|
--- |
|
language: |
|
- es |
|
license: apache-2.0 |
|
tags: |
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- 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. |
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sentences: |
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- 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. |
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- El 'rosario de candiles' es una tradición religiosa celebrada en la festividad |
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de San Juan, en la que los mineros escuchan y acompañan con sus candiles de carburo, |
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rezando a dos coros y cantando en parte. |
|
- source_sentence: ¿Qué significa la expresión 'pillar una mojadina'? |
|
sentences: |
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- 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 |
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a CC.PP. ya que era necesario que la plantilla de la empresa superase el número |
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de 50 trabajadores.. |
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- '''Pillar una mojadina'' significa empaparse, quedar empapado.' |
|
- source_sentence: ¿En qué año Carbones de Teruel registra la mina 'pablo' en Escucha? |
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sentences: |
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- Puede referirse a un calcetín para bebés o a un calcetín gordo. |
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- Carbones de Teruel registra la mina 'pablo' en Escucha en 1900. |
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- 'Jesús Conesa explicó a la Junta de Espectáculos que el anterior propietario, |
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Sr. Latorre Galindo, tenía otro cine en Utrillas, lo que causaba continuos equívocos |
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en envíos de material y pagos, al creerse que ambos cines le pertenecían o eran |
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la misma empresa. ' |
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- source_sentence: ¿Quién regentaba el Cine Avenida de Escucha en el momento de su |
|
cierre? |
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sentences: |
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- Se usa con el significado de 'cuando'. |
|
- El CD Escucha alineó a Castillo, Romero, Bobadilla, Moraleda, Luis, González, |
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Higinio, Torres, Calomarde I, Calomarde II y Navarro en el partido de Copa contra |
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el Alcorisa. |
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- Antonio Malpica regentaba el Cine Avenida de Escucha en el momento de su cierre. |
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- 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: |
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- 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. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
|
metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
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- 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 | |
|
|
|
<!-- |
|
## 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.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
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|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
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