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3223787167449139 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.033907146583202916 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03317683881064163 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03223787167449139 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.10172143974960876 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.16588419405320814 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3223787167449139 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15172399342641055 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1010190774275283 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12301092660478197 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.04225352112676056 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.10954616588419405 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.18466353677621283 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3270735524256651 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.04225352112676056 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03651538862806468 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03693270735524257 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03270735524256651 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.04225352112676056 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.10954616588419405 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.18466353677621283 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3270735524256651 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15644008525556197 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10541458628313109 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.1273528705075161 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.11267605633802817 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.17996870109546165 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.3145539906103286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03755868544600939 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03599374021909233 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.03145539906103287 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.11267605633802817 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.17996870109546165 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.3145539906103286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.15177339619789426 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.10291936806021326 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.12605282457123526 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.09859154929577464 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.1596244131455399 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.29107981220657275 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.03286384976525822 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.03192488262910798 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.02910798122065728 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.0406885758998435 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.09859154929577464 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.1596244131455399 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.29107981220657275 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.14046451788883374 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.09552562287304085 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.11941800675417487 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-m3 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 1024 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("adriansanz/sqv-5ep") |
|
# Run inference |
|
sentences = [ |
|
'Import En cas de renovació per caducitat, pèrdua, sostracció o deteriorament: 12,00 € (en metàl·lic i preferiblement import exacte).', |
|
'Quin és el procediment per a la renovació del DNI en cas de sostracció?', |
|
"Quin és el paper del motiu legítim en l'oposició de dades personals en cas de motiu legítim i situació personal concreta?", |
|
] |
|
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 | 0.0407 | |
|
| cosine_accuracy@3 | 0.1174 | |
|
| cosine_accuracy@5 | 0.1815 | |
|
| cosine_accuracy@10 | 0.3302 | |
|
| cosine_precision@1 | 0.0407 | |
|
| cosine_precision@3 | 0.0391 | |
|
| cosine_precision@5 | 0.0363 | |
|
| cosine_precision@10 | 0.033 | |
|
| cosine_recall@1 | 0.0407 | |
|
| cosine_recall@3 | 0.1174 | |
|
| cosine_recall@5 | 0.1815 | |
|
| cosine_recall@10 | 0.3302 | |
|
| cosine_ndcg@10 | 0.158 | |
|
| cosine_mrr@10 | 0.1065 | |
|
| **cosine_map@100** | **0.1279** | |
|
|
|
#### 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 | 0.0391 | |
|
| cosine_accuracy@3 | 0.108 | |
|
| cosine_accuracy@5 | 0.1815 | |
|
| cosine_accuracy@10 | 0.3286 | |
|
| cosine_precision@1 | 0.0391 | |
|
| cosine_precision@3 | 0.036 | |
|
| cosine_precision@5 | 0.0363 | |
|
| cosine_precision@10 | 0.0329 | |
|
| cosine_recall@1 | 0.0391 | |
|
| cosine_recall@3 | 0.108 | |
|
| cosine_recall@5 | 0.1815 | |
|
| cosine_recall@10 | 0.3286 | |
|
| cosine_ndcg@10 | 0.1551 | |
|
| cosine_mrr@10 | 0.1033 | |
|
| **cosine_map@100** | **0.1247** | |
|
|
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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 | 0.0407 | |
|
| cosine_accuracy@3 | 0.1017 | |
|
| cosine_accuracy@5 | 0.1659 | |
|
| cosine_accuracy@10 | 0.3224 | |
|
| cosine_precision@1 | 0.0407 | |
|
| cosine_precision@3 | 0.0339 | |
|
| cosine_precision@5 | 0.0332 | |
|
| cosine_precision@10 | 0.0322 | |
|
| cosine_recall@1 | 0.0407 | |
|
| cosine_recall@3 | 0.1017 | |
|
| cosine_recall@5 | 0.1659 | |
|
| cosine_recall@10 | 0.3224 | |
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| cosine_ndcg@10 | 0.1517 | |
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| cosine_mrr@10 | 0.101 | |
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| **cosine_map@100** | **0.123** | |
|
|
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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 | 0.0423 | |
|
| cosine_accuracy@3 | 0.1095 | |
|
| cosine_accuracy@5 | 0.1847 | |
|
| cosine_accuracy@10 | 0.3271 | |
|
| cosine_precision@1 | 0.0423 | |
|
| cosine_precision@3 | 0.0365 | |
|
| cosine_precision@5 | 0.0369 | |
|
| cosine_precision@10 | 0.0327 | |
|
| cosine_recall@1 | 0.0423 | |
|
| cosine_recall@3 | 0.1095 | |
|
| cosine_recall@5 | 0.1847 | |
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| cosine_recall@10 | 0.3271 | |
|
| cosine_ndcg@10 | 0.1564 | |
|
| cosine_mrr@10 | 0.1054 | |
|
| **cosine_map@100** | **0.1274** | |
|
|
|
#### Information Retrieval |
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* 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 | 0.0407 | |
|
| cosine_accuracy@3 | 0.1127 | |
|
| cosine_accuracy@5 | 0.18 | |
|
| cosine_accuracy@10 | 0.3146 | |
|
| cosine_precision@1 | 0.0407 | |
|
| cosine_precision@3 | 0.0376 | |
|
| cosine_precision@5 | 0.036 | |
|
| cosine_precision@10 | 0.0315 | |
|
| cosine_recall@1 | 0.0407 | |
|
| cosine_recall@3 | 0.1127 | |
|
| cosine_recall@5 | 0.18 | |
|
| cosine_recall@10 | 0.3146 | |
|
| cosine_ndcg@10 | 0.1518 | |
|
| cosine_mrr@10 | 0.1029 | |
|
| **cosine_map@100** | **0.1261** | |
|
|
|
#### 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 | 0.0407 | |
|
| cosine_accuracy@3 | 0.0986 | |
|
| cosine_accuracy@5 | 0.1596 | |
|
| cosine_accuracy@10 | 0.2911 | |
|
| cosine_precision@1 | 0.0407 | |
|
| cosine_precision@3 | 0.0329 | |
|
| cosine_precision@5 | 0.0319 | |
|
| cosine_precision@10 | 0.0291 | |
|
| cosine_recall@1 | 0.0407 | |
|
| cosine_recall@3 | 0.0986 | |
|
| cosine_recall@5 | 0.1596 | |
|
| cosine_recall@10 | 0.2911 | |
|
| cosine_ndcg@10 | 0.1405 | |
|
| cosine_mrr@10 | 0.0955 | |
|
| **cosine_map@100** | **0.1194** | |
|
|
|
<!-- |
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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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<!-- |
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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 |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 5,750 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: 4 tokens</li><li>mean: 43.32 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.77 tokens</li><li>max: 45 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| |
|
| <code>Aquest tràmit permet donar d'alta ofertes de treball que es gestionaran pel Servei a l'Ocupació.</code> | <code>Com puc saber si el meu perfil és compatible amb les ofertes de treball?</code> | |
|
| <code>El titular de l’activitat ha de declarar sota la seva responsabilitat, que compleix els requisits establerts per la normativa vigent per a l’exercici de l’activitat, que disposa d’un certificat tècnic justificatiu i que es compromet a mantenir-ne el compliment durant el seu exercici.</code> | <code>Quin és el paper del titular de l'activitat en la Declaració responsable?</code> | |
|
| <code>Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.</code> | <code>Quin és el paper del cedent en la transmissió de drets funeraris?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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1024, |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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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 |
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- `warmup_ratio`: 0.2 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `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 |
|
- `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 |
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- `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 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
|
- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `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 |
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- `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 |
|
- `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 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.4444 | 10 | 4.5093 | - | - | - | - | - | - | |
|
| 0.8889 | 20 | 2.7989 | - | - | - | - | - | - | |
|
| 0.9778 | 22 | - | 0.1072 | 0.1182 | 0.1122 | 0.1083 | 0.1044 | 0.1082 | |
|
| 1.3333 | 30 | 1.8343 | - | - | - | - | - | - | |
|
| 1.7778 | 40 | 1.5248 | - | - | - | - | - | - | |
|
| 2.0 | 45 | - | 0.1182 | 0.1203 | 0.1163 | 0.1188 | 0.1209 | 0.1229 | |
|
| 2.2222 | 50 | 0.9624 | - | - | - | - | - | - | |
|
| 2.6667 | 60 | 1.1161 | - | - | - | - | - | - | |
|
| **2.9778** | **67** | **-** | **0.1235** | **0.1324** | **0.1302** | **0.1252** | **0.1213** | **0.1239** | |
|
| 3.1111 | 70 | 0.7405 | - | - | - | - | - | - | |
|
| 3.5556 | 80 | 0.8621 | - | - | - | - | - | - | |
|
| 4.0 | 90 | 0.6071 | 0.1249 | 0.1282 | 0.1310 | 0.1280 | 0.1181 | 0.1278 | |
|
| 4.4444 | 100 | 0.7091 | - | - | - | - | - | - | |
|
| 4.8889 | 110 | 0.606 | 0.1279 | 0.1261 | 0.1274 | 0.1230 | 0.1194 | 0.1247 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.4.0+cu121 |
|
- Accelerate: 0.35.0.dev0 |
|
- Datasets: 2.21.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
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## 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 |
|
|
|
*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 |
|
|
|
*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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--> |
