|
--- |
|
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:6468 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: El seu objecte és que -prèviament a la seva execució material- |
|
l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, |
|
així com a les ordenances municipals sobre l’ús del sòl i edificació. |
|
sentences: |
|
- Quin és el paper de les ordenances municipals en la llicència d'extracció d'àrids |
|
i explotació de pedreres? |
|
- Quin és el percentatge de bonificació que es pot obtenir? |
|
- Quin és el propòsit del tràmit d'adjudicació d'habitatges socials i d'emergència? |
|
- source_sentence: La renda és un element important en la tramitació d'un ajornament |
|
o fraccionament, ja que es té en compte per determinar si el sol·licitant compleix |
|
els requisits per a sol·licitar el criteri excepcional. |
|
sentences: |
|
- Quin és el paper de la renda en la tramitació d'un ajornament o fraccionament? |
|
- Quin és l'objectiu del tràmit C03? |
|
- Quin és el paper de les ordenances municipals en la llicència de parcel·lació? |
|
- source_sentence: L’article 14 de la llei 39/2015 estableix l’obligatorietat de l’ús |
|
de mitjans electrònics, informàtics o telemàtics per desenvolupar totes les fases |
|
del procediment de contractació. |
|
sentences: |
|
- Quin és el paper de les ordenances municipals sobre l’ús del sòl i edificació |
|
en el tràmit de modificació substancial de la llicència d'obres? |
|
- Quin és el requisit per a la intervenció d'una persona tècnica? |
|
- Quin és el propòsit de l’article 14 de la llei 39/2015? |
|
- source_sentence: El seu objecte és que -prèviament a la seva execució material- |
|
l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, |
|
així com a les ordenances municipals sobre l’ús del sòl i edificació. |
|
sentences: |
|
- Quin és el paper del planejament en el tràmit de llicència d'obres per l'obertura, |
|
la pavimentació i la modificació de camins rurals? |
|
- Quin és el requisit per presentar una sol·licitud? |
|
- Quin és el resultat de la falta de presentació de la documentació tècnica corresponent? |
|
- source_sentence: L’Ajuntament de Sant Quirze del Vallès reconeix un dret preferent |
|
al titular del dret funerari sobre la corresponent sepultura o al successor o |
|
causahavent de l’anterior titular d’aquest dret, que permet adquirir de nou el |
|
dret funerari referit, sobre la mateixa sepultura, un cop el dret atorgat ha exhaurit |
|
el termini de vigència |
|
sentences: |
|
- Quin és el requisit per a les instal·lacions solars per mantenir la bonificació? |
|
- Quin és el paper del cens electoral en les eleccions? |
|
- Quan es pot adquirir de nou el dret funerari? |
|
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: 0.10173160173160173 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.27705627705627706 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.36796536796536794 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.48268398268398266 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10173160173160173 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09235209235209235 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0735930735930736 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04826839826839826 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10173160173160173 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.27705627705627706 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.36796536796536794 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.48268398268398266 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.27573421573267004 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21126485947914525 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22874042563037256 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11904761904761904 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.29004329004329005 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3658008658008658 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.49567099567099565 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11904761904761904 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09668109668109669 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07316017316017315 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.049567099567099565 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11904761904761904 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.29004329004329005 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3658008658008658 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.49567099567099565 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2892077987787756 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.22525767882910738 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24276232307204765 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.10822510822510822 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2662337662337662 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.36363636363636365 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.5064935064935064 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.10822510822510822 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08874458874458875 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.07272727272727272 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.050649350649350645 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.10822510822510822 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2662337662337662 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.36363636363636365 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.5064935064935064 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.28386807922368074 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21557239057239053 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.23234161860560523 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11471861471861472 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.24025974025974026 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3398268398268398 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4805194805194805 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11471861471861472 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08008658008658008 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.06796536796536796 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04805194805194805 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11471861471861472 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.24025974025974026 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3398268398268398 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4805194805194805 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2749619650624931 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21201642273070856 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.23043548788604293 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.11255411255411256 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.26406926406926406 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.329004329004329 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.487012987012987 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.11255411255411256 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08802308802308802 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0658008658008658 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.048701298701298704 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.11255411255411256 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.26406926406926406 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.329004329004329 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.487012987012987 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.27907708560411776 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.21522795987081703 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.23398722217128723 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.1038961038961039 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.2619047619047619 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.3354978354978355 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.474025974025974 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.1038961038961039 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.0873015873015873 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.0670995670995671 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0474025974025974 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.1038961038961039 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.2619047619047619 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.3354978354978355 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.474025974025974 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2700415740619265 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.20714285714285718 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.22556246902969454 |
|
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-SQV-007-5ep") |
|
# Run inference |
|
sentences = [ |
|
'L’Ajuntament de Sant Quirze del Vallès reconeix un dret preferent al titular del dret funerari sobre la corresponent sepultura o al successor o causahavent de l’anterior titular d’aquest dret, que permet adquirir de nou el dret funerari referit, sobre la mateixa sepultura, un cop el dret atorgat ha exhaurit el termini de vigència', |
|
'Quan es pot adquirir de nou el dret funerari?', |
|
'Quin és el paper del cens electoral en les eleccions?', |
|
] |
|
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 | 0.1017 | |
|
| cosine_accuracy@3 | 0.2771 | |
|
| cosine_accuracy@5 | 0.368 | |
|
| cosine_accuracy@10 | 0.4827 | |
|
| cosine_precision@1 | 0.1017 | |
|
| cosine_precision@3 | 0.0924 | |
|
| cosine_precision@5 | 0.0736 | |
|
| cosine_precision@10 | 0.0483 | |
|
| cosine_recall@1 | 0.1017 | |
|
| cosine_recall@3 | 0.2771 | |
|
| cosine_recall@5 | 0.368 | |
|
| cosine_recall@10 | 0.4827 | |
|
| cosine_ndcg@10 | 0.2757 | |
|
| cosine_mrr@10 | 0.2113 | |
|
| **cosine_map@100** | **0.2287** | |
|
|
|
#### 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 | 0.119 | |
|
| cosine_accuracy@3 | 0.29 | |
|
| cosine_accuracy@5 | 0.3658 | |
|
| cosine_accuracy@10 | 0.4957 | |
|
| cosine_precision@1 | 0.119 | |
|
| cosine_precision@3 | 0.0967 | |
|
| cosine_precision@5 | 0.0732 | |
|
| cosine_precision@10 | 0.0496 | |
|
| cosine_recall@1 | 0.119 | |
|
| cosine_recall@3 | 0.29 | |
|
| cosine_recall@5 | 0.3658 | |
|
| cosine_recall@10 | 0.4957 | |
|
| cosine_ndcg@10 | 0.2892 | |
|
| cosine_mrr@10 | 0.2253 | |
|
| **cosine_map@100** | **0.2428** | |
|
|
|
#### 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 | 0.1082 | |
|
| cosine_accuracy@3 | 0.2662 | |
|
| cosine_accuracy@5 | 0.3636 | |
|
| cosine_accuracy@10 | 0.5065 | |
|
| cosine_precision@1 | 0.1082 | |
|
| cosine_precision@3 | 0.0887 | |
|
| cosine_precision@5 | 0.0727 | |
|
| cosine_precision@10 | 0.0506 | |
|
| cosine_recall@1 | 0.1082 | |
|
| cosine_recall@3 | 0.2662 | |
|
| cosine_recall@5 | 0.3636 | |
|
| cosine_recall@10 | 0.5065 | |
|
| cosine_ndcg@10 | 0.2839 | |
|
| cosine_mrr@10 | 0.2156 | |
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| **cosine_map@100** | **0.2323** | |
|
|
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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 | 0.1147 | |
|
| cosine_accuracy@3 | 0.2403 | |
|
| cosine_accuracy@5 | 0.3398 | |
|
| cosine_accuracy@10 | 0.4805 | |
|
| cosine_precision@1 | 0.1147 | |
|
| cosine_precision@3 | 0.0801 | |
|
| cosine_precision@5 | 0.068 | |
|
| cosine_precision@10 | 0.0481 | |
|
| cosine_recall@1 | 0.1147 | |
|
| cosine_recall@3 | 0.2403 | |
|
| cosine_recall@5 | 0.3398 | |
|
| cosine_recall@10 | 0.4805 | |
|
| cosine_ndcg@10 | 0.275 | |
|
| cosine_mrr@10 | 0.212 | |
|
| **cosine_map@100** | **0.2304** | |
|
|
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#### 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 | 0.1126 | |
|
| cosine_accuracy@3 | 0.2641 | |
|
| cosine_accuracy@5 | 0.329 | |
|
| cosine_accuracy@10 | 0.487 | |
|
| cosine_precision@1 | 0.1126 | |
|
| cosine_precision@3 | 0.088 | |
|
| cosine_precision@5 | 0.0658 | |
|
| cosine_precision@10 | 0.0487 | |
|
| cosine_recall@1 | 0.1126 | |
|
| cosine_recall@3 | 0.2641 | |
|
| cosine_recall@5 | 0.329 | |
|
| cosine_recall@10 | 0.487 | |
|
| cosine_ndcg@10 | 0.2791 | |
|
| cosine_mrr@10 | 0.2152 | |
|
| **cosine_map@100** | **0.234** | |
|
|
|
#### 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 | 0.1039 | |
|
| cosine_accuracy@3 | 0.2619 | |
|
| cosine_accuracy@5 | 0.3355 | |
|
| cosine_accuracy@10 | 0.474 | |
|
| cosine_precision@1 | 0.1039 | |
|
| cosine_precision@3 | 0.0873 | |
|
| cosine_precision@5 | 0.0671 | |
|
| cosine_precision@10 | 0.0474 | |
|
| cosine_recall@1 | 0.1039 | |
|
| cosine_recall@3 | 0.2619 | |
|
| cosine_recall@5 | 0.3355 | |
|
| cosine_recall@10 | 0.474 | |
|
| cosine_ndcg@10 | 0.27 | |
|
| cosine_mrr@10 | 0.2071 | |
|
| **cosine_map@100** | **0.2256** | |
|
|
|
<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 6,468 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: 5 tokens</li><li>mean: 39.4 tokens</li><li>max: 168 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.48 tokens</li><li>max: 44 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Aquest tràmit permet la inscripció al padró dels canvis de domicili dins de Sant Quirze del Vallès...</code> | <code>Quin és el benefici de la inscripció al Padró d'Habitants?</code> | |
|
| <code>Els recursos que es poden oferir al banc de recursos són: MATERIALS, PROFESSIONALS i SOCIALS.</code> | <code>Quins tipus de recursos es poden oferir al banc de recursos?</code> | |
|
| <code>El termini per a la presentació de sol·licituds serà del 8 al 21 de maig de 2024, ambdós inclosos.</code> | <code>Quin és el termini per a la presentació de sol·licituds per a la preinscripció a l'Escola Bressol Municipal El Patufet?</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, |
|
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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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
|
|
|
- `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 |
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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 |
|
|
|
#### 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 |
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- `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 | |
|
|:---------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.3951 | 10 | 4.4042 | - | - | - | - | - | - | |
|
| 0.7901 | 20 | 2.9471 | - | - | - | - | - | - | |
|
| 0.9877 | 25 | - | 0.2293 | 0.2045 | 0.2099 | 0.2138 | 0.1717 | 0.2242 | |
|
| 1.1852 | 30 | 2.2351 | - | - | - | - | - | - | |
|
| 1.5802 | 40 | 1.5289 | - | - | - | - | - | - | |
|
| 1.9753 | 50 | 1.2045 | 0.2332 | 0.2182 | 0.2277 | 0.2221 | 0.2051 | 0.2248 | |
|
| 2.3704 | 60 | 0.9435 | - | - | - | - | - | - | |
|
| 2.7654 | 70 | 0.7958 | - | - | - | - | - | - | |
|
| **2.963** | **75** | **-** | **0.2379** | **0.2352** | **0.2276** | **0.2204** | **0.2138** | **0.2235** | |
|
| 3.1605 | 80 | 0.6703 | - | - | - | - | - | - | |
|
| 3.5556 | 90 | 0.6162 | - | - | - | - | - | - | |
|
| 3.9506 | 100 | 0.6079 | - | - | - | - | - | - | |
|
| 3.9901 | 101 | - | 0.2251 | 0.2307 | 0.2201 | 0.2343 | 0.2210 | 0.2348 | |
|
| 4.3457 | 110 | 0.5085 | - | - | - | - | - | - | |
|
| 4.7407 | 120 | 0.5248 | - | - | - | - | - | - | |
|
| 4.9383 | 125 | - | 0.2287 | 0.2340 | 0.2304 | 0.2323 | 0.2256 | 0.2428 | |
|
|
|
* 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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## 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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