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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:5214
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Pel que fa als avals, la Junta de Govern Local en sessió celebrada
el 4 de juliol de 2006, va aprovar els models d'aval en funció del concepte a
garantir.
sentences:
- Quin és el benefici de la unitat de queixes i suggeriments per a la qualitat dels
serveis de l'Ajuntament de Sitges?
- Quin és el paper de la Junta de Govern Local?
- Quin és el propòsit més important del tràmit de canvi de titular de la llicència
de gual?
- source_sentence: Per a tenir dret a ésser inscrit en el Registre de Sol·licitants
d'Habitatge amb Protecció Oficial s'han de complir els procediments i els requisits
establerts per normativa.
sentences:
- Quin és el paper de la persona sol·licitant en la gestió de les fiances o dipòsits
d'una llicència d'obra?
- Quin és el benefici de complir els procediments i els requisits establerts per
normativa?
- Quin és el centre cultural que es troba a l'Escorxador de Sitges i ofereix activitats
culturals?
- source_sentence: Aquest tràmit permet comunicar a l'Ajuntament de Sitges la finalització
de les obres de nova construcció, o bé aquelles que hagin estat objecte de modificació
substancial o d’ampliació quan per a l’autorització de les obres s’hagi exigit
un projecte tècnic i a l’empara d’una llicència urbanística d’obra major.
sentences:
- Què passa si la modificació no té efectes sobre les persones o el medi ambient?
- Quin és el requisit principal per a la gestió diària d'una colònia felina?
- Quin és el paper del tràmit de comunicació prèvia de primera utilització i ocupació
d'edificis i instal·lacions en el procés d'obtenció de la llicència urbanística
d’obra major?
- source_sentence: Es tracta dels ajuts per a la realització de la Inspecció Tècnica
de l’Edifici (ITE) conjuntament amb l’elaboració dels certificats energètics.
sentences:
- Quins són els tipus de garanties que es poden ingressar?
- Quin és el procés d’elaboració dels certificats energètics?
- Quin és el paper de la consulta prèvia de classificació d'activitat en la tramitació
administrativa municipal?
- source_sentence: Les queixes, observacions i suggeriments són una eina important
per a millorar la qualitat dels serveis municipals.
sentences:
- Quin és el propòsit dels ajuts econòmics?
- Què és el que es busca amb les queixes, observacions i suggeriments?
- Qui són les persones beneficiàries de l'ajut per a la creació de noves empreses?
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: 0.14367088607594936
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2818565400843882
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3930379746835443
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5664556962025317
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14367088607594936
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09395218002812938
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07860759493670887
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05664556962025316
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14367088607594936
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2818565400843882
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3930379746835443
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5664556962025317
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.32426778614918705
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.25066212912731944
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2694799737895368
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.1470464135021097
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2871308016877637
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.390084388185654
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5630801687763713
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1470464135021097
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09571026722925456
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07801687763713079
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.056308016877637125
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1470464135021097
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2871308016877637
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.390084388185654
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5630801687763713
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.32549268557195893
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.25325421940928294
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.272264774489146
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.14177215189873418
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28375527426160335
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3890295358649789
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5620253164556962
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14177215189873418
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09458509142053445
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07780590717299578
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05620253164556962
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14177215189873418
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28375527426160335
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3890295358649789
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5620253164556962
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.322564230377663
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24968421405130298
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.26885741426647297
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.14345991561181434
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2831223628691983
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3850210970464135
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5550632911392405
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14345991561181434
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09437412095639944
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0770042194092827
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05550632911392406
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14345991561181434
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2831223628691983
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3850210970464135
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5550632911392405
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3205268083804564
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24917821981113142
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2685327848764784
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.13924050632911392
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2795358649789029
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3837552742616034
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5533755274261604
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.13924050632911392
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09317862165963431
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07675105485232067
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05533755274261602
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.13924050632911392
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2795358649789029
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3837552742616034
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5533755274261604
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31759054947613424
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2457681166700155
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2649300065982546
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.14029535864978904
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.27531645569620256
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.369831223628692
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5360759493670886
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14029535864978904
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09177215189873417
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0739662447257384
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.053607594936708865
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14029535864978904
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.27531645569620256
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.369831223628692
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5360759493670886
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3099216271465372
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24117783470631593
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2601649646918979
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-005-5ep")
# Run inference
sentences = [
'Les queixes, observacions i suggeriments són una eina important per a millorar la qualitat dels serveis municipals.',
'Què és el que es busca amb les queixes, observacions i suggeriments?',
'Quin és el propòsit dels ajuts econòmics?',
]
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 | 0.1437 |
| cosine_accuracy@3 | 0.2819 |
| cosine_accuracy@5 | 0.393 |
| cosine_accuracy@10 | 0.5665 |
| cosine_precision@1 | 0.1437 |
| cosine_precision@3 | 0.094 |
| cosine_precision@5 | 0.0786 |
| cosine_precision@10 | 0.0566 |
| cosine_recall@1 | 0.1437 |
| cosine_recall@3 | 0.2819 |
| cosine_recall@5 | 0.393 |
| cosine_recall@10 | 0.5665 |
| cosine_ndcg@10 | 0.3243 |
| cosine_mrr@10 | 0.2507 |
| **cosine_map@100** | **0.2695** |
#### 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 | 0.147 |
| cosine_accuracy@3 | 0.2871 |
| cosine_accuracy@5 | 0.3901 |
| cosine_accuracy@10 | 0.5631 |
| cosine_precision@1 | 0.147 |
| cosine_precision@3 | 0.0957 |
| cosine_precision@5 | 0.078 |
| cosine_precision@10 | 0.0563 |
| cosine_recall@1 | 0.147 |
| cosine_recall@3 | 0.2871 |
| cosine_recall@5 | 0.3901 |
| cosine_recall@10 | 0.5631 |
| cosine_ndcg@10 | 0.3255 |
| cosine_mrr@10 | 0.2533 |
| **cosine_map@100** | **0.2723** |
#### 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 | 0.1418 |
| cosine_accuracy@3 | 0.2838 |
| cosine_accuracy@5 | 0.389 |
| cosine_accuracy@10 | 0.562 |
| cosine_precision@1 | 0.1418 |
| cosine_precision@3 | 0.0946 |
| cosine_precision@5 | 0.0778 |
| cosine_precision@10 | 0.0562 |
| cosine_recall@1 | 0.1418 |
| cosine_recall@3 | 0.2838 |
| cosine_recall@5 | 0.389 |
| cosine_recall@10 | 0.562 |
| cosine_ndcg@10 | 0.3226 |
| cosine_mrr@10 | 0.2497 |
| **cosine_map@100** | **0.2689** |
#### 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 | 0.1435 |
| cosine_accuracy@3 | 0.2831 |
| cosine_accuracy@5 | 0.385 |
| cosine_accuracy@10 | 0.5551 |
| cosine_precision@1 | 0.1435 |
| cosine_precision@3 | 0.0944 |
| cosine_precision@5 | 0.077 |
| cosine_precision@10 | 0.0555 |
| cosine_recall@1 | 0.1435 |
| cosine_recall@3 | 0.2831 |
| cosine_recall@5 | 0.385 |
| cosine_recall@10 | 0.5551 |
| cosine_ndcg@10 | 0.3205 |
| cosine_mrr@10 | 0.2492 |
| **cosine_map@100** | **0.2685** |
#### 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 | 0.1392 |
| cosine_accuracy@3 | 0.2795 |
| cosine_accuracy@5 | 0.3838 |
| cosine_accuracy@10 | 0.5534 |
| cosine_precision@1 | 0.1392 |
| cosine_precision@3 | 0.0932 |
| cosine_precision@5 | 0.0768 |
| cosine_precision@10 | 0.0553 |
| cosine_recall@1 | 0.1392 |
| cosine_recall@3 | 0.2795 |
| cosine_recall@5 | 0.3838 |
| cosine_recall@10 | 0.5534 |
| cosine_ndcg@10 | 0.3176 |
| cosine_mrr@10 | 0.2458 |
| **cosine_map@100** | **0.2649** |
#### 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 | 0.1403 |
| cosine_accuracy@3 | 0.2753 |
| cosine_accuracy@5 | 0.3698 |
| cosine_accuracy@10 | 0.5361 |
| cosine_precision@1 | 0.1403 |
| cosine_precision@3 | 0.0918 |
| cosine_precision@5 | 0.074 |
| cosine_precision@10 | 0.0536 |
| cosine_recall@1 | 0.1403 |
| cosine_recall@3 | 0.2753 |
| cosine_recall@5 | 0.3698 |
| cosine_recall@10 | 0.5361 |
| cosine_ndcg@10 | 0.3099 |
| cosine_mrr@10 | 0.2412 |
| **cosine_map@100** | **0.2602** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 5,214 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 49.66 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.85 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| <code>Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19)</code> | <code>Quin és el requisit per a les petites empreses per rebre ajuts?</code> |
| <code>En cas de no poder desenvolupar el projecte o activitat per la qual s'ha sol·licitat la subvenció, l'entitat beneficiària pot renunciar a la subvenció.</code> | <code>Puc renunciar a una subvenció si ja l'he rebut?</code> |
| <code>L’Espai Jove de Sitges és l'equipament municipal on els joves poden dur a terme iniciatives pròpies i on també es desenvolupen d’altres impulsades per la regidoria de Joventut.</code> | <code>Quin és el paper de la regidoria de Joventut a l'Espai Jove de Sitges?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4908 | 10 | 3.3699 | - | - | - | - | - | - |
| 0.9816 | 20 | 1.8761 | 0.2565 | 0.2430 | 0.2509 | 0.2499 | 0.2301 | 0.2567 |
| 1.4724 | 30 | 1.3111 | - | - | - | - | - | - |
| 1.9632 | 40 | 0.8122 | 0.2636 | 0.2578 | 0.2629 | 0.2639 | 0.2486 | 0.2654 |
| 2.4540 | 50 | 0.5903 | - | - | - | - | - | - |
| 2.9448 | 60 | 0.4306 | - | - | - | - | - | - |
| **2.9939** | **61** | **-** | **0.2661** | **0.2636** | **0.2648** | **0.2659** | **0.2544** | **0.2694** |
| 3.4356 | 70 | 0.3553 | - | - | - | - | - | - |
| 3.9264 | 80 | 0.2925 | - | - | - | - | - | - |
| 3.9755 | 81 | - | 0.2701 | 0.2621 | 0.2663 | 0.2706 | 0.2602 | 0.2709 |
| 4.4172 | 90 | 0.2797 | - | - | - | - | - | - |
| 4.9080 | 100 | 0.267 | 0.2695 | 0.2649 | 0.2685 | 0.2689 | 0.2602 | 0.2723 |
* 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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