|
--- |
|
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:4173 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: L'Espai d'escalada és una instal·lació municipal en forma de túnel |
|
a una sala interior, amb una llargada de 10m, una amplada de 4,6m i una alçada |
|
de 4m. |
|
sentences: |
|
- Quin és el registre on es comprova la inscripció dels estrangers amb ciutadania |
|
de l'Espai Econòmic Europeu? |
|
- On es pot trobar les bases generals de les convocatòries de selecció de personal |
|
de l'Ajuntament? |
|
- Quina és la llargada de l'Espai d'Escalada? |
|
- source_sentence: Les activitats s’organitzen per setmanes. |
|
sentences: |
|
- Quin és el format en què es desenvolupen les activitats de l'Estiu Jove? |
|
- Quin és el paper del subjecte passiu en la gestió de pagaments? |
|
- Quin és el benefici de les subvencions? |
|
- source_sentence: Les Estades Esportives cerquen que els infants aprenguin a relacionar-se |
|
i a compartir mitjançant l'esport, experiències i vivències amb d'altres infants |
|
amb qui no estan en contacte durant la resta de l'any. |
|
sentences: |
|
- Quin és el propòsit de l'ajut per a la creació de noves empreses? |
|
- Quin és el propòsit de la llicència de parcel·lació? |
|
- Quin és el benefici principal de les Estades Esportives? |
|
- source_sentence: Declaració tributària mitjançant la qual es sol·licita la baixa |
|
d'una activitat de la Taxa pel servei municipal complementari de recollida, tractament |
|
i eliminació de residus comercials. |
|
sentences: |
|
- Quin és el format de la Declaració de baixa? |
|
- Quin és el resultat de justificar una sol·licitud de canvi a les estades esportives? |
|
- Quin és el període de celebració de la Fira d'Art de Sitges? |
|
- source_sentence: Les entitats inscrites en el Registre resten obligades a comunicar |
|
a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar |
|
la seva cancel·lació o comunicant la seva dissolució. |
|
sentences: |
|
- Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre |
|
municipal d'entitats? |
|
- Quin és el propòsit de la quota del servei de les Llars d'Infants Municipals? |
|
- Quin és el paper de les entitats de protecció dels animals en la gestió de les |
|
colònies urbanes felines? |
|
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: 0.08620689655172414 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21551724137931033 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3275862068965517 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5107758620689655 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08620689655172414 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07183908045977011 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06551724137931034 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05107758620689654 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08620689655172414 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21551724137931033 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3275862068965517 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5107758620689655 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.26401643418499254 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1896731321839082 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2150866107809785 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08405172413793104 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20905172413793102 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.31896551724137934 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08405172413793104 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.069683908045977 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06379310344827585 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08405172413793104 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20905172413793102 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.31896551724137934 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2594763687925116 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.18673713738368922 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21319033477988852 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08620689655172414 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.21120689655172414 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.32112068965517243 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5129310344827587 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08620689655172414 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07040229885057471 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06422413793103447 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.051293103448275854 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08620689655172414 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21120689655172414 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.32112068965517243 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5129310344827587 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2646539120704089 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1899279898741108 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21554766038692458 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08189655172413793 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20474137931034483 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.30603448275862066 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5043103448275862 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08189655172413793 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0682471264367816 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.061206896551724135 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05043103448275862 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08189655172413793 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20474137931034483 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.30603448275862066 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5043103448275862 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.25554093803691474 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1807856116584566 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.20657861277416045 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08405172413793104 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20043103448275862 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3146551724137931 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49137931034482757 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08405172413793104 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0668103448275862 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06293103448275862 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04913793103448275 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08405172413793104 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20043103448275862 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3146551724137931 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49137931034482757 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2516576518560222 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.1794651409414343 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.20584710715396837 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.07974137931034483 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2025862068965517 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3017241379310345 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4956896551724138 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.07974137931034483 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06752873563218391 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0603448275862069 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04956896551724138 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.07974137931034483 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2025862068965517 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3017241379310345 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4956896551724138 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2527082338557514 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.17959085933223878 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2058214047481906 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-m3 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
|
- **Maximum Sequence Length:** 8192 tokens |
|
- **Output Dimensionality:** 1024 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("adriansanz/sitgrsBAAIbge-m3-290824") |
|
# Run inference |
|
sentences = [ |
|
'Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució.', |
|
"Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre municipal d'entitats?", |
|
'Quin és el paper de les entitats de protecció dels animals en la gestió de les colònies urbanes felines?', |
|
] |
|
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 | 0.0862 | |
|
| cosine_accuracy@3 | 0.2155 | |
|
| cosine_accuracy@5 | 0.3276 | |
|
| cosine_accuracy@10 | 0.5108 | |
|
| cosine_precision@1 | 0.0862 | |
|
| cosine_precision@3 | 0.0718 | |
|
| cosine_precision@5 | 0.0655 | |
|
| cosine_precision@10 | 0.0511 | |
|
| cosine_recall@1 | 0.0862 | |
|
| cosine_recall@3 | 0.2155 | |
|
| cosine_recall@5 | 0.3276 | |
|
| cosine_recall@10 | 0.5108 | |
|
| cosine_ndcg@10 | 0.264 | |
|
| cosine_mrr@10 | 0.1897 | |
|
| **cosine_map@100** | **0.2151** | |
|
|
|
#### 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 | 0.0841 | |
|
| cosine_accuracy@3 | 0.2091 | |
|
| cosine_accuracy@5 | 0.319 | |
|
| cosine_accuracy@10 | 0.5 | |
|
| cosine_precision@1 | 0.0841 | |
|
| cosine_precision@3 | 0.0697 | |
|
| cosine_precision@5 | 0.0638 | |
|
| cosine_precision@10 | 0.05 | |
|
| cosine_recall@1 | 0.0841 | |
|
| cosine_recall@3 | 0.2091 | |
|
| cosine_recall@5 | 0.319 | |
|
| cosine_recall@10 | 0.5 | |
|
| cosine_ndcg@10 | 0.2595 | |
|
| cosine_mrr@10 | 0.1867 | |
|
| **cosine_map@100** | **0.2132** | |
|
|
|
#### 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 | 0.0862 | |
|
| cosine_accuracy@3 | 0.2112 | |
|
| cosine_accuracy@5 | 0.3211 | |
|
| cosine_accuracy@10 | 0.5129 | |
|
| cosine_precision@1 | 0.0862 | |
|
| cosine_precision@3 | 0.0704 | |
|
| cosine_precision@5 | 0.0642 | |
|
| cosine_precision@10 | 0.0513 | |
|
| cosine_recall@1 | 0.0862 | |
|
| cosine_recall@3 | 0.2112 | |
|
| cosine_recall@5 | 0.3211 | |
|
| cosine_recall@10 | 0.5129 | |
|
| cosine_ndcg@10 | 0.2647 | |
|
| cosine_mrr@10 | 0.1899 | |
|
| **cosine_map@100** | **0.2155** | |
|
|
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#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.0819 | |
|
| cosine_accuracy@3 | 0.2047 | |
|
| cosine_accuracy@5 | 0.306 | |
|
| cosine_accuracy@10 | 0.5043 | |
|
| cosine_precision@1 | 0.0819 | |
|
| cosine_precision@3 | 0.0682 | |
|
| cosine_precision@5 | 0.0612 | |
|
| cosine_precision@10 | 0.0504 | |
|
| cosine_recall@1 | 0.0819 | |
|
| cosine_recall@3 | 0.2047 | |
|
| cosine_recall@5 | 0.306 | |
|
| cosine_recall@10 | 0.5043 | |
|
| cosine_ndcg@10 | 0.2555 | |
|
| cosine_mrr@10 | 0.1808 | |
|
| **cosine_map@100** | **0.2066** | |
|
|
|
#### 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 | 0.0841 | |
|
| cosine_accuracy@3 | 0.2004 | |
|
| cosine_accuracy@5 | 0.3147 | |
|
| cosine_accuracy@10 | 0.4914 | |
|
| cosine_precision@1 | 0.0841 | |
|
| cosine_precision@3 | 0.0668 | |
|
| cosine_precision@5 | 0.0629 | |
|
| cosine_precision@10 | 0.0491 | |
|
| cosine_recall@1 | 0.0841 | |
|
| cosine_recall@3 | 0.2004 | |
|
| cosine_recall@5 | 0.3147 | |
|
| cosine_recall@10 | 0.4914 | |
|
| cosine_ndcg@10 | 0.2517 | |
|
| cosine_mrr@10 | 0.1795 | |
|
| **cosine_map@100** | **0.2058** | |
|
|
|
#### 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 | 0.0797 | |
|
| cosine_accuracy@3 | 0.2026 | |
|
| cosine_accuracy@5 | 0.3017 | |
|
| cosine_accuracy@10 | 0.4957 | |
|
| cosine_precision@1 | 0.0797 | |
|
| cosine_precision@3 | 0.0675 | |
|
| cosine_precision@5 | 0.0603 | |
|
| cosine_precision@10 | 0.0496 | |
|
| cosine_recall@1 | 0.0797 | |
|
| cosine_recall@3 | 0.2026 | |
|
| cosine_recall@5 | 0.3017 | |
|
| cosine_recall@10 | 0.4957 | |
|
| cosine_ndcg@10 | 0.2527 | |
|
| cosine_mrr@10 | 0.1796 | |
|
| **cosine_map@100** | **0.2058** | |
|
|
|
<!-- |
|
## 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 |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 4,173 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: 8 tokens</li><li>mean: 48.75 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.07 tokens</li><li>max: 47 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------| |
|
| <code>Els ajuts per a la realització d'activitats en el lleure esportiu estan destinats a les entitats sense ànim de lucre que desenvolupen activitats esportives i de lleure.</code> | <code>Quins són els sectors que es beneficien dels ajuts?</code> | |
|
| <code>En el certificat s'indiquen les dades de planejament vigent, classificació del sòl, qualificació urbanística, condicions de l’edificació i usos admesos referides a una finca o solar concreta.</code> | <code>Quin és el contingut de les condicions de l'edificació en el certificat d'aprofitament urbanístic?</code> | |
|
| <code>Aportació de documentació. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2</code> | <code>Quin és el paper dels ajuts en la situació de crisis?</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, |
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512, |
|
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, |
|
1, |
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1, |
|
1 |
|
], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
|
- `num_train_epochs`: 10 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
|
- `bf16`: True |
|
- `tf32`: False |
|
- `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 |
|
- `learning_rate`: 5e-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`: 10 |
|
- `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`: False |
|
- `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 |
|
- `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 | |
|
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.6130 | 10 | 3.0594 | - | - | - | - | - | - | |
|
| 0.9808 | 16 | - | 0.2047 | 0.1922 | 0.2020 | 0.2016 | 0.1774 | 0.2115 | |
|
| 1.2261 | 20 | 1.525 | - | - | - | - | - | - | |
|
| 1.8391 | 30 | 0.7434 | - | - | - | - | - | - | |
|
| 1.9617 | 32 | - | 0.2186 | 0.2003 | 0.2102 | 0.2092 | 0.1870 | 0.2101 | |
|
| 2.4521 | 40 | 0.4451 | - | - | - | - | - | - | |
|
| 2.9425 | 48 | - | 0.2083 | 0.2054 | 0.2091 | 0.2118 | 0.2009 | 0.2140 | |
|
| 3.0651 | 50 | 0.2518 | - | - | - | - | - | - | |
|
| 3.6782 | 60 | 0.1801 | - | - | - | - | - | - | |
|
| 3.9847 | 65 | - | 0.2135 | 0.2071 | 0.2037 | 0.2115 | 0.2030 | 0.2191 | |
|
| 4.2912 | 70 | 0.1483 | - | - | - | - | - | - | |
|
| 4.9042 | 80 | 0.0893 | - | - | - | - | - | - | |
|
| 4.9655 | 81 | - | 0.2066 | 0.2053 | 0.2057 | 0.2137 | 0.1982 | 0.2176 | |
|
| 5.5172 | 90 | 0.0748 | - | - | - | - | - | - | |
|
| 5.9464 | 97 | - | 0.2171 | 0.2113 | 0.2086 | 0.2178 | 0.2120 | 0.2193 | |
|
| 6.1303 | 100 | 0.064 | - | - | - | - | - | - | |
|
| 6.7433 | 110 | 0.0458 | - | - | - | - | - | - | |
|
| 6.9885 | 114 | - | 0.2294 | 0.2132 | 0.2151 | 0.2227 | 0.2054 | 0.2138 | |
|
| 7.3563 | 120 | 0.0436 | - | - | - | - | - | - | |
|
| 7.9693 | 130 | 0.0241 | 0.2133 | 0.2083 | 0.2096 | 0.2138 | 0.2080 | 0.2124 | |
|
| 8.5824 | 140 | 0.021 | - | - | - | - | - | - | |
|
| **8.9502** | **146** | **-** | **0.216** | **0.2074** | **0.2081** | **0.2162** | **0.2094** | **0.2177** | |
|
| 9.1954 | 150 | 0.0237 | - | - | - | - | - | - | |
|
| 9.8084 | 160 | 0.0145 | 0.2151 | 0.2058 | 0.2066 | 0.2155 | 0.2058 | 0.2132 | |
|
|
|
* 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: 0.34.0.dev0 |
|
- Datasets: 2.21.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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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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--> |
