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---
base_model: BAAI/bge-m3
datasets: []
language:
- es
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2947
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Es uso privativo el que determina la ocupación de una porción del
dominio público, de modo que se limita o excluye la utilización del mismo por
otros interesados.
sentences:
- ¿Qué es el uso privativo de los bienes de dominio público?
- ¿Qué es la sanidad ambiental?
- ¿Qué información básica debe contener la información que se facilita al afectado
cuando se obtienen datos personales de él?
- source_sentence: 'Las retribuciones básicas, que se fijan en la Ley de Presupuestos
Generales del Estado, estarán integradas única y exclusivamente por: a) El sueldo
asignado a cada Subgrupo o Grupo de clasificación profesional, en el supuesto
de que éste no tenga Subgrupo. b) Los trienios, que consisten en una cantidad,
que será igual para cada Subgrupo o Grupo de clasificación profesional, en el
supuesto de que éste no tenga Subgrupo, por cada tres años de servicio.'
sentences:
- ¿Qué se entiende por retribuciones básicas?
- ¿Cuál es el título competencial de esta ley orgánica?
- ¿Qué se aprueba a propuesta del Ministro de Hacienda?
- source_sentence: Se reconoce el valor social de las niñas, niños y adolescentes
como personas que realizan un aporte afectivo, cultural y ético al caudal social,
y cuyo protagonismo, creatividad y posicionamiento activo enriquecen la vida colectiva.
sentences:
- ¿Qué sucede si se produce un incumplimiento de las actuaciones establecidas en
el Plan de inclusión sociolaboral?
- ¿Qué se reconoce en cuanto al valor social de la infancia?
- ¿Cuál es el plazo de prescripción de las infracciones?
- source_sentence: Las empresas y las universidades podrán promover y participar en
programas de voluntariado que cumplan los requisitos establecidos en esta Ley.
sentences:
- ¿Cuál es la consideración de las infracciones muy graves?
- ¿Qué tipo de empresas pueden promover y participar en programas de voluntariado?
- ¿Qué tipo de entidades están obligadas a cumplir con las obligaciones de publicidad
activa?
- source_sentence: Artículo 6. Definiciones. 1. Discriminación directa e indirecta.
b) La discriminación indirecta se produce cuando una disposición, criterio o práctica
aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja
particular con respecto a otras por razón de las causas previstas en el apartado
1 del artículo 2.
sentences:
- ¿Cuál es el papel del Consejo de Salud de Área?
- ¿Qué se considera discriminación indirecta?
- ¿Qué tipo de información se considera veraz?
model-index:
- name: BGE large Legal Spanish
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.5426829268292683
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7987804878048781
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8384146341463414
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8871951219512195
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5426829268292683
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.266260162601626
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16768292682926828
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08871951219512193
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5426829268292683
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7987804878048781
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8384146341463414
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8871951219512195
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7232630895931937
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6696029326364694
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6746421405883097
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.5396341463414634
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8048780487804879
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8445121951219512
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8902439024390244
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5396341463414634
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2682926829268293
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16890243902439023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08902439024390242
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5396341463414634
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8048780487804879
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8445121951219512
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8902439024390244
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7245682830632947
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6701642953929542
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6749054080636328
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.5487804878048781
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.801829268292683
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8353658536585366
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8932926829268293
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5487804878048781
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26727642276422764
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1670731707317073
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08932926829268292
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5487804878048781
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.801829268292683
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8353658536585366
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8932926829268293
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7304163166331036
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6771317266744099
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6810536400270114
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.5457317073170732
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7774390243902439
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8292682926829268
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8719512195121951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5457317073170732
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25914634146341464
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16585365853658537
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0871951219512195
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5457317073170732
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7774390243902439
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8292682926829268
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8719512195121951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7182651883104234
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.667831736353078
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6733111746390299
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.5335365853658537
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7621951219512195
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8140243902439024
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8658536585365854
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5335365853658537
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25406504065040647
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16280487804878047
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08658536585365852
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5335365853658537
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7621951219512195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8140243902439024
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8658536585365854
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7079855810333241
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6563213801780877
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6616757296099581
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.5121951219512195
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7317073170731707
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7896341463414634
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8658536585365854
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5121951219512195
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24390243902439024
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15792682926829266
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08658536585365853
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5121951219512195
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7317073170731707
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7896341463414634
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8658536585365854
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6907536996968978
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6346544715447154
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6393928977007713
name: Cosine Map@100
---
# BGE large Legal Spanish
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** es
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("dariolopez/bge-m3-es-legal-tmp-4")
# Run inference
sentences = [
'Artículo 6. Definiciones. 1. Discriminación directa e indirecta. b) La discriminación indirecta se produce cuando una disposición, criterio o práctica aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja particular con respecto a otras por razón de las causas previstas en el apartado 1 del artículo 2.',
'¿Qué se considera discriminación indirecta?',
'¿Qué tipo de información se considera veraz?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5427 |
| cosine_accuracy@3 | 0.7988 |
| cosine_accuracy@5 | 0.8384 |
| cosine_accuracy@10 | 0.8872 |
| cosine_precision@1 | 0.5427 |
| cosine_precision@3 | 0.2663 |
| cosine_precision@5 | 0.1677 |
| cosine_precision@10 | 0.0887 |
| cosine_recall@1 | 0.5427 |
| cosine_recall@3 | 0.7988 |
| cosine_recall@5 | 0.8384 |
| cosine_recall@10 | 0.8872 |
| cosine_ndcg@10 | 0.7233 |
| cosine_mrr@10 | 0.6696 |
| **cosine_map@100** | **0.6746** |
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5396 |
| cosine_accuracy@3 | 0.8049 |
| cosine_accuracy@5 | 0.8445 |
| cosine_accuracy@10 | 0.8902 |
| cosine_precision@1 | 0.5396 |
| cosine_precision@3 | 0.2683 |
| cosine_precision@5 | 0.1689 |
| cosine_precision@10 | 0.089 |
| cosine_recall@1 | 0.5396 |
| cosine_recall@3 | 0.8049 |
| cosine_recall@5 | 0.8445 |
| cosine_recall@10 | 0.8902 |
| cosine_ndcg@10 | 0.7246 |
| cosine_mrr@10 | 0.6702 |
| **cosine_map@100** | **0.6749** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5488 |
| cosine_accuracy@3 | 0.8018 |
| cosine_accuracy@5 | 0.8354 |
| cosine_accuracy@10 | 0.8933 |
| cosine_precision@1 | 0.5488 |
| cosine_precision@3 | 0.2673 |
| cosine_precision@5 | 0.1671 |
| cosine_precision@10 | 0.0893 |
| cosine_recall@1 | 0.5488 |
| cosine_recall@3 | 0.8018 |
| cosine_recall@5 | 0.8354 |
| cosine_recall@10 | 0.8933 |
| cosine_ndcg@10 | 0.7304 |
| cosine_mrr@10 | 0.6771 |
| **cosine_map@100** | **0.6811** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5457 |
| cosine_accuracy@3 | 0.7774 |
| cosine_accuracy@5 | 0.8293 |
| cosine_accuracy@10 | 0.872 |
| cosine_precision@1 | 0.5457 |
| cosine_precision@3 | 0.2591 |
| cosine_precision@5 | 0.1659 |
| cosine_precision@10 | 0.0872 |
| cosine_recall@1 | 0.5457 |
| cosine_recall@3 | 0.7774 |
| cosine_recall@5 | 0.8293 |
| cosine_recall@10 | 0.872 |
| cosine_ndcg@10 | 0.7183 |
| cosine_mrr@10 | 0.6678 |
| **cosine_map@100** | **0.6733** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5335 |
| cosine_accuracy@3 | 0.7622 |
| cosine_accuracy@5 | 0.814 |
| cosine_accuracy@10 | 0.8659 |
| cosine_precision@1 | 0.5335 |
| cosine_precision@3 | 0.2541 |
| cosine_precision@5 | 0.1628 |
| cosine_precision@10 | 0.0866 |
| cosine_recall@1 | 0.5335 |
| cosine_recall@3 | 0.7622 |
| cosine_recall@5 | 0.814 |
| cosine_recall@10 | 0.8659 |
| cosine_ndcg@10 | 0.708 |
| cosine_mrr@10 | 0.6563 |
| **cosine_map@100** | **0.6617** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5122 |
| cosine_accuracy@3 | 0.7317 |
| cosine_accuracy@5 | 0.7896 |
| cosine_accuracy@10 | 0.8659 |
| cosine_precision@1 | 0.5122 |
| cosine_precision@3 | 0.2439 |
| cosine_precision@5 | 0.1579 |
| cosine_precision@10 | 0.0866 |
| cosine_recall@1 | 0.5122 |
| cosine_recall@3 | 0.7317 |
| cosine_recall@5 | 0.7896 |
| cosine_recall@10 | 0.8659 |
| cosine_ndcg@10 | 0.6908 |
| cosine_mrr@10 | 0.6347 |
| **cosine_map@100** | **0.6394** |
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 16
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 16
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:---------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4324 | 5 | 1.6932 | - | - | - | - | - | - | - |
| 0.8649 | 10 | 1.1787 | - | - | - | - | - | - | - |
| 0.9514 | 11 | - | 0.6685 | 0.6708 | 0.6300 | 0.6676 | 0.6716 | 0.5560 | 0.6781 |
| 1.2973 | 15 | 1.0084 | - | - | - | - | - | - | - |
| 1.7297 | 20 | 0.5743 | - | - | - | - | - | - | - |
| 1.9892 | 23 | - | 0.4458 | 0.6734 | 0.6533 | 0.6773 | 0.6770 | 0.6174 | 0.6657 |
| 2.1622 | 25 | 0.4435 | - | - | - | - | - | - | - |
| 2.5946 | 30 | 0.2396 | - | - | - | - | - | - | - |
| 2.9405 | 34 | - | 0.4239 | 0.6749 | 0.6591 | 0.6725 | 0.6752 | 0.6188 | 0.6784 |
| 3.0270 | 35 | 0.1568 | - | - | - | - | - | - | - |
| 3.4595 | 40 | 0.1085 | - | - | - | - | - | - | - |
| 3.8919 | 45 | 0.0582 | - | - | - | - | - | - | - |
| 3.9784 | 46 | - | 0.3934 | 0.6820 | 0.6594 | 0.6862 | 0.6856 | 0.6293 | 0.6777 |
| 4.3243 | 50 | 0.0543 | - | - | - | - | - | - | - |
| 4.7568 | 55 | 0.0349 | - | - | - | - | - | - | - |
| 4.9297 | 57 | - | 0.3690 | 0.6747 | 0.6582 | 0.6760 | 0.6852 | 0.6375 | 0.6774 |
| 5.1892 | 60 | 0.03 | - | - | - | - | - | - | - |
| 5.6216 | 65 | 0.0228 | - | - | - | - | - | - | - |
| **5.9676** | **69** | **-** | **0.362** | **0.6752** | **0.6643** | **0.6784** | **0.6809** | **0.6312** | **0.6799** |
| 6.0541 | 70 | 0.0183 | - | - | - | - | - | - | - |
| 6.4865 | 75 | 0.0159 | - | - | - | - | - | - | - |
| 6.9189 | 80 | 0.0113 | 0.3608 | 0.6780 | 0.6582 | 0.6769 | 0.6785 | 0.6366 | 0.6769 |
| 7.3514 | 85 | 0.0107 | - | - | - | - | - | - | - |
| 7.7838 | 90 | 0.0098 | - | - | - | - | - | - | - |
| 7.9568 | 92 | - | 0.3307 | 0.6804 | 0.6511 | 0.6774 | 0.6823 | 0.6355 | 0.6747 |
| 8.2162 | 95 | 0.0084 | - | - | - | - | - | - | - |
| 8.6486 | 100 | 0.0067 | - | - | - | - | - | - | - |
| 8.9946 | 104 | - | 0.3387 | 0.6778 | 0.6518 | 0.6751 | 0.6787 | 0.6313 | 0.6693 |
| 9.0811 | 105 | 0.0074 | - | - | - | - | - | - | - |
| 9.5135 | 110 | 0.0064 | - | - | - | - | - | - | - |
| 9.9459 | 115 | 0.0052 | 0.3222 | 0.6776 | 0.6571 | 0.6745 | 0.6810 | 0.6397 | 0.6722 |
| 10.3784 | 120 | 0.0058 | - | - | - | - | - | - | - |
| 10.8108 | 125 | 0.0058 | - | - | - | - | - | - | - |
| 10.9838 | 127 | - | 0.3325 | 0.6760 | 0.6595 | 0.6714 | 0.6807 | 0.6399 | 0.6729 |
| 11.2432 | 130 | 0.0052 | - | - | - | - | - | - | - |
| 11.6757 | 135 | 0.0046 | - | - | - | - | - | - | - |
| 11.9351 | 138 | - | 0.3366 | 0.6770 | 0.6598 | 0.6730 | 0.6813 | 0.6360 | 0.6733 |
| 12.1081 | 140 | 0.0053 | - | - | - | - | - | - | - |
| 12.5405 | 145 | 0.0046 | - | - | - | - | - | - | - |
| 12.9730 | 150 | 0.0045 | 0.3263 | 0.6759 | 0.6599 | 0.6743 | 0.6816 | 0.6394 | 0.6759 |
| 13.4054 | 155 | 0.0044 | - | - | - | - | - | - | - |
| 13.8378 | 160 | 0.0043 | - | - | - | - | - | - | - |
| 13.9243 | 161 | - | 0.3231 | 0.6747 | 0.6593 | 0.6729 | 0.6804 | 0.6407 | 0.6746 |
| 14.2703 | 165 | 0.005 | - | - | - | - | - | - | - |
| 14.7027 | 170 | 0.004 | - | - | - | - | - | - | - |
| 14.9622 | 173 | - | 0.3238 | 0.6743 | 0.6597 | 0.6720 | 0.6828 | 0.6395 | 0.6759 |
| 15.1351 | 175 | 0.005 | - | - | - | - | - | - | - |
| 15.2216 | 176 | - | 0.3244 | 0.6746 | 0.6617 | 0.6733 | 0.6811 | 0.6394 | 0.6749 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.2.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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