|
--- |
|
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:828 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: Comunicació prèvia per l'execució de cales, pous i sondejos, en |
|
terreny privat, previs a l'actuació definitiva. |
|
sentences: |
|
- Quin és el requisit per a l'execució de les obres en terreny privat? |
|
- Quin és el propòsit del tràmit de rectificació de dades personals? |
|
- Quin és el requisit per a la crema en zones de conservació? |
|
- source_sentence: En el mateix tràmit també es pot actualitzar el canvi de domicili |
|
o dades personals, si escau. |
|
sentences: |
|
- Quins tributs puc domiciliar amb aquest tràmit? |
|
- Quin és el compromís del titular de l'activitat en la Declaració responsable? |
|
- Quin és el tràmit que permet actualitzar les dades personals? |
|
- source_sentence: El reconeixement administratiu del dret comunicat es produeix salvat |
|
el dret de propietat, sens perjudici del de tercers ni de les competències d’altres |
|
organismes i administracions. |
|
sentences: |
|
- Quin és el tràmit que permet una major transparència en la gestió dels animals |
|
domèstics? |
|
- Quin és el requisit per considerar una tala de masses arbòries? |
|
- Quin és el reconeixement administratiu del dret comunicat? |
|
- source_sentence: El seu objecte és que -prèviament a la seva execució material- |
|
l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, |
|
així com a les ordenances municipals. |
|
sentences: |
|
- Quin és el resultat de rectificar les meves dades personals? |
|
- Quin és el paper de les llicències urbanístiques en la instal·lació de construccions |
|
auxiliars o mòduls prefabricats? |
|
- Quin és l'objectiu de l'Ajuntament en aquest tràmit? |
|
- source_sentence: 'Permet sol·licitar l’autorització per a l’ús comú especial de |
|
la via pública per a reserves temporals d’estacionament i espai públic per: càrrega/descàrrega |
|
de materials diversos davant d''una obra;' |
|
sentences: |
|
- Quin és el propòsit de les actuacions de manteniment d'elements de façana i cobertes? |
|
- Quin és el tràmit per canviar el domicili del permís de conducció i del permís |
|
de circulació? |
|
- Quins són els materials que es poden càrregar/descarregar en l'ocupació i reserves |
|
temporals amb càrrega/descàrrega de materials? |
|
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: 0.1956521739130435 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6739130434782609 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7717391304347826 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1956521739130435 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18115942028985504 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13478260869565215 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07717391304347823 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1956521739130435 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6739130434782609 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7717391304347826 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.48504415203944085 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.39229641131815035 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4002530280745044 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1956521739130435 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5543478260869565 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6739130434782609 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7717391304347826 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1956521739130435 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18478260869565213 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13478260869565215 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07717391304347823 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1956521739130435 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5543478260869565 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6739130434782609 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7717391304347826 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.48804421462232656 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3962215320910973 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.404212372178018 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.20652173913043478 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6521739130434783 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7608695652173914 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.20652173913043478 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18115942028985504 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13043478260869562 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07608695652173911 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.20652173913043478 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6521739130434783 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7608695652173914 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4840641874049137 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.39500086266390616 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4031258766496075 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.18478260869565216 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6521739130434783 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.75 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.18478260869565216 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18115942028985504 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13043478260869562 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07499999999999998 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.18478260869565216 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6521739130434783 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.75 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4702420475154915 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.3799301242236025 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.38860307402910876 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.22826086956521738 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6956521739130435 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.782608695652174 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.22826086956521738 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18115942028985504 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13913043478260867 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07826086956521737 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.22826086956521738 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5434782608695652 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6956521739130435 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.782608695652174 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5045819494113778 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.41489820565907526 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4206777643300118 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.17391304347826086 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4891304347826087 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6630434782608695 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7608695652173914 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.17391304347826086 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.16304347826086954 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1326086956521739 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07608695652173911 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.17391304347826086 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4891304347826087 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6630434782608695 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7608695652173914 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4628441336923734 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.36670548654244295 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.37290616382203134 |
|
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/sqv-v3") |
|
# Run inference |
|
sentences = [ |
|
"Permet sol·licitar l’autorització per a l’ús comú especial de la via pública per a reserves temporals d’estacionament i espai públic per: càrrega/descàrrega de materials diversos davant d'una obra;", |
|
"Quins són els materials que es poden càrregar/descarregar en l'ocupació i reserves temporals amb càrrega/descàrrega de materials?", |
|
'Quin és el tràmit per canviar el domicili del permís de conducció i del permís de circulació?', |
|
] |
|
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 | 0.1957 | |
|
| cosine_accuracy@3 | 0.5435 | |
|
| cosine_accuracy@5 | 0.6739 | |
|
| cosine_accuracy@10 | 0.7717 | |
|
| cosine_precision@1 | 0.1957 | |
|
| cosine_precision@3 | 0.1812 | |
|
| cosine_precision@5 | 0.1348 | |
|
| cosine_precision@10 | 0.0772 | |
|
| cosine_recall@1 | 0.1957 | |
|
| cosine_recall@3 | 0.5435 | |
|
| cosine_recall@5 | 0.6739 | |
|
| cosine_recall@10 | 0.7717 | |
|
| cosine_ndcg@10 | 0.485 | |
|
| cosine_mrr@10 | 0.3923 | |
|
| **cosine_map@100** | **0.4003** | |
|
|
|
#### 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 | 0.1957 | |
|
| cosine_accuracy@3 | 0.5543 | |
|
| cosine_accuracy@5 | 0.6739 | |
|
| cosine_accuracy@10 | 0.7717 | |
|
| cosine_precision@1 | 0.1957 | |
|
| cosine_precision@3 | 0.1848 | |
|
| cosine_precision@5 | 0.1348 | |
|
| cosine_precision@10 | 0.0772 | |
|
| cosine_recall@1 | 0.1957 | |
|
| cosine_recall@3 | 0.5543 | |
|
| cosine_recall@5 | 0.6739 | |
|
| cosine_recall@10 | 0.7717 | |
|
| cosine_ndcg@10 | 0.488 | |
|
| cosine_mrr@10 | 0.3962 | |
|
| **cosine_map@100** | **0.4042** | |
|
|
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#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.2065 | |
|
| cosine_accuracy@3 | 0.5435 | |
|
| cosine_accuracy@5 | 0.6522 | |
|
| cosine_accuracy@10 | 0.7609 | |
|
| cosine_precision@1 | 0.2065 | |
|
| cosine_precision@3 | 0.1812 | |
|
| cosine_precision@5 | 0.1304 | |
|
| cosine_precision@10 | 0.0761 | |
|
| cosine_recall@1 | 0.2065 | |
|
| cosine_recall@3 | 0.5435 | |
|
| cosine_recall@5 | 0.6522 | |
|
| cosine_recall@10 | 0.7609 | |
|
| cosine_ndcg@10 | 0.4841 | |
|
| cosine_mrr@10 | 0.395 | |
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| **cosine_map@100** | **0.4031** | |
|
|
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#### Information Retrieval |
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* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.1848 | |
|
| cosine_accuracy@3 | 0.5435 | |
|
| cosine_accuracy@5 | 0.6522 | |
|
| cosine_accuracy@10 | 0.75 | |
|
| cosine_precision@1 | 0.1848 | |
|
| cosine_precision@3 | 0.1812 | |
|
| cosine_precision@5 | 0.1304 | |
|
| cosine_precision@10 | 0.075 | |
|
| cosine_recall@1 | 0.1848 | |
|
| cosine_recall@3 | 0.5435 | |
|
| cosine_recall@5 | 0.6522 | |
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| cosine_recall@10 | 0.75 | |
|
| cosine_ndcg@10 | 0.4702 | |
|
| cosine_mrr@10 | 0.3799 | |
|
| **cosine_map@100** | **0.3886** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.2283 | |
|
| cosine_accuracy@3 | 0.5435 | |
|
| cosine_accuracy@5 | 0.6957 | |
|
| cosine_accuracy@10 | 0.7826 | |
|
| cosine_precision@1 | 0.2283 | |
|
| cosine_precision@3 | 0.1812 | |
|
| cosine_precision@5 | 0.1391 | |
|
| cosine_precision@10 | 0.0783 | |
|
| cosine_recall@1 | 0.2283 | |
|
| cosine_recall@3 | 0.5435 | |
|
| cosine_recall@5 | 0.6957 | |
|
| cosine_recall@10 | 0.7826 | |
|
| cosine_ndcg@10 | 0.5046 | |
|
| cosine_mrr@10 | 0.4149 | |
|
| **cosine_map@100** | **0.4207** | |
|
|
|
#### 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 | 0.1739 | |
|
| cosine_accuracy@3 | 0.4891 | |
|
| cosine_accuracy@5 | 0.663 | |
|
| cosine_accuracy@10 | 0.7609 | |
|
| cosine_precision@1 | 0.1739 | |
|
| cosine_precision@3 | 0.163 | |
|
| cosine_precision@5 | 0.1326 | |
|
| cosine_precision@10 | 0.0761 | |
|
| cosine_recall@1 | 0.1739 | |
|
| cosine_recall@3 | 0.4891 | |
|
| cosine_recall@5 | 0.663 | |
|
| cosine_recall@10 | 0.7609 | |
|
| cosine_ndcg@10 | 0.4628 | |
|
| cosine_mrr@10 | 0.3667 | |
|
| **cosine_map@100** | **0.3729** | |
|
|
|
<!-- |
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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: 828 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 828 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 41.95 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.81 tokens</li><li>max: 50 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Consultar l'estat tributari d'un contribuent. Us permet consultar l'estat dels rebuts i liquidacions que estan a nom del contribuent titular d'un certificat electrònic, així com els elements que configuren el càlcul per determinar el deute tributari de cadascun d'ells.</code> | <code>Com puc consultar l'estat tributari d'un contribuent?</code> | |
|
| <code>L'informe facultatiu servirà per tramitar una autorització de residència temporal per arrelament social.</code> | <code>Quin és el tràmit relacionat amb la residència a l'Ajuntament?</code> | |
|
| <code>Aquesta targeta, és el document que dona dret a persones físiques o jurídiques titulars de vehicles adaptats destinats al transport col·lectiu de persones amb discapacitat...</code> | <code>Quin és el benefici de tenir la targeta d'aparcament de transport col·lectiu per a les persones amb discapacitat?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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1024, |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 5 |
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- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.2 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
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|
|
#### 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 |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `eval_use_gather_object`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:-------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.9231 | 3 | - | 0.3914 | 0.3466 | 0.3625 | 0.3778 | 0.3067 | 0.3810 | |
|
| 1.8462 | 6 | - | 0.3835 | 0.3940 | 0.3789 | 0.3857 | 0.3407 | 0.3808 | |
|
| 2.7692 | 9 | - | 0.4028 | 0.4159 | 0.3961 | 0.4098 | 0.3803 | 0.4029 | |
|
| 3.0769 | 10 | 3.1546 | - | - | - | - | - | - | |
|
| **4.0** | **13** | **-** | **0.3992** | **0.4209** | **0.3905** | **0.4121** | **0.3806** | **0.4009** | |
|
| 4.6154 | 15 | - | 0.4003 | 0.4207 | 0.3886 | 0.4031 | 0.3729 | 0.4042 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.44.2 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 0.35.0.dev0 |
|
- Datasets: 3.0.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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<!-- |
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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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## 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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## 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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