---
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: 0.05603448275862069
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.125
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21336206896551724
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.40948275862068967
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05603448275862069
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.041666666666666664
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04267241379310346
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.040948275862068964
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05603448275862069
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.125
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21336206896551724
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.40948275862068967
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19394246727908016
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1301253762999455
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.15541893353957212
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.05172413793103448
      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: 0.4073275862068966
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05172413793103448
      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: 0.04073275862068965
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05172413793103448
      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: 0.4073275862068966
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19075313852531367
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1267044677066231
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.15217462615525276
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.05818965517241379
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.1206896551724138
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.20689655172413793
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.41594827586206895
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05818965517241379
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04022988505747126
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.041379310344827586
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04159482758620689
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05818965517241379
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1206896551724138
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.20689655172413793
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.41594827586206895
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19717072550930018
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.13257902298850593
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1580145716033785
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.05603448275862069
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11853448275862069
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1939655172413793
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4202586206896552
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05603448275862069
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.039511494252873564
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03879310344827587
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04202586206896552
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05603448275862069
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11853448275862069
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1939655172413793
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4202586206896552
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19482639723718284
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1286176108374386
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.15326245290189994
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.05172413793103448
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.1336206896551724
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.20905172413793102
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.39439655172413796
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05172413793103448
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.044540229885057465
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04181034482758621
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03943965517241379
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05172413793103448
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1336206896551724
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.20905172413793102
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.39439655172413796
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.188263246156266
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.12684814586754262
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.15277153038949104
      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-rerankv4-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   | 0.056      |
| cosine_accuracy@3   | 0.125      |
| cosine_accuracy@5   | 0.2134     |
| cosine_accuracy@10  | 0.4095     |
| cosine_precision@1  | 0.056      |
| cosine_precision@3  | 0.0417     |
| cosine_precision@5  | 0.0427     |
| cosine_precision@10 | 0.0409     |
| cosine_recall@1     | 0.056      |
| cosine_recall@3     | 0.125      |
| cosine_recall@5     | 0.2134     |
| cosine_recall@10    | 0.4095     |
| cosine_ndcg@10      | 0.1939     |
| cosine_mrr@10       | 0.1301     |
| **cosine_map@100**  | **0.1554** |

#### 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   | 0.0517     |
| cosine_accuracy@3   | 0.1228     |
| cosine_accuracy@5   | 0.2004     |
| cosine_accuracy@10  | 0.4073     |
| cosine_precision@1  | 0.0517     |
| cosine_precision@3  | 0.0409     |
| cosine_precision@5  | 0.0401     |
| cosine_precision@10 | 0.0407     |
| cosine_recall@1     | 0.0517     |
| cosine_recall@3     | 0.1228     |
| cosine_recall@5     | 0.2004     |
| cosine_recall@10    | 0.4073     |
| cosine_ndcg@10      | 0.1908     |
| cosine_mrr@10       | 0.1267     |
| **cosine_map@100**  | **0.1522** |

#### 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   | 0.0582    |
| cosine_accuracy@3   | 0.1207    |
| cosine_accuracy@5   | 0.2069    |
| cosine_accuracy@10  | 0.4159    |
| cosine_precision@1  | 0.0582    |
| cosine_precision@3  | 0.0402    |
| cosine_precision@5  | 0.0414    |
| cosine_precision@10 | 0.0416    |
| cosine_recall@1     | 0.0582    |
| cosine_recall@3     | 0.1207    |
| cosine_recall@5     | 0.2069    |
| cosine_recall@10    | 0.4159    |
| cosine_ndcg@10      | 0.1972    |
| cosine_mrr@10       | 0.1326    |
| **cosine_map@100**  | **0.158** |

#### 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   | 0.056      |
| cosine_accuracy@3   | 0.1185     |
| cosine_accuracy@5   | 0.194      |
| cosine_accuracy@10  | 0.4203     |
| cosine_precision@1  | 0.056      |
| cosine_precision@3  | 0.0395     |
| cosine_precision@5  | 0.0388     |
| cosine_precision@10 | 0.042      |
| cosine_recall@1     | 0.056      |
| cosine_recall@3     | 0.1185     |
| cosine_recall@5     | 0.194      |
| cosine_recall@10    | 0.4203     |
| cosine_ndcg@10      | 0.1948     |
| cosine_mrr@10       | 0.1286     |
| **cosine_map@100**  | **0.1533** |

#### 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   | 0.0517     |
| cosine_accuracy@3   | 0.1336     |
| cosine_accuracy@5   | 0.2091     |
| cosine_accuracy@10  | 0.3944     |
| cosine_precision@1  | 0.0517     |
| cosine_precision@3  | 0.0445     |
| cosine_precision@5  | 0.0418     |
| cosine_precision@10 | 0.0394     |
| cosine_recall@1     | 0.0517     |
| cosine_recall@3     | 0.1336     |
| cosine_recall@5     | 0.2091     |
| cosine_recall@10    | 0.3944     |
| cosine_ndcg@10      | 0.1883     |
| cosine_mrr@10       | 0.1268     |
| **cosine_map@100**  | **0.1528** |

<!--
## 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.*
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<!--
### 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`: 10
- `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`: 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`: 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                 | 0.1390                 | 0.1268                | 0.1462                 |
| 0.6130     | 10      | 0.096         | -                      | -                      | -                      | -                     | -                      |
| 0.9808     | 16      | -             | 0.1458                 | 0.1414                 | 0.1443                 | 0.1369                | 0.1407                 |
| 1.2261     | 20      | 0.1118        | -                      | -                      | -                      | -                     | -                      |
| 1.8391     | 30      | 0.1335        | -                      | -                      | -                      | -                     | -                      |
| 1.9617     | 32      | -             | 0.1486                 | 0.1476                 | 0.1419                 | 0.1489                | 0.1503                 |
| 2.4521     | 40      | 0.0765        | -                      | -                      | -                      | -                     | -                      |
| 2.9425     | 48      | -             | 0.1501                 | 0.1459                 | 0.1424                 | 0.1413                | 0.1437                 |
| 3.0651     | 50      | 0.1449        | -                      | -                      | -                      | -                     | -                      |
| 3.6782     | 60      | 0.0954        | -                      | -                      | -                      | -                     | -                      |
| 3.9847     | 65      | -             | 0.1562                 | 0.1559                 | 0.1517                 | 0.1409                | 0.1553                 |
| 4.2912     | 70      | 0.0786        | -                      | -                      | -                      | -                     | -                      |
| 4.9042     | 80      | 0.0973        | -                      | -                      | -                      | -                     | -                      |
| 4.9655     | 81      | -             | 0.1433                 | 0.1397                 | 0.1459                 | 0.1430                | 0.1457                 |
| 5.5172     | 90      | 0.0334        | -                      | -                      | -                      | -                     | -                      |
| 5.9464     | 97      | -             | 0.1499                 | 0.1482                 | 0.1478                 | 0.1466                | 0.1503                 |
| 6.1303     | 100     | 0.0278        | -                      | -                      | -                      | -                     | -                      |
| 6.7433     | 110     | 0.0223        | -                      | -                      | -                      | -                     | -                      |
| 6.9885     | 114     | -             | 0.1561                 | 0.1532                 | 0.1509                 | 0.1519                | 0.1547                 |
| 7.3563     | 120     | 0.0137        | -                      | -                      | -                      | -                     | -                      |
| 7.9693     | 130     | 0.0129        | 0.1525                 | 0.1557                 | 0.1505                 | 0.1570                | 0.1570                 |
| 8.5824     | 140     | 0.0052        | -                      | -                      | -                      | -                     | -                      |
| **8.9502** | **146** | **-**         | **0.1525**             | **0.1586**             | **0.1493**             | **0.1569**            | **0.1553**             |
| 9.1954     | 150     | 0.0044        | -                      | -                      | -                      | -                     | -                      |
| 9.8084     | 160     | 0.0064        | 0.1533                 | 0.1580                 | 0.1522                 | 0.1528                | 0.1554                 |

* 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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