|
--- |
|
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:4091 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Aquest tràmit permet formalitzar la matrícula a les llars d’infants |
|
municipals, si l'infant ha estat admès al període de preinscripcions. |
|
sentences: |
|
- Quin és el tràmit que es realitza abans de la matrícula? |
|
- Quin és el propòsit de l'Ajuntament en aquest tràmit? |
|
- Què es pot fer amb les exclusions indegudes al Cens Electoral? |
|
- source_sentence: També cal que facis aquest tràmit per revocar o modificar les dades |
|
de correu electrònic i/o telèfon mòbil facilitades per portar a terme les notificacions. |
|
sentences: |
|
- Què passa si vull canviar la meva adreça de correu electrònic? |
|
- Quin és el resultat de no comunicar la finalització de les obres en el termini |
|
establert? |
|
- Quin és el procés de selecció de personal de l'Ajuntament de Viladecavalls? |
|
- source_sentence: Aquest tràmit et permet comunicar a l'ajuntament de Viladecavalls, |
|
l'actuació en representació fer efectuar un tràmit, d'acord a l'article 5 de la |
|
Llei 39/2015,d'1 d'octubre, del Procediment Administratiu Comú de les Administracions |
|
Públiques. |
|
sentences: |
|
- Quin és el registre que es relaciona amb les dades que es modifiquen? |
|
- Quan es pot consultar la llista definitiva d'admessos? |
|
- Quin és el paper de fer efectuar un tràmit en representació a tercers? |
|
- source_sentence: La taxa per la prestació del Servei de Gestió dels Residus Municipals. |
|
sentences: |
|
- Quins són els motius per inscriure's al Servei Local d'Ocupació? |
|
- Quin és el document que es necessita per a la sol·licitud de volants col·lectius |
|
o de convivència? |
|
- Quin és el paper de la taxa d'escombraries en aquest procés? |
|
- source_sentence: S'ha de comunicar la realització de focs d’esbarjo i qualsevol |
|
mena de crema de vegetació agrària en microexplotacions o petites explotacions |
|
agràries... |
|
sentences: |
|
- Què cal fer si no has rebut el document per pagar IVTM o IBI? |
|
- Quin és el tipus de explotacions agràries que estan subjectes a la comunicació |
|
de focs d'esbarjo o cremes de vegetació agrària en microexplotacions? |
|
- Quin és el paper de les bases de la convocatòria en la sol·licitud de subvenció? |
|
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: 0.12408759124087591 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.22627737226277372 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3357664233576642 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5328467153284672 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.12408759124087591 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0754257907542579 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06715328467153285 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05328467153284672 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.12408759124087591 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.22627737226277372 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3357664233576642 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5328467153284672 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.28998901896488977 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21748928281774996 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24037395859471752 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1386861313868613 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.26277372262773724 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3357664233576642 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5693430656934306 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1386861313868613 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08759124087591241 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06715328467153284 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05693430656934306 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1386861313868613 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.26277372262773724 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3357664233576642 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5693430656934306 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.31363827421519996 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23752751708956085 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2568041111732728 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1386861313868613 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.27007299270072993 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3795620437956204 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5693430656934306 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1386861313868613 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0900243309002433 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07591240875912408 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05693430656934306 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1386861313868613 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.27007299270072993 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3795620437956204 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5693430656934306 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.317041085199572 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.24058046576294745 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2615607719139071 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.12408759124087591 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2773722627737226 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.32116788321167883 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5182481751824818 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.12408759124087591 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09245742092457421 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06423357664233577 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.051824817518248176 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.12408759124087591 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2773722627737226 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.32116788321167883 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5182481751824818 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.29042019634687105 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2218456725755996 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24399596123266679 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10948905109489052 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.25547445255474455 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.40145985401459855 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5401459854014599 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10948905109489052 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08515815085158149 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08029197080291971 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05401459854014598 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10948905109489052 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.25547445255474455 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.40145985401459855 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5401459854014599 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2983398214582463 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.22380952380952376 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.2454078859030295 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10948905109489052 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.20437956204379562 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3284671532846715 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5547445255474452 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10948905109489052 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06812652068126519 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06569343065693431 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05547445255474452 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10948905109489052 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.20437956204379562 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3284671532846715 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5547445255474452 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.28965339873789575 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21023635731664925 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22988556376565739 |
|
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-VL-001-5ep") |
|
# Run inference |
|
sentences = [ |
|
"S'ha de comunicar la realització de focs d’esbarjo i qualsevol mena de crema de vegetació agrària en microexplotacions o petites explotacions agràries...", |
|
"Quin és el tipus de explotacions agràries que estan subjectes a la comunicació de focs d'esbarjo o cremes de vegetació agrària en microexplotacions?", |
|
'Quin és el paper de les bases de la convocatòria en la sol·licitud de subvenció?', |
|
] |
|
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 | 0.1241 | |
|
| cosine_accuracy@3 | 0.2263 | |
|
| cosine_accuracy@5 | 0.3358 | |
|
| cosine_accuracy@10 | 0.5328 | |
|
| cosine_precision@1 | 0.1241 | |
|
| cosine_precision@3 | 0.0754 | |
|
| cosine_precision@5 | 0.0672 | |
|
| cosine_precision@10 | 0.0533 | |
|
| cosine_recall@1 | 0.1241 | |
|
| cosine_recall@3 | 0.2263 | |
|
| cosine_recall@5 | 0.3358 | |
|
| cosine_recall@10 | 0.5328 | |
|
| cosine_ndcg@10 | 0.29 | |
|
| cosine_mrr@10 | 0.2175 | |
|
| **cosine_map@100** | **0.2404** | |
|
|
|
#### 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 | 0.1387 | |
|
| cosine_accuracy@3 | 0.2628 | |
|
| cosine_accuracy@5 | 0.3358 | |
|
| cosine_accuracy@10 | 0.5693 | |
|
| cosine_precision@1 | 0.1387 | |
|
| cosine_precision@3 | 0.0876 | |
|
| cosine_precision@5 | 0.0672 | |
|
| cosine_precision@10 | 0.0569 | |
|
| cosine_recall@1 | 0.1387 | |
|
| cosine_recall@3 | 0.2628 | |
|
| cosine_recall@5 | 0.3358 | |
|
| cosine_recall@10 | 0.5693 | |
|
| cosine_ndcg@10 | 0.3136 | |
|
| cosine_mrr@10 | 0.2375 | |
|
| **cosine_map@100** | **0.2568** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1387 | |
|
| cosine_accuracy@3 | 0.2701 | |
|
| cosine_accuracy@5 | 0.3796 | |
|
| cosine_accuracy@10 | 0.5693 | |
|
| cosine_precision@1 | 0.1387 | |
|
| cosine_precision@3 | 0.09 | |
|
| cosine_precision@5 | 0.0759 | |
|
| cosine_precision@10 | 0.0569 | |
|
| cosine_recall@1 | 0.1387 | |
|
| cosine_recall@3 | 0.2701 | |
|
| cosine_recall@5 | 0.3796 | |
|
| cosine_recall@10 | 0.5693 | |
|
| cosine_ndcg@10 | 0.317 | |
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| cosine_mrr@10 | 0.2406 | |
|
| **cosine_map@100** | **0.2616** | |
|
|
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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 | 0.1241 | |
|
| cosine_accuracy@3 | 0.2774 | |
|
| cosine_accuracy@5 | 0.3212 | |
|
| cosine_accuracy@10 | 0.5182 | |
|
| cosine_precision@1 | 0.1241 | |
|
| cosine_precision@3 | 0.0925 | |
|
| cosine_precision@5 | 0.0642 | |
|
| cosine_precision@10 | 0.0518 | |
|
| cosine_recall@1 | 0.1241 | |
|
| cosine_recall@3 | 0.2774 | |
|
| cosine_recall@5 | 0.3212 | |
|
| cosine_recall@10 | 0.5182 | |
|
| cosine_ndcg@10 | 0.2904 | |
|
| cosine_mrr@10 | 0.2218 | |
|
| **cosine_map@100** | **0.244** | |
|
|
|
#### 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 | 0.1095 | |
|
| cosine_accuracy@3 | 0.2555 | |
|
| cosine_accuracy@5 | 0.4015 | |
|
| cosine_accuracy@10 | 0.5401 | |
|
| cosine_precision@1 | 0.1095 | |
|
| cosine_precision@3 | 0.0852 | |
|
| cosine_precision@5 | 0.0803 | |
|
| cosine_precision@10 | 0.054 | |
|
| cosine_recall@1 | 0.1095 | |
|
| cosine_recall@3 | 0.2555 | |
|
| cosine_recall@5 | 0.4015 | |
|
| cosine_recall@10 | 0.5401 | |
|
| cosine_ndcg@10 | 0.2983 | |
|
| cosine_mrr@10 | 0.2238 | |
|
| **cosine_map@100** | **0.2454** | |
|
|
|
#### 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 | 0.1095 | |
|
| cosine_accuracy@3 | 0.2044 | |
|
| cosine_accuracy@5 | 0.3285 | |
|
| cosine_accuracy@10 | 0.5547 | |
|
| cosine_precision@1 | 0.1095 | |
|
| cosine_precision@3 | 0.0681 | |
|
| cosine_precision@5 | 0.0657 | |
|
| cosine_precision@10 | 0.0555 | |
|
| cosine_recall@1 | 0.1095 | |
|
| cosine_recall@3 | 0.2044 | |
|
| cosine_recall@5 | 0.3285 | |
|
| cosine_recall@10 | 0.5547 | |
|
| cosine_ndcg@10 | 0.2897 | |
|
| cosine_mrr@10 | 0.2102 | |
|
| **cosine_map@100** | **0.2299** | |
|
|
|
<!-- |
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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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<!-- |
|
### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 4,091 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: 6 tokens</li><li>mean: 39.34 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.77 tokens</li><li>max: 49 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------| |
|
| <code>Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació prèvia corresponent.</code> | <code>Quin és el resultat esperat després d'obtenir l'informe previ en matèria d'incendis?</code> | |
|
| <code>El certificat tècnic és un requisit per a l'exercici d'una activitat econòmica innòcua.</code> | <code>Quin és el paper del certificat tècnic en la Declaració responsable d'obertura?</code> | |
|
| <code>El document necessari per realitzar l'autoliquidació de taxa per llicència de primera ocupació és la llicència de primera ocupació de l'immoble.</code> | <code>Quin és el document necessari per realitzar l'autoliquidació de taxa per llicència de primera ocupació?</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", |
|
"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, |
|
1, |
|
1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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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 |
|
- `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 |
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- `tf32`: True |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
|
- `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 | |
|
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.625 | 10 | 4.3533 | - | - | - | - | - | - | |
|
| 1.0 | 16 | - | 0.2076 | 0.2123 | 0.2055 | 0.1996 | 0.2188 | 0.1861 | |
|
| 1.2461 | 20 | 2.4149 | - | - | - | - | - | - | |
|
| 1.8711 | 30 | 1.1968 | - | - | - | - | - | - | |
|
| 1.9961 | 32 | - | 0.2056 | 0.2318 | 0.2363 | 0.1932 | 0.2330 | 0.2255 | |
|
| 2.4922 | 40 | 0.7983 | - | - | - | - | - | - | |
|
| **2.9922** | **48** | **-** | **0.2322** | **0.2512** | **0.2514** | **0.2385** | **0.2437** | **0.2489** | |
|
| 3.1133 | 50 | 0.4869 | - | - | - | - | - | - | |
|
| 3.7383 | 60 | 0.3793 | - | - | - | - | - | - | |
|
| 3.9883 | 64 | - | 0.2414 | 0.2364 | 0.2365 | 0.2244 | 0.2167 | 0.2190 | |
|
| 4.3594 | 70 | 0.3421 | - | - | - | - | - | - | |
|
| 4.9844 | 80 | 0.2925 | 0.2404 | 0.2568 | 0.2616 | 0.2440 | 0.2454 | 0.2299 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.2.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.5.0+cu121 |
|
- Accelerate: 1.1.0.dev0 |
|
- Datasets: 3.1.0 |
|
- 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 |
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|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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--> |
|
|
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<!-- |
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## Model Card Contact |
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|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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--> |
