|
--- |
|
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:2372 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Heu de veure si és necessari un estudi d'aïllament acústic i quin |
|
nivell d'aïllament acústic precisa l'activitat. |
|
sentences: |
|
- Quin és el paper de les persones que resideixen amb el titular del dret d'habitatge |
|
en la política d'habitatge? |
|
- Quin és el límit de superfície per a les carpes informatives? |
|
- Quin és l'objectiu de l'estudi d'aïllament acústic? |
|
- source_sentence: 'Si us voleu matricular al proper curs 2022-2023 d''arts plàstiques |
|
ho podeu fer a partir del 1 de juliol a les 16h, seleccionant una d''aquestes |
|
opcions:' |
|
sentences: |
|
- Quin és el període de matrícula per al curs 2022-2023 d'arts plàstiques? |
|
- Quan no cal presentar al·legacions en un expedient de baixa d'ofici? |
|
- Quin és l'objectiu de les al·legacions respecte a un expedient sancionador de |
|
l'Ordenança Municipal de Civisme i Convivència Ciutadana? |
|
- source_sentence: Annexes Econòmics (Cooperació) |
|
sentences: |
|
- Qui és el responsable de l'elaboració de l'informe d'adequació de l'habitatge? |
|
- Què han de fer les persones interessades durant el tràmit d'audiència en el procés |
|
d'inclusió al registre municipal d'immobles desocupats? |
|
- Quin és l'àmbit de la cooperació econòmica? |
|
- source_sentence: En virtut del conveni de col.laboració amb l'Atrium de Viladecans, |
|
tots els ciutadans que acreditin la seva residència a Viladecans es podran beneficiar |
|
d'un 20% de descompte en la programació de teatre, música i dansa, objecte del |
|
conveni. |
|
sentences: |
|
- Quin és el resultat de consultar un expedient d'activitats? |
|
- Quin és el format de resposta d'aquesta sol·licitud? |
|
- Quin és el descompte que s'aplica en la programació de teatre, música i dansa |
|
per als ciutadans de Viladecans? |
|
- source_sentence: Descripció. Retorna en format JSON adequat |
|
sentences: |
|
- Quin és el contingut de l'annex específic? |
|
- Quin tipus d'ocupació es refereix a la renúncia de la llicència? |
|
- Què passa amb l'habitatge? |
|
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: 0.33220910623946037 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5902192242833052 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6998313659359191 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8094435075885329 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.33220910623946037 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1967397414277684 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1399662731871838 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08094435075885327 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.33220910623946037 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5902192242833052 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6998313659359191 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8094435075885329 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5625986746470664 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4843170320404718 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.49243646079034575 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3406408094435076 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5767284991568297 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6981450252951096 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8161888701517707 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3406408094435076 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.19224283305227655 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1396290050590219 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08161888701517706 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3406408094435076 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5767284991568297 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6981450252951096 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8161888701517707 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5661348054508011 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4872065633448428 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.49520736709122076 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3305227655986509 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5801011804384486 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6947723440134908 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8161888701517707 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3305227655986509 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.19336706014614952 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13895446880269813 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08161888701517707 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3305227655986509 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5801011804384486 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6947723440134908 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8161888701517707 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5629643418278626 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4829913809256133 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.49079988310494693 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3288364249578415 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5885328836424958 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7015177065767285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8094435075885329 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3288364249578415 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1961776278808319 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.14030354131534567 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08094435075885327 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3288364249578415 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5885328836424958 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7015177065767285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8094435075885329 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5625842077927447 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.48416981182579805 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.49201787335851555 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3473861720067454 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.581787521079258 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6998313659359191 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.806070826306914 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3473861720067454 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.19392917369308602 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1399662731871838 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0806070826306914 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3473861720067454 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.581787521079258 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6998313659359191 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.806070826306914 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.565365572327355 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4893626703070211 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.49726527073459287 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.2917369308600337 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5682967959527825 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6644182124789207 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7875210792580101 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.2917369308600337 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18943226531759413 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13288364249578413 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07875210792580102 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.2917369308600337 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5682967959527825 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6644182124789207 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7875210792580101 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5320349463938843 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.45117106988945077 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.45948574441166834 |
|
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-SB-001-5ep") |
|
# Run inference |
|
sentences = [ |
|
'Descripció. Retorna en format JSON adequat', |
|
"Quin és el contingut de l'annex específic?", |
|
"Què passa amb l'habitatge?", |
|
] |
|
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 | 0.3322 | |
|
| cosine_accuracy@3 | 0.5902 | |
|
| cosine_accuracy@5 | 0.6998 | |
|
| cosine_accuracy@10 | 0.8094 | |
|
| cosine_precision@1 | 0.3322 | |
|
| cosine_precision@3 | 0.1967 | |
|
| cosine_precision@5 | 0.14 | |
|
| cosine_precision@10 | 0.0809 | |
|
| cosine_recall@1 | 0.3322 | |
|
| cosine_recall@3 | 0.5902 | |
|
| cosine_recall@5 | 0.6998 | |
|
| cosine_recall@10 | 0.8094 | |
|
| cosine_ndcg@10 | 0.5626 | |
|
| cosine_mrr@10 | 0.4843 | |
|
| **cosine_map@100** | **0.4924** | |
|
|
|
#### 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 | 0.3406 | |
|
| cosine_accuracy@3 | 0.5767 | |
|
| cosine_accuracy@5 | 0.6981 | |
|
| cosine_accuracy@10 | 0.8162 | |
|
| cosine_precision@1 | 0.3406 | |
|
| cosine_precision@3 | 0.1922 | |
|
| cosine_precision@5 | 0.1396 | |
|
| cosine_precision@10 | 0.0816 | |
|
| cosine_recall@1 | 0.3406 | |
|
| cosine_recall@3 | 0.5767 | |
|
| cosine_recall@5 | 0.6981 | |
|
| cosine_recall@10 | 0.8162 | |
|
| cosine_ndcg@10 | 0.5661 | |
|
| cosine_mrr@10 | 0.4872 | |
|
| **cosine_map@100** | **0.4952** | |
|
|
|
#### 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 | 0.3305 | |
|
| cosine_accuracy@3 | 0.5801 | |
|
| cosine_accuracy@5 | 0.6948 | |
|
| cosine_accuracy@10 | 0.8162 | |
|
| cosine_precision@1 | 0.3305 | |
|
| cosine_precision@3 | 0.1934 | |
|
| cosine_precision@5 | 0.139 | |
|
| cosine_precision@10 | 0.0816 | |
|
| cosine_recall@1 | 0.3305 | |
|
| cosine_recall@3 | 0.5801 | |
|
| cosine_recall@5 | 0.6948 | |
|
| cosine_recall@10 | 0.8162 | |
|
| cosine_ndcg@10 | 0.563 | |
|
| cosine_mrr@10 | 0.483 | |
|
| **cosine_map@100** | **0.4908** | |
|
|
|
#### 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 | 0.3288 | |
|
| cosine_accuracy@3 | 0.5885 | |
|
| cosine_accuracy@5 | 0.7015 | |
|
| cosine_accuracy@10 | 0.8094 | |
|
| cosine_precision@1 | 0.3288 | |
|
| cosine_precision@3 | 0.1962 | |
|
| cosine_precision@5 | 0.1403 | |
|
| cosine_precision@10 | 0.0809 | |
|
| cosine_recall@1 | 0.3288 | |
|
| cosine_recall@3 | 0.5885 | |
|
| cosine_recall@5 | 0.7015 | |
|
| cosine_recall@10 | 0.8094 | |
|
| cosine_ndcg@10 | 0.5626 | |
|
| cosine_mrr@10 | 0.4842 | |
|
| **cosine_map@100** | **0.492** | |
|
|
|
#### 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 | 0.3474 | |
|
| cosine_accuracy@3 | 0.5818 | |
|
| cosine_accuracy@5 | 0.6998 | |
|
| cosine_accuracy@10 | 0.8061 | |
|
| cosine_precision@1 | 0.3474 | |
|
| cosine_precision@3 | 0.1939 | |
|
| cosine_precision@5 | 0.14 | |
|
| cosine_precision@10 | 0.0806 | |
|
| cosine_recall@1 | 0.3474 | |
|
| cosine_recall@3 | 0.5818 | |
|
| cosine_recall@5 | 0.6998 | |
|
| cosine_recall@10 | 0.8061 | |
|
| cosine_ndcg@10 | 0.5654 | |
|
| cosine_mrr@10 | 0.4894 | |
|
| **cosine_map@100** | **0.4973** | |
|
|
|
#### 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 | 0.2917 | |
|
| cosine_accuracy@3 | 0.5683 | |
|
| cosine_accuracy@5 | 0.6644 | |
|
| cosine_accuracy@10 | 0.7875 | |
|
| cosine_precision@1 | 0.2917 | |
|
| cosine_precision@3 | 0.1894 | |
|
| cosine_precision@5 | 0.1329 | |
|
| cosine_precision@10 | 0.0788 | |
|
| cosine_recall@1 | 0.2917 | |
|
| cosine_recall@3 | 0.5683 | |
|
| cosine_recall@5 | 0.6644 | |
|
| cosine_recall@10 | 0.7875 | |
|
| cosine_ndcg@10 | 0.532 | |
|
| cosine_mrr@10 | 0.4512 | |
|
| **cosine_map@100** | **0.4595** | |
|
|
|
<!-- |
|
## 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: 2,372 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: 35.12 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.49 tokens</li><li>max: 47 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| |
|
| <code>Comunicar la variació d'alguna de les següents dades del Padró Municipal d'Habitants: Nom, Cognoms, Data de naixement, DNI, Passaport, Número de permís de residència (NIE), Sexe, Municipi i/o província de naixement, Nacionalitat, Titulació acadèmica.</code> | <code>Quin és l'objectiu del canvi de dades personals en el Padró Municipal d'Habitants?</code> | |
|
| <code>EN QUÈ CONSISTEIX: Tramitar la sol·licitud de matrimoni civil a l'Ajuntament.</code> | <code>Què és el matrimoni civil a l'Ajuntament de Sant Boi de Llobregat?</code> | |
|
| <code>En domiciliar el pagament de tributs municipals en entitats bancàries.</code> | <code>Quin és el benefici de domiciliar el pagament de tributs?</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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.9664 | 9 | - | 0.4730 | 0.4766 | 0.4640 | 0.4612 | 0.4456 | 0.4083 | |
|
| 1.0738 | 10 | 2.6023 | - | - | - | - | - | - | |
|
| 1.9329 | 18 | - | 0.4951 | 0.4966 | 0.4977 | 0.4773 | 0.4849 | 0.4501 | |
|
| 2.1477 | 20 | 0.974 | - | - | - | - | - | - | |
|
| 2.8993 | 27 | - | 0.4891 | 0.4973 | 0.4941 | 0.4867 | 0.4925 | 0.4684 | |
|
| 3.2215 | 30 | 0.408 | - | - | - | - | - | - | |
|
| **3.9732** | **37** | **-** | **0.4944** | **0.4998** | **0.4931** | **0.4991** | **0.4974** | **0.4616** | |
|
| 4.2953 | 40 | 0.2718 | - | - | - | - | - | - | |
|
| 4.8322 | 45 | - | 0.4924 | 0.4952 | 0.4908 | 0.4920 | 0.4973 | 0.4595 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.2.0 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 1.1.0.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.* |
|
--> |