---
language:
- es
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:14907
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: jinaai/jina-embeddings-v3
widget:
- source_sentence: ¿Qué característica especial tenía la escultura del 'Torico' creada
    por Pedro Blesa?
  sentences:
  - 'Después de dorar el conejo en la receta de Conejo escabechado, en la misma sartén
    se rehogan los ajos, con el laurel y la pimienta.

    '
  - Rafael Barcelón se encargaba del servicio de electricidad en Valdeconejos en 1951.
  - La escultura del 'Torico' creada por Pedro Blesa era un anaglifo, visible en 3D
    con gafas especiales.
- source_sentence: ¿Por qué cantidad adquirió Francisco Santacruz la mina Escuadra
    en la subasta pública?
  sentences:
  - Después de la temporada 1986-87, el equipo descendió, lo que provocó su desaparición
    del campeonato en la temporada 1987-88.
  - '''Al bies'' significa en diagonal.'
  - Francisco Santacruz adquirió la mina Escuadra por la cantidad de 931 pesetas.
- source_sentence: ¿Quién se desempeñaba como fiscal en el ayuntamiento de Escucha
    en el año 1916?
  sentences:
  - El autor mencionado para la receta Sopas de ajo es Teo Martin Lafuente.
  - En Escucha en 1916, D. Joaquín Latorre del Río se desempeñaba como fiscal.
  - Felipe Mallén era el farmacéutico en Valdeconejos en 1928.
- source_sentence: ¿Qué información transmiten los 'toques' en la caña de un pozo
    durante las operaciones mineras?
  sentences:
  - Juan Pedro Martín encontró fragmentos de carbón de piedra en el paraje de El Horcajo.
  - Se publicó en 1970 por Ediciones Cultura y Acción. CNT.
  - 'Los ''toques'' son señales que se hacen en la caña del pozo para las distintas
    operaciones 1: alto 2: arriba 3: abajo 1+2: despacio arriba 1+3: despacio abajo
    4+2: personal arriba 4+3: personal abajo 4+1+2: señalista en jaula arriba 4+1+3:
    señalista en jaula abajo 5: jaula libre 6: maniobra'
- source_sentence: ¿En qué año se demarcó y reconoció la mina 'El Pilar'?
  sentences:
  - Según la quinta demanda del SOMM, todas compañías mineras debían entregar a todos
    sus obreros un libramiento de liquidación mensual
  - '''Tontiar'' significa cuando dos jóvenes empiezan con un noviazgo.'
  - La mina 'El Pilar' se demarcó y reconoció en 1857.
pipeline_tag: sentence-similarity
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
model-index:
- name: Lampistero
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.7700663850331925
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8925769462884732
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9155099577549789
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9330114665057333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7700663850331925
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2975256487628244
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18310199155099577
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09330114665057333
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7700663850331925
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8925769462884732
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9155099577549789
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9330114665057333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8578914781807897
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8330619976817926
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8343424106284848
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.7694628847314424
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8889559444779722
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9124924562462281
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9330114665057333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7694628847314424
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29631864815932407
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1824984912492456
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09330114665057332
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7694628847314424
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8889559444779722
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9124924562462281
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9330114665057333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8571049923900239
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8320899311243306
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8333457816447034
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.7682558841279421
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8865419432709717
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9112854556427278
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9305974652987327
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7682558841279421
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2955139810903239
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18225709112854557
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09305974652987326
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7682558841279421
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8865419432709717
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9112854556427278
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9305974652987327
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8555277012951626
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8307227155597702
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8321030396467847
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.764031382015691
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8901629450814725
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9082679541339771
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9299939649969825
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.764031382015691
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2967209816938242
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1816535908267954
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09299939649969825
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.764031382015691
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8901629450814725
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9082679541339771
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9299939649969825
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8535167149096011
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8282907530342651
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8296119986031772
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.7447193723596862
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8768859384429692
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9028364514182257
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9215449607724804
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7447193723596862
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2922953128143231
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1805672902836451
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09215449607724803
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7447193723596862
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8768859384429692
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9028364514182257
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9215449607724804
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8402664516336745
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8133905221714518
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8148588407289652
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.7103198551599276
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8491249245624622
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8780929390464696
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.899818949909475
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7103198551599276
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2830416415208208
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1756185878092939
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08998189499094747
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7103198551599276
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8491249245624622
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8780929390464696
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.899818949909475
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8119294706592789
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7829293234091058
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7850878407159746
      name: Cosine Map@100
---

# Lampistero

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) <!-- at revision f1944de8402dcd5f2b03f822a4bc22a7f2de2eb9 -->
- **Maximum Sequence Length:** 8194 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **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(
  (transformer): Transformer(
    (auto_model): XLMRobertaLoRA(
      (roberta): XLMRobertaModel(
        (embeddings): XLMRobertaEmbeddings(
          (word_embeddings): ParametrizedEmbedding(
            250002, 1024, padding_idx=1
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (token_type_embeddings): ParametrizedEmbedding(
            1, 1024
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
        )
        (emb_drop): Dropout(p=0.1, inplace=False)
        (emb_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
        (encoder): XLMRobertaEncoder(
          (layers): ModuleList(
            (0-23): 24 x Block(
              (mixer): MHA(
                (rotary_emb): RotaryEmbedding()
                (Wqkv): ParametrizedLinearResidual(
                  in_features=1024, out_features=3072, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (inner_attn): FlashSelfAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (inner_cross_attn): FlashCrossAttention(
                  (drop): Dropout(p=0.1, inplace=False)
                )
                (out_proj): ParametrizedLinear(
                  in_features=1024, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout1): Dropout(p=0.1, inplace=False)
              (drop_path1): StochasticDepth(p=0.0, mode=row)
              (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
              (mlp): Mlp(
                (fc1): ParametrizedLinear(
                  in_features=1024, out_features=4096, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
                (fc2): ParametrizedLinear(
                  in_features=4096, out_features=1024, bias=True
                  (parametrizations): ModuleDict(
                    (weight): ParametrizationList(
                      (0): LoRAParametrization()
                    )
                  )
                )
              )
              (dropout2): Dropout(p=0.1, inplace=False)
              (drop_path2): StochasticDepth(p=0.0, mode=row)
              (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            )
          )
        )
        (pooler): XLMRobertaPooler(
          (dense): ParametrizedLinear(
            in_features=1024, out_features=1024, bias=True
            (parametrizations): ModuleDict(
              (weight): ParametrizationList(
                (0): LoRAParametrization()
              )
            )
          )
          (activation): Tanh()
        )
      )
    )
  )
  (pooler): Pooling({'word_embedding_dimension': 1024, '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})
  (normalizer): 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("csanz91/lampistero_rag_embeddings_2")
# Run inference
sentences = [
    "¿En qué año se demarcó y reconoció la mina 'El Pilar'?",
    "La mina 'El Pilar' se demarcó y reconoció en 1857.",
    'Según la quinta demanda del SOMM, todas compañías mineras debían entregar a todos sus obreros un libramiento de liquidación mensual',
]
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) with these parameters:
  ```json
  {
      "truncate_dim": 1024
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7701     |
| cosine_accuracy@3   | 0.8926     |
| cosine_accuracy@5   | 0.9155     |
| cosine_accuracy@10  | 0.933      |
| cosine_precision@1  | 0.7701     |
| cosine_precision@3  | 0.2975     |
| cosine_precision@5  | 0.1831     |
| cosine_precision@10 | 0.0933     |
| cosine_recall@1     | 0.7701     |
| cosine_recall@3     | 0.8926     |
| cosine_recall@5     | 0.9155     |
| cosine_recall@10    | 0.933      |
| **cosine_ndcg@10**  | **0.8579** |
| cosine_mrr@10       | 0.8331     |
| cosine_map@100      | 0.8343     |

#### Information Retrieval

* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 768
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7695     |
| cosine_accuracy@3   | 0.889      |
| cosine_accuracy@5   | 0.9125     |
| cosine_accuracy@10  | 0.933      |
| cosine_precision@1  | 0.7695     |
| cosine_precision@3  | 0.2963     |
| cosine_precision@5  | 0.1825     |
| cosine_precision@10 | 0.0933     |
| cosine_recall@1     | 0.7695     |
| cosine_recall@3     | 0.889      |
| cosine_recall@5     | 0.9125     |
| cosine_recall@10    | 0.933      |
| **cosine_ndcg@10**  | **0.8571** |
| cosine_mrr@10       | 0.8321     |
| cosine_map@100      | 0.8333     |

#### Information Retrieval

* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 512
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7683     |
| cosine_accuracy@3   | 0.8865     |
| cosine_accuracy@5   | 0.9113     |
| cosine_accuracy@10  | 0.9306     |
| cosine_precision@1  | 0.7683     |
| cosine_precision@3  | 0.2955     |
| cosine_precision@5  | 0.1823     |
| cosine_precision@10 | 0.0931     |
| cosine_recall@1     | 0.7683     |
| cosine_recall@3     | 0.8865     |
| cosine_recall@5     | 0.9113     |
| cosine_recall@10    | 0.9306     |
| **cosine_ndcg@10**  | **0.8555** |
| cosine_mrr@10       | 0.8307     |
| cosine_map@100      | 0.8321     |

#### Information Retrieval

* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 256
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.764      |
| cosine_accuracy@3   | 0.8902     |
| cosine_accuracy@5   | 0.9083     |
| cosine_accuracy@10  | 0.93       |
| cosine_precision@1  | 0.764      |
| cosine_precision@3  | 0.2967     |
| cosine_precision@5  | 0.1817     |
| cosine_precision@10 | 0.093      |
| cosine_recall@1     | 0.764      |
| cosine_recall@3     | 0.8902     |
| cosine_recall@5     | 0.9083     |
| cosine_recall@10    | 0.93       |
| **cosine_ndcg@10**  | **0.8535** |
| cosine_mrr@10       | 0.8283     |
| cosine_map@100      | 0.8296     |

#### Information Retrieval

* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 128
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7447     |
| cosine_accuracy@3   | 0.8769     |
| cosine_accuracy@5   | 0.9028     |
| cosine_accuracy@10  | 0.9215     |
| cosine_precision@1  | 0.7447     |
| cosine_precision@3  | 0.2923     |
| cosine_precision@5  | 0.1806     |
| cosine_precision@10 | 0.0922     |
| cosine_recall@1     | 0.7447     |
| cosine_recall@3     | 0.8769     |
| cosine_recall@5     | 0.9028     |
| cosine_recall@10    | 0.9215     |
| **cosine_ndcg@10**  | **0.8403** |
| cosine_mrr@10       | 0.8134     |
| cosine_map@100      | 0.8149     |

#### Information Retrieval

* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
  ```json
  {
      "truncate_dim": 64
  }
  ```

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.7103     |
| cosine_accuracy@3   | 0.8491     |
| cosine_accuracy@5   | 0.8781     |
| cosine_accuracy@10  | 0.8998     |
| cosine_precision@1  | 0.7103     |
| cosine_precision@3  | 0.283      |
| cosine_precision@5  | 0.1756     |
| cosine_precision@10 | 0.09       |
| cosine_recall@1     | 0.7103     |
| cosine_recall@3     | 0.8491     |
| cosine_recall@5     | 0.8781     |
| cosine_recall@10    | 0.8998     |
| **cosine_ndcg@10**  | **0.8119** |
| cosine_mrr@10       | 0.7829     |
| cosine_map@100      | 0.7851     |

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 14,907 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | answer                                                                             |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             |
  | details | <ul><li>min: 9 tokens</li><li>mean: 26.09 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 34.02 tokens</li><li>max: 405 tokens</li></ul> |
* Samples:
  | query                                                                                                                                                                       | answer                                                                                                                                                                    |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>¿Qué tipos de palas se utilizan para cargar el carbón y el mineral?</code>                                                                                            | <code>Se utiliza una pala convencional y una pala hidráulica, esta última descarga sobre un páncer, puede hacerlo lateralmente y se desplaza sobre ruedas u oruga.</code> |
  | <code>Tras el cierre de la tejería de Florencio Salvador, ¿de dónde procedieron finalmente los ladrillos para las doscientas diez viviendas construidas en Utrillas?</code> | <code>Los ladrillos y material para las doscientas diez viviendas construidas en Utrillas procedieron finalmente de Letux, Zaragoza .</code>                              |
  | <code>¿Cuál es el formato de los juegos infantiles que se están preparando para el verano en Escucha en 2021?</code>                                                        | <code>Los juegos infantiles que se están preparando para el verano en Escucha en 2021 están en formato revista.</code>                                                    |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 8
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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`: 64
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `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`: False
- `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}
- `tp_size`: 0
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0    | 8    | -             | 0.7841                  | 0.7835                 | 0.7836                 | 0.7791                 | 0.7665                 | 0.7226                |
| 1.2747 | 10   | 58.1187       | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.0    | 16   | -             | 0.8348                  | 0.8366                 | 0.8345                 | 0.8301                 | 0.8184                 | 0.7861                |
| 2.5494 | 20   | 24.4181       | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.0    | 24   | -             | 0.8521                  | 0.8504                 | 0.8503                 | 0.8457                 | 0.8319                 | 0.8007                |
| 3.8240 | 30   | 16.1488       | -                       | -                      | -                      | -                      | -                      | -                     |
| 4.0    | 32   | -             | 0.8561                  | 0.8548                 | 0.8555                 | 0.8509                 | 0.8387                 | 0.8073                |
| 5.0    | 40   | 13.4897       | 0.8585                  | 0.8556                 | 0.8545                 | 0.8528                 | 0.8397                 | 0.8111                |
| 6.0    | 48   | -             | 0.8578                  | 0.8563                 | 0.8550                 | 0.8535                 | 0.8410                 | 0.8110                |
| 6.2747 | 50   | 13.7469       | -                       | -                      | -                      | -                      | -                      | -                     |
| 7.0    | 56   | -             | 0.8579                  | 0.8571                 | 0.8555                 | 0.8535                 | 0.8403                 | 0.8119                |


### Framework Versions
- Python: 3.12.10
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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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