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Add new SentenceTransformer model
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---
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: Describe la tradición del 'rosario de candiles' en el contexto
de la minería.
sentences:
- Un mechazo es la combustión de la mecha sin que se llegue a inflamar el barreno.
- La siega tradicional en Escucha comenzaba antes de San Juan con las cebadas.
- El 'rosario de candiles' es una tradición religiosa celebrada en la festividad
de San Juan, en la que los mineros escuchan y acompañan con sus candiles de carburo,
rezando a dos coros y cantando en parte.
- source_sentence: ¿Qué significa la expresión 'pillar una mojadina'?
sentences:
- En el campeonato provincial de atletismo en Alcorisa en mayo, Pilar Brumos de
Escucha logró la 3ª posición en 600 metros y el subcampeonato en peso.
- Los empresarios de Escucha se habían unido para poder participar en las elecciones
a CC.PP. ya que era necesario que la plantilla de la empresa superase el número
de 50 trabajadores..
- '''Pillar una mojadina'' significa empaparse, quedar empapado.'
- source_sentence: ¿En qué año Carbones de Teruel registra la mina 'pablo' en Escucha?
sentences:
- Puede referirse a un calcetín para bebés o a un calcetín gordo.
- Carbones de Teruel registra la mina 'pablo' en Escucha en 1900.
- 'Jesús Conesa explicó a la Junta de Espectáculos que el anterior propietario,
Sr. Latorre Galindo, tenía otro cine en Utrillas, lo que causaba continuos equívocos
en envíos de material y pagos, al creerse que ambos cines le pertenecían o eran
la misma empresa. '
- source_sentence: ¿Quién regentaba el Cine Avenida de Escucha en el momento de su
cierre?
sentences:
- Se usa con el significado de 'cuando'.
- El CD Escucha alineó a Castillo, Romero, Bobadilla, Moraleda, Luis, González,
Higinio, Torres, Calomarde I, Calomarde II y Navarro en el partido de Copa contra
el Alcorisa.
- Antonio Malpica regentaba el Cine Avenida de Escucha en el momento de su cierre.
- source_sentence: ¿Qué porcentaje de aumento salarial reclamaba el Sindicato Minero
en el conflicto de Utrillas que llevó a plantear la huelga del 12 de octubre de
1930?
sentences:
- Antonio Gargallo.
- Una publicación con una fotografía para el recuerdo de la locomotora llamada 'Escucha'.
- El Sindicato Minero reclamaba un aumento del 20% los sueldos en el conflicto de
Utrillas.
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: 0.7803258901629451
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8883524441762221
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.904043452021726
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9233554616777309
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7803258901629451
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29611748139207406
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18080869040434522
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09233554616777308
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7803258901629451
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8883524441762221
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.904043452021726
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9233554616777309
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8576141434466037
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8359425142014155
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8374344979701236
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7827398913699457
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8877489438744719
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9034399517199758
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9245624622812312
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7827398913699457
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.295916314624824
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18068799034399516
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09245624622812311
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7827398913699457
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8877489438744719
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9034399517199758
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9245624622812312
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.858770916125463
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8371705894186279
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8385437636605255
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7797223898611949
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8859384429692215
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9010259505129753
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9227519613759807
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7797223898611949
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2953128143230738
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18020519010259503
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09227519613759806
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7797223898611949
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8859384429692215
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9010259505129753
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9227519613759807
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8564496755344808
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8346785163471941
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8361853082918266
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7706698853349426
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8823174411587206
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9016294508147255
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9191309595654797
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7706698853349426
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2941058137195735
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18032589016294506
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09191309595654798
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7706698853349426
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8823174411587206
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9016294508147255
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9191309595654797
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.851155539622205
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8286940445057519
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8302805177061129
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.7604103802051901
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8690404345202173
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8901629450814725
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9130959565479783
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7604103802051901
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28968014484007243
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1780325890162945
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09130959565479783
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7604103802051901
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8690404345202173
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8901629450814725
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9130959565479783
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8415141158022221
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8181217729497756
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8199539602494803
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.7248038624019312
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.852142426071213
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8750754375377188
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8974049487024743
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7248038624019312
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28404747535707103
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17501508750754374
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08974049487024743
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7248038624019312
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.852142426071213
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8750754375377188
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8974049487024743
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8181789750224895
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7920167926353802
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.793825252598125
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")
# Run inference
sentences = [
'¿Qué porcentaje de aumento salarial reclamaba el Sindicato Minero en el conflicto de Utrillas que llevó a plantear la huelga del 12 de octubre de 1930?',
'El Sindicato Minero reclamaba un aumento del 20% los sueldos en el conflicto de Utrillas.',
'Antonio Gargallo.',
]
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 | 0.7803 |
| cosine_accuracy@3 | 0.8884 |
| cosine_accuracy@5 | 0.904 |
| cosine_accuracy@10 | 0.9234 |
| cosine_precision@1 | 0.7803 |
| cosine_precision@3 | 0.2961 |
| cosine_precision@5 | 0.1808 |
| cosine_precision@10 | 0.0923 |
| cosine_recall@1 | 0.7803 |
| cosine_recall@3 | 0.8884 |
| cosine_recall@5 | 0.904 |
| cosine_recall@10 | 0.9234 |
| **cosine_ndcg@10** | **0.8576** |
| cosine_mrr@10 | 0.8359 |
| cosine_map@100 | 0.8374 |
#### 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 | 0.7827 |
| cosine_accuracy@3 | 0.8877 |
| cosine_accuracy@5 | 0.9034 |
| cosine_accuracy@10 | 0.9246 |
| cosine_precision@1 | 0.7827 |
| cosine_precision@3 | 0.2959 |
| cosine_precision@5 | 0.1807 |
| cosine_precision@10 | 0.0925 |
| cosine_recall@1 | 0.7827 |
| cosine_recall@3 | 0.8877 |
| cosine_recall@5 | 0.9034 |
| cosine_recall@10 | 0.9246 |
| **cosine_ndcg@10** | **0.8588** |
| cosine_mrr@10 | 0.8372 |
| cosine_map@100 | 0.8385 |
#### 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 | 0.7797 |
| cosine_accuracy@3 | 0.8859 |
| cosine_accuracy@5 | 0.901 |
| cosine_accuracy@10 | 0.9228 |
| cosine_precision@1 | 0.7797 |
| cosine_precision@3 | 0.2953 |
| cosine_precision@5 | 0.1802 |
| cosine_precision@10 | 0.0923 |
| cosine_recall@1 | 0.7797 |
| cosine_recall@3 | 0.8859 |
| cosine_recall@5 | 0.901 |
| cosine_recall@10 | 0.9228 |
| **cosine_ndcg@10** | **0.8564** |
| cosine_mrr@10 | 0.8347 |
| cosine_map@100 | 0.8362 |
#### 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 | 0.7707 |
| cosine_accuracy@3 | 0.8823 |
| cosine_accuracy@5 | 0.9016 |
| cosine_accuracy@10 | 0.9191 |
| cosine_precision@1 | 0.7707 |
| cosine_precision@3 | 0.2941 |
| cosine_precision@5 | 0.1803 |
| cosine_precision@10 | 0.0919 |
| cosine_recall@1 | 0.7707 |
| cosine_recall@3 | 0.8823 |
| cosine_recall@5 | 0.9016 |
| cosine_recall@10 | 0.9191 |
| **cosine_ndcg@10** | **0.8512** |
| cosine_mrr@10 | 0.8287 |
| cosine_map@100 | 0.8303 |
#### 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 | 0.7604 |
| cosine_accuracy@3 | 0.869 |
| cosine_accuracy@5 | 0.8902 |
| cosine_accuracy@10 | 0.9131 |
| cosine_precision@1 | 0.7604 |
| cosine_precision@3 | 0.2897 |
| cosine_precision@5 | 0.178 |
| cosine_precision@10 | 0.0913 |
| cosine_recall@1 | 0.7604 |
| cosine_recall@3 | 0.869 |
| cosine_recall@5 | 0.8902 |
| cosine_recall@10 | 0.9131 |
| **cosine_ndcg@10** | **0.8415** |
| cosine_mrr@10 | 0.8181 |
| cosine_map@100 | 0.82 |
#### 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 | 0.7248 |
| cosine_accuracy@3 | 0.8521 |
| cosine_accuracy@5 | 0.8751 |
| cosine_accuracy@10 | 0.8974 |
| cosine_precision@1 | 0.7248 |
| cosine_precision@3 | 0.284 |
| cosine_precision@5 | 0.175 |
| cosine_precision@10 | 0.0897 |
| cosine_recall@1 | 0.7248 |
| cosine_recall@3 | 0.8521 |
| cosine_recall@5 | 0.8751 |
| cosine_recall@10 | 0.8974 |
| **cosine_ndcg@10** | **0.8182** |
| cosine_mrr@10 | 0.792 |
| cosine_map@100 | 0.7938 |
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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: 25.88 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 34.09 tokens</li><li>max: 340 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
| <code>En Valdeconejos, ¿cuál era la sociedad de agricultores en 1952?</code> | <code>En Valdeconejos, la sociedad de agricultores en 1952 era el Pósito de Agricultores.</code> |
| <code>¿Qué nombres de capataces se registran en el pueblo de Escucha en el año 1952?</code> | <code>En Escucha, en 1952, los capataces registrados son Peralta (Manuel) y Rodriguez (Gonzalo).</code> |
| <code>En el contexto de la minería, ¿qué implica 'despajar'?</code> | <code>'Despajar' se refiere a cribar a mano material y desechos para obtener las partes de carbón que hay en ellos.</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`: 12
- `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`: 12
- `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 | - | 0.7663 | 0.7676 | 0.7656 | 0.7626 | 0.7393 | 0.6969 |
| 1.2747 | 10 | 127.0406 | - | - | - | - | - | - |
| 2.0 | 16 | - | 0.8244 | 0.8240 | 0.8226 | 0.8172 | 0.8060 | 0.7775 |
| 2.5494 | 20 | 38.8995 | - | - | - | - | - | - |
| 3.0 | 24 | - | 0.8425 | 0.8426 | 0.8444 | 0.8373 | 0.8252 | 0.7996 |
| 3.8240 | 30 | 20.1528 | - | - | - | - | - | - |
| 4.0 | 32 | - | 0.8526 | 0.8520 | 0.8498 | 0.8456 | 0.8289 | 0.8037 |
| 5.0 | 40 | 14.0513 | 0.8550 | 0.8543 | 0.8517 | 0.8490 | 0.8368 | 0.8139 |
| 6.0 | 48 | - | 0.8572 | 0.8565 | 0.8557 | 0.8520 | 0.8404 | 0.8170 |
| 6.2747 | 50 | 13.364 | - | - | - | - | - | - |
| 7.0 | 56 | - | 0.8579 | 0.8576 | 0.8553 | 0.8514 | 0.8422 | 0.8180 |
| 7.5494 | 60 | 12.7986 | - | - | - | - | - | - |
| 8.0 | 64 | - | 0.8573 | 0.8580 | 0.8560 | 0.8523 | 0.8414 | 0.8178 |
| 8.8240 | 70 | 12.0091 | - | - | - | - | - | - |
| 9.0 | 72 | - | 0.8578 | 0.8586 | 0.8562 | 0.8519 | 0.8423 | 0.8184 |
| 10.0 | 80 | 10.9468 | 0.8583 | 0.8589 | 0.8565 | 0.8530 | 0.8413 | 0.8191 |
| 10.5494 | 84 | - | 0.8576 | 0.8588 | 0.8564 | 0.8512 | 0.8415 | 0.8182 |
### 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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