---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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: Les respectives convocatòries determinaran les parades disponibles
    així com les seves característiques i descripció.
  sentences:
  - Quin és l'objectiu secundari dels ajuts per a la creació de noves empreses?
  - Qui és responsable de la resolució de la situació en un domini particular?
  - Quin és el paper de les convocatòries?
- source_sentence: Cal revisar la informació i els terminis de la convocatòria específica
    de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.
  sentences:
  - Quin és el paper de la Seu electrònica de l'Ajuntament de Sitges en un procés
    de selecció de personal?
  - Quin és l'objectiu principal de sol·licitar el certificat d'antiguitat i legalitat
    d'una finca?
  - Quin és el propòsit dels ajuts a la contractació laboral en relació amb l'ocupació?
- source_sentence: Per a poder rebre les subvencions pel suport educatiu a les escoles
    públiques de Sitges, els beneficiaris han de presentar un projecte d'acció que
    compleixi els requisits establerts en la convocatòria corresponent.
  sentences:
  - Quin és el propòsit de la sol·licitud d'ajuts?
  - Quin és el paper de l'Ajuntament de Sitges en la Fira de la Vila del Llibre de
    Sitges?
  - Quin és el requisit per a poder rebre les subvencions pel suport educatiu a les
    escoles públiques de Sitges?
- source_sentence: Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar
    l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació
    prèvia corresponent.
  sentences:
  - Quin és el paper de la comunicació prèvia en la prevenció d'incendis?
  - Quins són els requisits per a presentar una sol·licitud de subvenció?
  - Quins animals es consideren animals de companyia?
- source_sentence: El termini per a la presentació de les sol·licituds de modificació
    del projecte o activitat subvencionat és de 15 dies naturals abans de la finalització
    del projecte o activitat.
  sentences:
  - Quin és el termini per a la presentació de les sol·licituds de modificació del
    projecte o activitat subvencionat?
  - Quins són els tres tipus de llicència d'obra?
  - Quin és el registre on es troben les dades d'inscripció?
model-index:
- name: BGE base Financial Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.0625
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11637931034482758
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1810344827586207
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.35560344827586204
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0625
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03879310344827586
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.036206896551724134
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03556034482758621
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0625
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11637931034482758
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1810344827586207
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.35560344827586204
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17546345429803745
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.12245227832512329
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1487513151351338
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.0625
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.10991379310344827
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.17025862068965517
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.35560344827586204
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0625
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.036637931034482756
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03405172413793103
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03556034482758621
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0625
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.10991379310344827
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.17025862068965517
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.35560344827586204
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17281692680622274
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11932898877942
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.145505253139907
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.05603448275862069
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.12284482758620689
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1724137931034483
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34051724137931033
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05603448275862069
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.040948275862068964
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.034482758620689655
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03405172413793103
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05603448275862069
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12284482758620689
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1724137931034483
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34051724137931033
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16797293983212122
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11677955665024647
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14311504496457605
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.05172413793103448
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11422413793103449
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18318965517241378
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.31896551724137934
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05172413793103448
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.038074712643678156
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.036637931034482756
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03189655172413793
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05172413793103448
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11422413793103449
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18318965517241378
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.31896551724137934
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.15889833336121337
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11119406814449927
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1376499182467716
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.04525862068965517
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.10560344827586207
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.16594827586206898
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.30603448275862066
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04525862068965517
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.035201149425287355
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0331896551724138
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.030603448275862068
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04525862068965517
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.10560344827586207
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.16594827586206898
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.30603448275862066
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.14903489989981042
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.10241516146688567
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.12594670041141745
      name: Cosine Map@100
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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/sitges2608")
# Run inference
sentences = [
    'El termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat és de 15 dies naturals abans de la finalització del projecte o activitat.',
    'Quin és el termini per a la presentació de les sol·licituds de modificació del projecte o activitat subvencionat?',
    "Quin és el registre on es troben les dades d'inscripció?",
]
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   | 0.0625     |
| cosine_accuracy@3   | 0.1164     |
| cosine_accuracy@5   | 0.181      |
| cosine_accuracy@10  | 0.3556     |
| cosine_precision@1  | 0.0625     |
| cosine_precision@3  | 0.0388     |
| cosine_precision@5  | 0.0362     |
| cosine_precision@10 | 0.0356     |
| cosine_recall@1     | 0.0625     |
| cosine_recall@3     | 0.1164     |
| cosine_recall@5     | 0.181      |
| cosine_recall@10    | 0.3556     |
| cosine_ndcg@10      | 0.1755     |
| cosine_mrr@10       | 0.1225     |
| **cosine_map@100**  | **0.1488** |

#### 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   | 0.0625     |
| cosine_accuracy@3   | 0.1099     |
| cosine_accuracy@5   | 0.1703     |
| cosine_accuracy@10  | 0.3556     |
| cosine_precision@1  | 0.0625     |
| cosine_precision@3  | 0.0366     |
| cosine_precision@5  | 0.0341     |
| cosine_precision@10 | 0.0356     |
| cosine_recall@1     | 0.0625     |
| cosine_recall@3     | 0.1099     |
| cosine_recall@5     | 0.1703     |
| cosine_recall@10    | 0.3556     |
| cosine_ndcg@10      | 0.1728     |
| cosine_mrr@10       | 0.1193     |
| **cosine_map@100**  | **0.1455** |

#### 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   | 0.056      |
| cosine_accuracy@3   | 0.1228     |
| cosine_accuracy@5   | 0.1724     |
| cosine_accuracy@10  | 0.3405     |
| cosine_precision@1  | 0.056      |
| cosine_precision@3  | 0.0409     |
| cosine_precision@5  | 0.0345     |
| cosine_precision@10 | 0.0341     |
| cosine_recall@1     | 0.056      |
| cosine_recall@3     | 0.1228     |
| cosine_recall@5     | 0.1724     |
| cosine_recall@10    | 0.3405     |
| cosine_ndcg@10      | 0.168      |
| cosine_mrr@10       | 0.1168     |
| **cosine_map@100**  | **0.1431** |

#### 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   | 0.0517     |
| cosine_accuracy@3   | 0.1142     |
| cosine_accuracy@5   | 0.1832     |
| cosine_accuracy@10  | 0.319      |
| cosine_precision@1  | 0.0517     |
| cosine_precision@3  | 0.0381     |
| cosine_precision@5  | 0.0366     |
| cosine_precision@10 | 0.0319     |
| cosine_recall@1     | 0.0517     |
| cosine_recall@3     | 0.1142     |
| cosine_recall@5     | 0.1832     |
| cosine_recall@10    | 0.319      |
| cosine_ndcg@10      | 0.1589     |
| cosine_mrr@10       | 0.1112     |
| **cosine_map@100**  | **0.1376** |

#### 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   | 0.0453     |
| cosine_accuracy@3   | 0.1056     |
| cosine_accuracy@5   | 0.1659     |
| cosine_accuracy@10  | 0.306      |
| cosine_precision@1  | 0.0453     |
| cosine_precision@3  | 0.0352     |
| cosine_precision@5  | 0.0332     |
| cosine_precision@10 | 0.0306     |
| cosine_recall@1     | 0.0453     |
| cosine_recall@3     | 0.1056     |
| cosine_recall@5     | 0.1659     |
| cosine_recall@10    | 0.306      |
| cosine_ndcg@10      | 0.149      |
| cosine_mrr@10       | 0.1024     |
| **cosine_map@100**  | **0.1259** |

<!--
## 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.*
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## 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: 8 tokens</li><li>mean: 66.25 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 28.12 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                          | anchor                                                                               |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | <code>La persona titular d'una llicència de vehicle lleuger per al servei públic (auto-taxi), en produïr-se un canvi de vehicle, ha de notificar a l'Ajuntament les dades del nou vehicle.</code>                                                                                                 | <code>Quin és el propòsit de la notificació de les dades del nou vehicle?</code>     |
  | <code>S'entén per garantia l'ingrés a la Tresoreria de l'Ajuntament d'una quantitat econòmica que garanteix el compliment d'una obligació adquirida amb aquest (garanties de concursos o licitacions, fraccionaments de tributs en via executiva, reposició de paviments per obres, etc.).</code> | <code>Què s'entén per garantia a l'Ajuntament de Sitges?</code>                      |
  | <code>L'ús d'espais del Centre Cultural Miramar per a la realització d'exposicions.</code>                                                                                                                                                                                                        | <code>Quin és el centre cultural on es poden realitzar les exposicions d'art?</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`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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`: 32
- `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`: 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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9771     | 8      | -             | 0.1210                 | 0.1384                 | 0.1341                 | 0.1002                | 0.1376                 |
| 1.2137     | 10     | 7.5469        | -                      | -                      | -                      | -                     | -                      |
| **1.9466** | **16** | **-**         | **0.136**              | **0.1404**             | **0.1443**             | **0.1249**            | **0.1414**             |
| 2.4275     | 20     | 4.0024        | -                      | -                      | -                      | -                     | -                      |
| 2.9160     | 24     | -             | 0.1388                 | 0.1460                 | 0.1446                 | 0.1278                | 0.1436                 |
| 3.6412     | 30     | 3.2149        | -                      | -                      | -                      | -                     | -                      |
| 3.8855     | 32     | -             | 0.1376                 | 0.1431                 | 0.1455                 | 0.1259                | 0.1488                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+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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