---
base_model: PlanTL-GOB-ES/roberta-base-bne
datasets: []
language: []
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:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: L'Ajuntament de Sitges ofereix a aquelles famílies que acompleixin
    els requisits establerts, ajuts per al pagament de la quota del servei i de la
    quota del menjador dels infants matriculats a les Llars d'Infants Municipals (
    0-3 anys).
  sentences:
  - Quin és l'objectiu principal de les subvencions per a projectes i activitats de
    l'àmbit turístic?
  - Quin és el procediment per a obtenir una llicència per a disposar d'una parada
    en un mercat setmanal?
  - Quin és el paper de l'Ajuntament de Sitges en la quota del menjador de les Llars
    d'Infants Municipals?
- source_sentence: Es tracta de la sol·licitud de permís municipal per poder utilitzar
    de forma privativa una zona de la via pública per instal·lacions d’atraccions
    i venda en fires, amb independència de les possibles afectacions a la via pública...
  sentences:
  - Quin és el tipus de permís que es sol·licita?
  - Quin és el paper de l'Ajuntament en aquest tràmit?
  - Quin és el resultat de la llicència per a la constitució d'un règim de propietat
    horitzontal en relació amb l’escriptura de divisió horitzontal?
- source_sentence: Totes les persones que resideixen a Espanya estan obligades a inscriure's
    en el padró del municipi en el qual resideixen habitualment.
  sentences:
  - Quin és el benefici de l'ajut extraordinari per a la família de l'empleat?
  - Què passa si no es presenta la sol·licitud d'acceptació en el termini establert?
  - Qui està obligat a inscriure's en el Padró Municipal d'Habitants?
- source_sentence: Les persones i entitats beneficiaries hauran de justificar la realització
    del projecte/activitat subvencionada com a màxim el dia 31 de març de 2023.
  sentences:
  - Quin és el termini per presentar la justificació de la realització del projecte/activitat
    subvencionada?
  - Quin és el període durant el qual es poden sol·licitar els ajuts?
  - Quin és el registre on s'inscriuen les entitats d’interès ciutadà de Sitges?
- source_sentence: Els establiments locals tenen un paper clau en el projecte de la
    targeta de fidelització, ja que són els que ofereixen descomptes i ofertes especials
    als consumidors que utilitzen la targeta.
  sentences:
  - Quin és el paper dels establiments locals en el projecte de la targeta de fidelització?
  - Quin és el paper de la via pública en aquest tràmit?
  - Quins són els tractaments que beneficien la salut de l'empleat municipal que s'inclouen
    en l'ajuda?
model-index:
- name: SentenceTransformer based on PlanTL-GOB-ES/roberta-base-bne
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.05172413793103448
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.125
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1875
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3857758620689655
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05172413793103448
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.041666666666666664
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.037500000000000006
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.038577586206896546
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05172413793103448
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.125
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1875
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3857758620689655
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18210165785971896
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.12195368089764656
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14624173028144724
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.04525862068965517
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11422413793103449
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1810344827586207
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.38146551724137934
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04525862068965517
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.038074712643678156
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03620689655172414
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03814655172413794
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04525862068965517
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11422413793103449
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1810344827586207
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.38146551724137934
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17527880528414544
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1144422208538589
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1390144478189839
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.04741379310344827
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.12284482758620689
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.20043103448275862
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.39870689655172414
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04741379310344827
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.040948275862068964
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04008620689655173
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03987068965517242
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04741379310344827
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12284482758620689
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.20043103448275862
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.39870689655172414
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18506006244346174
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.12174072933771223
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1456718436547049
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.04525862068965517
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11637931034482758
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.16810344827586207
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.38146551724137934
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04525862068965517
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03879310344827586
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03362068965517242
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03814655172413794
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04525862068965517
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11637931034482758
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.16810344827586207
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.38146551724137934
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17534824322616613
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11455682129173515
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.139297198421225
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.03879310344827586
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.09698275862068965
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.15301724137931033
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34698275862068967
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.03879310344827586
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03232758620689655
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.030603448275862068
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.034698275862068965
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03879310344827586
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.09698275862068965
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.15301724137931033
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34698275862068967
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1568993526090112
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.10108699370552827
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1268371950835345
      name: Cosine Map@100
---

# SentenceTransformer based on PlanTL-GOB-ES/roberta-base-bne

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne). It maps sentences & paragraphs to a 768-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:** [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) <!-- at revision 0e598176534f3cf2e30105f8286cf2503d6e4731 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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/sitges10242608-4ep-rerankv3-sp")
# Run inference
sentences = [
    'Els establiments locals tenen un paper clau en el projecte de la targeta de fidelització, ja que són els que ofereixen descomptes i ofertes especials als consumidors que utilitzen la targeta.',
    'Quin és el paper dels establiments locals en el projecte de la targeta de fidelització?',
    "Quins són els tractaments que beneficien la salut de l'empleat municipal que s'inclouen en l'ajuda?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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_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   | 0.0517     |
| cosine_accuracy@3   | 0.125      |
| cosine_accuracy@5   | 0.1875     |
| cosine_accuracy@10  | 0.3858     |
| cosine_precision@1  | 0.0517     |
| cosine_precision@3  | 0.0417     |
| cosine_precision@5  | 0.0375     |
| cosine_precision@10 | 0.0386     |
| cosine_recall@1     | 0.0517     |
| cosine_recall@3     | 0.125      |
| cosine_recall@5     | 0.1875     |
| cosine_recall@10    | 0.3858     |
| cosine_ndcg@10      | 0.1821     |
| cosine_mrr@10       | 0.122      |
| **cosine_map@100**  | **0.1462** |

#### 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   | 0.0453    |
| cosine_accuracy@3   | 0.1142    |
| cosine_accuracy@5   | 0.181     |
| cosine_accuracy@10  | 0.3815    |
| cosine_precision@1  | 0.0453    |
| cosine_precision@3  | 0.0381    |
| cosine_precision@5  | 0.0362    |
| cosine_precision@10 | 0.0381    |
| cosine_recall@1     | 0.0453    |
| cosine_recall@3     | 0.1142    |
| cosine_recall@5     | 0.181     |
| cosine_recall@10    | 0.3815    |
| cosine_ndcg@10      | 0.1753    |
| cosine_mrr@10       | 0.1144    |
| **cosine_map@100**  | **0.139** |

#### 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   | 0.0474     |
| cosine_accuracy@3   | 0.1228     |
| cosine_accuracy@5   | 0.2004     |
| cosine_accuracy@10  | 0.3987     |
| cosine_precision@1  | 0.0474     |
| cosine_precision@3  | 0.0409     |
| cosine_precision@5  | 0.0401     |
| cosine_precision@10 | 0.0399     |
| cosine_recall@1     | 0.0474     |
| cosine_recall@3     | 0.1228     |
| cosine_recall@5     | 0.2004     |
| cosine_recall@10    | 0.3987     |
| cosine_ndcg@10      | 0.1851     |
| cosine_mrr@10       | 0.1217     |
| **cosine_map@100**  | **0.1457** |

#### 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   | 0.0453     |
| cosine_accuracy@3   | 0.1164     |
| cosine_accuracy@5   | 0.1681     |
| cosine_accuracy@10  | 0.3815     |
| cosine_precision@1  | 0.0453     |
| cosine_precision@3  | 0.0388     |
| cosine_precision@5  | 0.0336     |
| cosine_precision@10 | 0.0381     |
| cosine_recall@1     | 0.0453     |
| cosine_recall@3     | 0.1164     |
| cosine_recall@5     | 0.1681     |
| cosine_recall@10    | 0.3815     |
| cosine_ndcg@10      | 0.1753     |
| cosine_mrr@10       | 0.1146     |
| **cosine_map@100**  | **0.1393** |

#### 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   | 0.0388     |
| cosine_accuracy@3   | 0.097      |
| cosine_accuracy@5   | 0.153      |
| cosine_accuracy@10  | 0.347      |
| cosine_precision@1  | 0.0388     |
| cosine_precision@3  | 0.0323     |
| cosine_precision@5  | 0.0306     |
| cosine_precision@10 | 0.0347     |
| cosine_recall@1     | 0.0388     |
| cosine_recall@3     | 0.097      |
| cosine_recall@5     | 0.153      |
| cosine_recall@10    | 0.347      |
| cosine_ndcg@10      | 0.1569     |
| cosine_mrr@10       | 0.1011     |
| **cosine_map@100**  | **0.1268** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 4,173 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: 10 tokens</li><li>mean: 60.84 tokens</li><li>max: 206 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 25.34 tokens</li><li>max: 53 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                        | anchor                                                                                                                    |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
  | <code>L'objectiu principal de la persona coordinadora de colònia felina és garantir el benestar dels animals de la colònia.</code>                                                              | <code>Quin és l'objectiu principal de la persona coordinadora de colònia felina?</code>                                   |
  | <code>Es tracta d'una sala amb capacitat per a 125 persones, equipada amb un petit escenari, sistema de sonorització, pantalla per a projeccions, camerins i serveis higiènics (WC).</code>     | <code>Quin és el nombre de persones que pot acollir la sala d'actes del Casal Municipal de la Gent Gran de Sitges?</code> |
  | <code>Aquest ajut pretén fomentar l’associacionisme empresarial local, per tal de disposar d’agrupacions, gremis o associacions representatives de l’activitat empresarial del municipi.</code> | <code>Quin és el paper de les empreses en aquest ajut?</code>                                                             |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          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
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: False
- `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
- `learning_rate`: 5e-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`: 4
- `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`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6130     | 10     | 10.8464       | -                      | -                      | -                      | -                     | -                      |
| 0.9808     | 16     | -             | 0.1060                 | 0.1088                 | 0.1067                 | 0.0984                | 0.1074                 |
| 1.2261     | 20     | 3.5261        | -                      | -                      | -                      | -                     | -                      |
| 1.8391     | 30     | 1.4363        | -                      | -                      | -                      | -                     | -                      |
| 1.9617     | 32     | -             | 0.1406                 | 0.1468                 | 0.1356                 | 0.1395                | 0.1373                 |
| 2.4521     | 40     | 0.5627        | -                      | -                      | -                      | -                     | -                      |
| 2.9425     | 48     | -             | 0.1377                 | 0.1418                 | 0.1427                 | 0.1322                | 0.1437                 |
| 3.0651     | 50     | 0.2727        | -                      | -                      | -                      | -                     | -                      |
| 3.6782     | 60     | 0.1297        | -                      | -                      | -                      | -                     | -                      |
| **3.9234** | **64** | **-**         | **0.1393**             | **0.1457**             | **0.139**              | **0.1268**            | **0.1462**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.0.dev0
- Datasets: 2.21.0
- 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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