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---
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:6399
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Instal·lació de tendals.
sentences:
- Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit?
- Quin és el període en què es produeix la comunicació de tancament puntual d’una
activitat?
- Quin és el benefici del volant històric de convivència?
- source_sentence: Ajuts econòmics destinats a reforçar les activitats econòmiques
amb suspensió o limitació d’obertura al públic i per finançar les despeses de
lloguer o hipoteca per empreses i/o establiments comercials
sentences:
- Quin és el tràmit per a realitzar una obra que canvia la distribució d’un local
comercial?
- Quan cal sol·licitar l'informe previ en matèria d'incendis?
- Quin és el benefici dels ajuts econòmics per als treballadors?
- source_sentence: L'Ajuntament concedirà als empleats municipals que tinguin al seu
càrrec familiars amb discapacitat física, psíquica o sensorial, un ajut especial
que es reportarà mensualment segons el grau de discapacitat.
sentences:
- Quin és el benefici que es reporta mensualment?
- Quin és el resultat de la comunicació de canvi de titularitat a l'Ajuntament?
- Quin és el requisit per renovar la inscripció en el Registre municipal de sol·licitants
d'habitatge amb protecció oficial de Sitges?
- source_sentence: El volant històric de convivència és el document que informa de
la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament
d'una persona, i detalla tots els domicilis, la data inicial i final en els que
ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites,
segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició.
sentences:
- Quin és el límit de potència instal·lada per a les instal·lacions de plaques solars
en sòl urbà?
- Quin és el contingut del Padró Municipal d'Habitants?
- Quin és el resultat esperat de la gestió de les colònies felines?
- source_sentence: Els comerços locals obtenen un benefici principal de la implementació
del projecte d'implantació i ús de la targeta de fidelització del comerç local
de Sitges, que és la possibilitat d'augmentar les vendes i la fidelització dels
clients.
sentences:
- Quin és el benefici que els comerços locals obtenen de la implementació del projecte
d'implantació i ús de la targeta de fidelització?
- Quin és el pla d'ordenació urbanística municipal que regula l'ús d'habitatges
d'ús turístic de Sitges?
- Quin és el propòsit de la deixalleria municipal per a l’ambient?
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: 0.13305203938115331
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26244725738396624
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.35358649789029534
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5243319268635724
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.13305203938115331
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08748241912798875
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07071729957805907
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05243319268635724
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.13305203938115331
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26244725738396624
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.35358649789029534
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5243319268635724
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2985567963545146
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.23013316812894896
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2512708543031996
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.13220815752461323
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2630098452883263
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3541490857946554
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5285513361462728
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.13220815752461323
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08766994842944209
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07082981715893108
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05285513361462728
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.13220815752461323
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2630098452883263
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3541490857946554
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5285513361462728
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.30111353887210784
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2321642890630236
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2529696660722769
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.1341772151898734
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26554149085794654
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3589310829817159
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5257383966244725
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1341772151898734
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08851383028598217
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07178621659634317
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05257383966244726
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1341772151898734
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26554149085794654
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3589310829817159
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5257383966244725
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3010502512929789
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.23285647310963767
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.25376075028724965
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.12658227848101267
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26329113924050634
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3563994374120956
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5229254571026722
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12658227848101267
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08776371308016878
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07127988748241912
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05229254571026722
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12658227848101267
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26329113924050634
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3563994374120956
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5229254571026722
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2971826978005507
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22852298350188655
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24963995627964844
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.12742616033755275
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2683544303797468
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.35527426160337555
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5209563994374121
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12742616033755275
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08945147679324894
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0710548523206751
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05209563994374121
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12742616033755275
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2683544303797468
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.35527426160337555
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5209563994374121
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2973178953118737
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22926059875426977
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2507076323664793
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.12236286919831224
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2545710267229255
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3440225035161744
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5164556962025316
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12236286919831224
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0848570089076418
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06880450070323489
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05164556962025317
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12236286919831224
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2545710267229255
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3440225035161744
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5164556962025316
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.29092273297262244
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22250820440693853
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2429016668571107
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-sitges-003-10ep")
# Run inference
sentences = [
"Els comerços locals obtenen un benefici principal de la implementació del projecte d'implantació i ús de la targeta de fidelització del comerç local de Sitges, que és la possibilitat d'augmentar les vendes i la fidelització dels clients.",
"Quin és el benefici que els comerços locals obtenen de la implementació del projecte d'implantació i ús de la targeta de fidelització?",
'Quin és el propòsit de la deixalleria municipal per a l’ambient?',
]
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 | 0.1331 |
| cosine_accuracy@3 | 0.2624 |
| cosine_accuracy@5 | 0.3536 |
| cosine_accuracy@10 | 0.5243 |
| cosine_precision@1 | 0.1331 |
| cosine_precision@3 | 0.0875 |
| cosine_precision@5 | 0.0707 |
| cosine_precision@10 | 0.0524 |
| cosine_recall@1 | 0.1331 |
| cosine_recall@3 | 0.2624 |
| cosine_recall@5 | 0.3536 |
| cosine_recall@10 | 0.5243 |
| cosine_ndcg@10 | 0.2986 |
| cosine_mrr@10 | 0.2301 |
| **cosine_map@100** | **0.2513** |
#### 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 | 0.1322 |
| cosine_accuracy@3 | 0.263 |
| cosine_accuracy@5 | 0.3541 |
| cosine_accuracy@10 | 0.5286 |
| cosine_precision@1 | 0.1322 |
| cosine_precision@3 | 0.0877 |
| cosine_precision@5 | 0.0708 |
| cosine_precision@10 | 0.0529 |
| cosine_recall@1 | 0.1322 |
| cosine_recall@3 | 0.263 |
| cosine_recall@5 | 0.3541 |
| cosine_recall@10 | 0.5286 |
| cosine_ndcg@10 | 0.3011 |
| cosine_mrr@10 | 0.2322 |
| **cosine_map@100** | **0.253** |
#### 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 | 0.1342 |
| cosine_accuracy@3 | 0.2655 |
| cosine_accuracy@5 | 0.3589 |
| cosine_accuracy@10 | 0.5257 |
| cosine_precision@1 | 0.1342 |
| cosine_precision@3 | 0.0885 |
| cosine_precision@5 | 0.0718 |
| cosine_precision@10 | 0.0526 |
| cosine_recall@1 | 0.1342 |
| cosine_recall@3 | 0.2655 |
| cosine_recall@5 | 0.3589 |
| cosine_recall@10 | 0.5257 |
| cosine_ndcg@10 | 0.3011 |
| cosine_mrr@10 | 0.2329 |
| **cosine_map@100** | **0.2538** |
#### 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 | 0.1266 |
| cosine_accuracy@3 | 0.2633 |
| cosine_accuracy@5 | 0.3564 |
| cosine_accuracy@10 | 0.5229 |
| cosine_precision@1 | 0.1266 |
| cosine_precision@3 | 0.0878 |
| cosine_precision@5 | 0.0713 |
| cosine_precision@10 | 0.0523 |
| cosine_recall@1 | 0.1266 |
| cosine_recall@3 | 0.2633 |
| cosine_recall@5 | 0.3564 |
| cosine_recall@10 | 0.5229 |
| cosine_ndcg@10 | 0.2972 |
| cosine_mrr@10 | 0.2285 |
| **cosine_map@100** | **0.2496** |
#### 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 | 0.1274 |
| cosine_accuracy@3 | 0.2684 |
| cosine_accuracy@5 | 0.3553 |
| cosine_accuracy@10 | 0.521 |
| cosine_precision@1 | 0.1274 |
| cosine_precision@3 | 0.0895 |
| cosine_precision@5 | 0.0711 |
| cosine_precision@10 | 0.0521 |
| cosine_recall@1 | 0.1274 |
| cosine_recall@3 | 0.2684 |
| cosine_recall@5 | 0.3553 |
| cosine_recall@10 | 0.521 |
| cosine_ndcg@10 | 0.2973 |
| cosine_mrr@10 | 0.2293 |
| **cosine_map@100** | **0.2507** |
#### 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 | 0.1224 |
| cosine_accuracy@3 | 0.2546 |
| cosine_accuracy@5 | 0.344 |
| cosine_accuracy@10 | 0.5165 |
| cosine_precision@1 | 0.1224 |
| cosine_precision@3 | 0.0849 |
| cosine_precision@5 | 0.0688 |
| cosine_precision@10 | 0.0516 |
| cosine_recall@1 | 0.1224 |
| cosine_recall@3 | 0.2546 |
| cosine_recall@5 | 0.344 |
| cosine_recall@10 | 0.5165 |
| cosine_ndcg@10 | 0.2909 |
| cosine_mrr@10 | 0.2225 |
| **cosine_map@100** | **0.2429** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,399 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: 9 tokens</li><li>mean: 49.44 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.17 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges.</code> | <code>Quin és el benefici de les subvencions per a les entitats esportives?</code> |
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el període d'execució dels projectes i activitats esportives?</code> |
| <code>Certificat on s'indica el nombre d'habitatges que configuren el padró de l'Impost sobre Béns Immobles del municipi o bé d'una part d'aquest.</code> | <code>Quin és el contingut del certificat del nombre d'habitatges?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:--------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4 | 10 | 3.5464 | - | - | - | - | - | - |
| 0.8 | 20 | 2.3861 | - | - | - | - | - | - |
| 1.0 | 25 | - | 0.2327 | 0.2144 | 0.2252 | 0.2286 | 0.1938 | 0.2329 |
| 1.1975 | 30 | 1.8712 | - | - | - | - | - | - |
| 1.5975 | 40 | 1.3322 | - | - | - | - | - | - |
| 1.9975 | 50 | 0.9412 | 0.2410 | 0.2310 | 0.2383 | 0.2415 | 0.2236 | 0.2436 |
| 2.395 | 60 | 0.806 | - | - | - | - | - | - |
| 2.795 | 70 | 0.5024 | - | - | - | - | - | - |
| 2.995 | 75 | - | 0.2451 | 0.2384 | 0.2455 | 0.2487 | 0.2323 | 0.2423 |
| 3.1925 | 80 | 0.4259 | - | - | - | - | - | - |
| 3.5925 | 90 | 0.3556 | - | - | - | - | - | - |
| 3.9925 | 100 | 0.2555 | 0.2477 | 0.2443 | 0.2417 | 0.2485 | 0.2369 | 0.2470 |
| 4.39 | 110 | 0.2611 | - | - | - | - | - | - |
| 4.79 | 120 | 0.1939 | - | - | - | - | - | - |
| 4.99 | 125 | - | 0.2490 | 0.2425 | 0.2479 | 0.2485 | 0.2386 | 0.2495 |
| 5.1875 | 130 | 0.2021 | - | - | - | - | - | - |
| 5.5875 | 140 | 0.1537 | - | - | - | - | - | - |
| 5.9875 | 150 | 0.1277 | 0.2535 | 0.2491 | 0.2491 | 0.2534 | 0.2403 | 0.2541 |
| 6.385 | 160 | 0.1213 | - | - | - | - | - | - |
| 6.785 | 170 | 0.1035 | - | - | - | - | - | - |
| 6.985 | 175 | - | 0.2513 | 0.2493 | 0.2435 | 0.2515 | 0.2380 | 0.2528 |
| 7.1825 | 180 | 0.0965 | - | - | - | - | - | - |
| 7.5825 | 190 | 0.0861 | - | - | - | - | - | - |
| 7.9825 | 200 | 0.0794 | 0.2529 | 0.2536 | 0.2526 | 0.2545 | 0.2438 | 0.2570 |
| 8.38 | 210 | 0.0734 | - | - | - | - | - | - |
| 8.78 | 220 | 0.066 | - | - | - | - | - | - |
| **8.98** | **225** | **-** | **0.2538** | **0.2523** | **0.2519** | **0.2542** | **0.2457** | **0.2572** |
| 9.1775 | 230 | 0.0731 | - | - | - | - | - | - |
| 9.5775 | 240 | 0.0726 | - | - | - | - | - | - |
| 9.9775 | 250 | 0.0632 | 0.2513 | 0.2507 | 0.2496 | 0.2538 | 0.2429 | 0.2530 |
* 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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