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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:8769
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Aquelles persones que fan un ús regular i continuat de la deixalleria
municipal poden gaudir d’una bonificació del 20% sobre la quota de les taxes per
recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris.
sentences:
- Quin és el contingut dels documents dirigits a l'Ajuntament de Sitges?
- Quin és el benefici de la deixalleria municipal?
- Quin és el mètode de pagament dels ajuts atorgats en cas de normalitat?
- source_sentence: Les subvencions per al desenvolupament i/o consolidació de sectors
econòmics del municipi tenen com a objectiu generar un benefici ambiental per
al municipi, a través de la promoció de pràctiques sostenibles.
sentences:
- Quin és el requisit per a la llicència per a la modificació d'un règim de propietat
horitzontal?
- Quin és el benefici ambiental esperat de les subvencions per al desenvolupament
i/o consolidació de sectors econòmics del municipi?
- Quin és el propòsit de la liquidació de l'import corresponent a l'exercici?
- source_sentence: Aquelles persones que s'hagin inscrit a les estades esportives
organitzades per l'Ajuntament de Sitges i que formin part d'una unitat familiar
amb uns ingressos bruts mensuals, que una vegada dividits pel nombre de membres,
siguin inferiors entre una i dues terceres parts de l'IPREM, poden sol·licitar
una reducció de la quota d'aquestes activitats o l'aplicació de la corresponent
tarifa bonificada establerta en les ordenances dels preus públics.
sentences:
- Quin és el benefici de les subvencions per a les entitats culturals?
- Quin és el paper de l'IPREM en la sol·licitud de reducció de la quota d'una estada
esportiva?
- Quin és el paper de l'Ajuntament en la resolució d'una situació sanitària no adequada
en un domini particular?
- source_sentence: La inscripció al cens municipal facilita la recuperació d’aquests
animals en cas de pèrdua alhora que permet a l’Ajuntament disposar de les dades
necessàries en cas que s’hagin de realitzar campanyes sanitàries.
sentences:
- Quin és el tipus de serveis auxiliars que es consideren despeses elegibles?
- Quin és el benefici d'estacionar a les zones verdes per als residents?
- Quin és el motiu pel qual es crea el cens municipal d’animals de companyia?
- source_sentence: A la nostra vila hi ha veïns i veïnes que els agradaria tornar
a fer de pagès o provar-ho per primera vegada.
sentences:
- Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?
- Quin és el propòsit del carnet de conductor de taxi?
- Quin és el paper de les persones en relació amb les indemnitzacions?
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: 0.11054852320675106
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2270042194092827
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.30548523206751055
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4531645569620253
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11054852320675106
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07566807313642755
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06109704641350212
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04531645569620253
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11054852320675106
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2270042194092827
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30548523206751055
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4531645569620253
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25622764604771076
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1965350612818966
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21859411055862238
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.11561181434599156
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2320675105485232
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.31139240506329113
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.44556962025316454
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11561181434599156
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07735583684950773
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06227848101265824
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.044556962025316456
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11561181434599156
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2320675105485232
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.31139240506329113
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.44556962025316454
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2579660315889156
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20086732301922164
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22344331787470567
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.10379746835443038
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2210970464135021
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2970464135021097
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.43966244725738396
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10379746835443038
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07369901547116735
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05940928270042194
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.043966244725738395
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10379746835443038
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2210970464135021
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2970464135021097
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.43966244725738396
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2473619714740055
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.18892840399169497
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21182552044674802
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.10042194092827005
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21518987341772153
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2978902953586498
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4438818565400844
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10042194092827005
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07172995780590716
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05957805907172995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04438818565400844
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10042194092827005
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21518987341772153
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2978902953586498
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4438818565400844
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2479637375723138
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.18831156653941447
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21130848497160895
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.10886075949367088
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22616033755274262
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3029535864978903
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4413502109704641
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10886075949367088
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07538677918424753
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.060590717299578066
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04413502109704641
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10886075949367088
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22616033755274262
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3029535864978903
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4413502109704641
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25366131313332974
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.19639441430580665
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2187767008895725
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.09367088607594937
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2742616033755274
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4177215189873418
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.09367088607594937
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05485232067510549
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04177215189873418
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09367088607594937
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2742616033755274
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4177215189873418
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.23046340016141767
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1738279418659165
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.19782551958501599
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-003-5ep")
# Run inference
sentences = [
'A la nostra vila hi ha veïns i veïnes que els agradaria tornar a fer de pagès o provar-ho per primera vegada.',
"Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?",
'Quin és el paper de les persones en relació amb les indemnitzacions?',
]
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 | 0.1105 |
| cosine_accuracy@3 | 0.227 |
| cosine_accuracy@5 | 0.3055 |
| cosine_accuracy@10 | 0.4532 |
| cosine_precision@1 | 0.1105 |
| cosine_precision@3 | 0.0757 |
| cosine_precision@5 | 0.0611 |
| cosine_precision@10 | 0.0453 |
| cosine_recall@1 | 0.1105 |
| cosine_recall@3 | 0.227 |
| cosine_recall@5 | 0.3055 |
| cosine_recall@10 | 0.4532 |
| cosine_ndcg@10 | 0.2562 |
| cosine_mrr@10 | 0.1965 |
| **cosine_map@100** | **0.2186** |
#### 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 | 0.1156 |
| cosine_accuracy@3 | 0.2321 |
| cosine_accuracy@5 | 0.3114 |
| cosine_accuracy@10 | 0.4456 |
| cosine_precision@1 | 0.1156 |
| cosine_precision@3 | 0.0774 |
| cosine_precision@5 | 0.0623 |
| cosine_precision@10 | 0.0446 |
| cosine_recall@1 | 0.1156 |
| cosine_recall@3 | 0.2321 |
| cosine_recall@5 | 0.3114 |
| cosine_recall@10 | 0.4456 |
| cosine_ndcg@10 | 0.258 |
| cosine_mrr@10 | 0.2009 |
| **cosine_map@100** | **0.2234** |
#### 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 | 0.1038 |
| cosine_accuracy@3 | 0.2211 |
| cosine_accuracy@5 | 0.297 |
| cosine_accuracy@10 | 0.4397 |
| cosine_precision@1 | 0.1038 |
| cosine_precision@3 | 0.0737 |
| cosine_precision@5 | 0.0594 |
| cosine_precision@10 | 0.044 |
| cosine_recall@1 | 0.1038 |
| cosine_recall@3 | 0.2211 |
| cosine_recall@5 | 0.297 |
| cosine_recall@10 | 0.4397 |
| cosine_ndcg@10 | 0.2474 |
| cosine_mrr@10 | 0.1889 |
| **cosine_map@100** | **0.2118** |
#### 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 | 0.1004 |
| cosine_accuracy@3 | 0.2152 |
| cosine_accuracy@5 | 0.2979 |
| cosine_accuracy@10 | 0.4439 |
| cosine_precision@1 | 0.1004 |
| cosine_precision@3 | 0.0717 |
| cosine_precision@5 | 0.0596 |
| cosine_precision@10 | 0.0444 |
| cosine_recall@1 | 0.1004 |
| cosine_recall@3 | 0.2152 |
| cosine_recall@5 | 0.2979 |
| cosine_recall@10 | 0.4439 |
| cosine_ndcg@10 | 0.248 |
| cosine_mrr@10 | 0.1883 |
| **cosine_map@100** | **0.2113** |
#### 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 | 0.1089 |
| cosine_accuracy@3 | 0.2262 |
| cosine_accuracy@5 | 0.303 |
| cosine_accuracy@10 | 0.4414 |
| cosine_precision@1 | 0.1089 |
| cosine_precision@3 | 0.0754 |
| cosine_precision@5 | 0.0606 |
| cosine_precision@10 | 0.0441 |
| cosine_recall@1 | 0.1089 |
| cosine_recall@3 | 0.2262 |
| cosine_recall@5 | 0.303 |
| cosine_recall@10 | 0.4414 |
| cosine_ndcg@10 | 0.2537 |
| cosine_mrr@10 | 0.1964 |
| **cosine_map@100** | **0.2188** |
#### 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 | 0.0937 |
| cosine_accuracy@3 | 0.2 |
| cosine_accuracy@5 | 0.2743 |
| cosine_accuracy@10 | 0.4177 |
| cosine_precision@1 | 0.0937 |
| cosine_precision@3 | 0.0667 |
| cosine_precision@5 | 0.0549 |
| cosine_precision@10 | 0.0418 |
| cosine_recall@1 | 0.0937 |
| cosine_recall@3 | 0.2 |
| cosine_recall@5 | 0.2743 |
| cosine_recall@10 | 0.4177 |
| cosine_ndcg@10 | 0.2305 |
| cosine_mrr@10 | 0.1738 |
| **cosine_map@100** | **0.1978** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 8,769 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: 49.22 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 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`: 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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2914 | 10 | 3.6318 | - | - | - | - | - | - |
| 0.5829 | 20 | 2.329 | - | - | - | - | - | - |
| 0.8743 | 30 | 1.5614 | - | - | - | - | - | - |
| 0.9909 | 34 | - | 0.2055 | 0.1998 | 0.2020 | 0.2001 | 0.1903 | 0.2019 |
| 1.1658 | 40 | 1.2383 | - | - | - | - | - | - |
| 1.4572 | 50 | 0.9323 | - | - | - | - | - | - |
| 1.7486 | 60 | 0.6616 | - | - | - | - | - | - |
| 1.9818 | 68 | - | 0.2244 | 0.2063 | 0.2223 | 0.2166 | 0.2011 | 0.2235 |
| 2.0401 | 70 | 0.5545 | - | - | - | - | - | - |
| 2.3315 | 80 | 0.5043 | - | - | - | - | - | - |
| 2.6230 | 90 | 0.3542 | - | - | - | - | - | - |
| 2.9144 | 100 | 0.3095 | - | - | - | - | - | - |
| 2.9727 | 102 | - | 0.2224 | 0.2046 | 0.2170 | 0.2100 | 0.1986 | 0.2144 |
| 3.2058 | 110 | 0.2863 | - | - | - | - | - | - |
| 3.4973 | 120 | 0.2329 | - | - | - | - | - | - |
| 3.7887 | 130 | 0.2353 | - | - | - | - | - | - |
| 3.9927 | 137 | - | 0.2197 | 0.2112 | 0.2098 | 0.2154 | 0.1949 | 0.2178 |
| 4.0801 | 140 | 0.1759 | - | - | - | - | - | - |
| 4.3716 | 150 | 0.2308 | - | - | - | - | - | - |
| 4.6630 | 160 | 0.1656 | - | - | - | - | - | - |
| **4.9545** | **170** | **0.1812** | **0.2186** | **0.2188** | **0.2113** | **0.2118** | **0.1978** | **0.2234** |
* 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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