---
base_model: BAAI/bge-m3
datasets: []
language:
- es
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:2947
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Es uso privativo el que determina la ocupación de una porción del
    dominio público, de modo que se limita o excluye la utilización del mismo por
    otros interesados.
  sentences:
  - ¿Qué es el uso privativo de los bienes de dominio público?
  - ¿Qué es la sanidad ambiental?
  - ¿Qué información básica debe contener la información que se facilita al afectado
    cuando se obtienen datos personales de él?
- source_sentence: 'Las retribuciones básicas, que se fijan en la Ley de Presupuestos
    Generales del Estado, estarán integradas única y exclusivamente por: a) El sueldo
    asignado a cada Subgrupo o Grupo de clasificación profesional, en el supuesto
    de que éste no tenga Subgrupo. b) Los trienios, que consisten en una cantidad,
    que será igual para cada Subgrupo o Grupo de clasificación profesional, en el
    supuesto de que éste no tenga Subgrupo, por cada tres años de servicio.'
  sentences:
  - ¿Qué se entiende por retribuciones básicas?
  - ¿Cuál es el título competencial de esta ley orgánica?
  - ¿Qué se aprueba a propuesta del Ministro de Hacienda?
- source_sentence: Se reconoce el valor social de las niñas, niños y adolescentes
    como personas que realizan un aporte afectivo, cultural y ético al caudal social,
    y cuyo protagonismo, creatividad y posicionamiento activo enriquecen la vida colectiva.
  sentences:
  - ¿Qué sucede si se produce un incumplimiento de las actuaciones establecidas en
    el Plan de inclusión sociolaboral?
  - ¿Qué se reconoce en cuanto al valor social de la infancia?
  - ¿Cuál es el plazo de prescripción de las infracciones?
- source_sentence: Las empresas y las universidades podrán promover y participar en
    programas de voluntariado que cumplan los requisitos establecidos en esta Ley.
  sentences:
  - ¿Cuál es la consideración de las infracciones muy graves?
  - ¿Qué tipo de empresas pueden promover y participar en programas de voluntariado?
  - ¿Qué tipo de entidades están obligadas a cumplir con las obligaciones de publicidad
    activa?
- source_sentence: Artículo 6. Definiciones. 1. Discriminación directa e indirecta.
    b) La discriminación indirecta se produce cuando una disposición, criterio o práctica
    aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja
    particular con respecto a otras por razón de las causas previstas en el apartado
    1 del artículo 2.
  sentences:
  - ¿Cuál es el papel del Consejo de Salud de Área?
  - ¿Qué se considera discriminación indirecta?
  - ¿Qué tipo de información se considera veraz?
model-index:
- name: BGE large Legal Spanish
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.551829268292683
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8048780487804879
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8445121951219512
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9024390243902439
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.551829268292683
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2682926829268293
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16890243902439023
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09024390243902437
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.551829268292683
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8048780487804879
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8445121951219512
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9024390243902439
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7379864083246442
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6841608594657377
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6880865147668174
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.5487804878048781
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8048780487804879
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.850609756097561
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9024390243902439
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5487804878048781
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2682926829268293
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17012195121951218
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09024390243902437
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5487804878048781
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8048780487804879
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.850609756097561
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9024390243902439
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.736128283939538
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6815560878823075
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6854885550473444
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.5579268292682927
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8109756097560976
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.850609756097561
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8932926829268293
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5579268292682927
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27032520325203246
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17012195121951218
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08932926829268292
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5579268292682927
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8109756097560976
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.850609756097561
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8932926829268293
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7362627915663099
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6845153406891215
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6889302518809046
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.5548780487804879
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7957317073170732
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8323170731707317
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8841463414634146
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5548780487804879
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2652439024390244
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16646341463414632
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08841463414634146
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5548780487804879
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7957317073170732
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8323170731707317
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8841463414634146
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7307377627264078
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6803994870305846
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6851337079025414
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5213414634146342
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7621951219512195
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8140243902439024
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8658536585365854
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5213414634146342
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25406504065040647
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16280487804878047
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08658536585365853
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5213414634146342
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7621951219512195
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8140243902439024
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8658536585365854
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7028480041122221
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6495075977545491
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6549966797371862
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.4847560975609756
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.725609756097561
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7804878048780488
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8536585365853658
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4847560975609756
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.24186991869918703
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15609756097560976
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08536585365853658
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.4847560975609756
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.725609756097561
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7804878048780488
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8536585365853658
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6729421249114532
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6146668118466899
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6198317239083065
      name: Cosine Map@100
---

# BGE large Legal Spanish

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** Unknown -->
- **Language:** es
- **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': 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("dariolopez/bge-m3-es-legal-tmp-6")
# Run inference
sentences = [
    'Artículo 6. Definiciones. 1. Discriminación directa e indirecta. b) La discriminación indirecta se produce cuando una disposición, criterio o práctica aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja particular con respecto a otras por razón de las causas previstas en el apartado 1 del artículo 2.',
    '¿Qué se considera discriminación indirecta?',
    '¿Qué tipo de información se considera veraz?',
]
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   | 0.5518     |
| cosine_accuracy@3   | 0.8049     |
| cosine_accuracy@5   | 0.8445     |
| cosine_accuracy@10  | 0.9024     |
| cosine_precision@1  | 0.5518     |
| cosine_precision@3  | 0.2683     |
| cosine_precision@5  | 0.1689     |
| cosine_precision@10 | 0.0902     |
| cosine_recall@1     | 0.5518     |
| cosine_recall@3     | 0.8049     |
| cosine_recall@5     | 0.8445     |
| cosine_recall@10    | 0.9024     |
| cosine_ndcg@10      | 0.738      |
| cosine_mrr@10       | 0.6842     |
| **cosine_map@100**  | **0.6881** |

#### 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   | 0.5488     |
| cosine_accuracy@3   | 0.8049     |
| cosine_accuracy@5   | 0.8506     |
| cosine_accuracy@10  | 0.9024     |
| cosine_precision@1  | 0.5488     |
| cosine_precision@3  | 0.2683     |
| cosine_precision@5  | 0.1701     |
| cosine_precision@10 | 0.0902     |
| cosine_recall@1     | 0.5488     |
| cosine_recall@3     | 0.8049     |
| cosine_recall@5     | 0.8506     |
| cosine_recall@10    | 0.9024     |
| cosine_ndcg@10      | 0.7361     |
| cosine_mrr@10       | 0.6816     |
| **cosine_map@100**  | **0.6855** |

#### 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   | 0.5579     |
| cosine_accuracy@3   | 0.811      |
| cosine_accuracy@5   | 0.8506     |
| cosine_accuracy@10  | 0.8933     |
| cosine_precision@1  | 0.5579     |
| cosine_precision@3  | 0.2703     |
| cosine_precision@5  | 0.1701     |
| cosine_precision@10 | 0.0893     |
| cosine_recall@1     | 0.5579     |
| cosine_recall@3     | 0.811      |
| cosine_recall@5     | 0.8506     |
| cosine_recall@10    | 0.8933     |
| cosine_ndcg@10      | 0.7363     |
| cosine_mrr@10       | 0.6845     |
| **cosine_map@100**  | **0.6889** |

#### 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   | 0.5549     |
| cosine_accuracy@3   | 0.7957     |
| cosine_accuracy@5   | 0.8323     |
| cosine_accuracy@10  | 0.8841     |
| cosine_precision@1  | 0.5549     |
| cosine_precision@3  | 0.2652     |
| cosine_precision@5  | 0.1665     |
| cosine_precision@10 | 0.0884     |
| cosine_recall@1     | 0.5549     |
| cosine_recall@3     | 0.7957     |
| cosine_recall@5     | 0.8323     |
| cosine_recall@10    | 0.8841     |
| cosine_ndcg@10      | 0.7307     |
| cosine_mrr@10       | 0.6804     |
| **cosine_map@100**  | **0.6851** |

#### 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   | 0.5213    |
| cosine_accuracy@3   | 0.7622    |
| cosine_accuracy@5   | 0.814     |
| cosine_accuracy@10  | 0.8659    |
| cosine_precision@1  | 0.5213    |
| cosine_precision@3  | 0.2541    |
| cosine_precision@5  | 0.1628    |
| cosine_precision@10 | 0.0866    |
| cosine_recall@1     | 0.5213    |
| cosine_recall@3     | 0.7622    |
| cosine_recall@5     | 0.814     |
| cosine_recall@10    | 0.8659    |
| cosine_ndcg@10      | 0.7028    |
| cosine_mrr@10       | 0.6495    |
| **cosine_map@100**  | **0.655** |

#### 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   | 0.4848     |
| cosine_accuracy@3   | 0.7256     |
| cosine_accuracy@5   | 0.7805     |
| cosine_accuracy@10  | 0.8537     |
| cosine_precision@1  | 0.4848     |
| cosine_precision@3  | 0.2419     |
| cosine_precision@5  | 0.1561     |
| cosine_precision@10 | 0.0854     |
| cosine_recall@1     | 0.4848     |
| cosine_recall@3     | 0.7256     |
| cosine_recall@5     | 0.7805     |
| cosine_recall@10    | 0.8537     |
| cosine_ndcg@10      | 0.6729     |
| cosine_mrr@10       | 0.6147     |
| **cosine_map@100**  | **0.6198** |

<!--
## 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 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`: 6
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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
- `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`: 6
- `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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | 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 |
|:----------:|:------:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4324     | 5      | 1.6507        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.8649     | 10     | 0.9598        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9514     | 11     | -             | 0.5477     | 0.6833                  | 0.6616                 | 0.6836                 | 0.6758                 | 0.5994                | 0.6744                 |
| 1.2973     | 15     | 0.8248        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.7297     | 20     | 0.3858        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9892     | 23     | -             | 0.4242     | 0.6748                  | 0.6544                 | 0.6833                 | 0.6740                 | 0.6233                | 0.6697                 |
| 2.1622     | 25     | 0.32          | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.5946     | 30     | 0.1703        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9405     | 34     | -             | 0.3940     | 0.6755                  | 0.6523                 | 0.6823                 | 0.6797                 | 0.6196                | 0.6776                 |
| 3.0270     | 35     | 0.1337        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4595     | 40     | 0.0949        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.8919     | 45     | 0.0594        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| **3.9784** | **46** | **-**         | **0.3735** | **0.6867**              | **0.6588**             | **0.6865**             | **0.6854**             | **0.6189**            | **0.6826**             |
| 4.3243     | 50     | 0.07          | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.7568     | 55     | 0.0524        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9297     | 57     | -             | 0.3642     | 0.6870                  | 0.6577                 | 0.6858                 | 0.6871                 | 0.6228                | 0.6853                 |
| 5.1892     | 60     | 0.0598        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.6216     | 65     | 0.0491        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.7081     | 66     | -             | 0.3626     | 0.6881                  | 0.6550                 | 0.6851                 | 0.6889                 | 0.6198                | 0.6855                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.2.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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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