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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: 0.5457317073170732
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7957317073170732
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8384146341463414
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8932926829268293
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5457317073170732
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2652439024390244
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1676829268292683
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08932926829268292
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5457317073170732
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7957317073170732
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8384146341463414
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8932926829268293
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7302586912423743
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6767615176151762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.681258027581737
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.5365853658536586
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8079268292682927
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8414634146341463
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8932926829268293
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5365853658536586
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2693089430894309
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16829268292682925
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08932926829268292
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5365853658536586
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8079268292682927
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8414634146341463
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8932926829268293
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7282267030500372
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6736728126209836
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6781247434270851
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.551829268292683
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8079268292682927
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8475609756097561
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8902439024390244
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.551829268292683
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26930894308943093
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1695121951219512
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08902439024390242
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.551829268292683
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8079268292682927
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8475609756097561
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8902439024390244
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7325574962343641
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6804551393728224
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.684820535249813
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.551829268292683
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7835365853658537
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8384146341463414
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8841463414634146
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.551829268292683
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2611788617886179
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16768292682926828
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08841463414634146
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.551829268292683
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7835365853658537
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8384146341463414
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8841463414634146
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7255160993526271
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6737950058072009
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6784370507793502
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.524390243902439
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7682926829268293
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8201219512195121
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8780487804878049
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.524390243902439
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25609756097560976
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16402439024390242
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08780487804878048
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.524390243902439
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7682926829268293
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8201219512195121
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8780487804878049
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7090498868459102
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6541049651567944
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6583146749893706
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.5030487804878049
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.725609756097561
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7896341463414634
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8567073170731707
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5030487804878049
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24186991869918703
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15792682926829268
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08567073170731705
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5030487804878049
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.725609756097561
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7896341463414634
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8567073170731707
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6821717367550763
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6260005323267519
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6310101112679509
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-5")
# 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 | 0.5457 |
| cosine_accuracy@3 | 0.7957 |
| cosine_accuracy@5 | 0.8384 |
| cosine_accuracy@10 | 0.8933 |
| cosine_precision@1 | 0.5457 |
| cosine_precision@3 | 0.2652 |
| cosine_precision@5 | 0.1677 |
| cosine_precision@10 | 0.0893 |
| cosine_recall@1 | 0.5457 |
| cosine_recall@3 | 0.7957 |
| cosine_recall@5 | 0.8384 |
| cosine_recall@10 | 0.8933 |
| cosine_ndcg@10 | 0.7303 |
| cosine_mrr@10 | 0.6768 |
| **cosine_map@100** | **0.6813** |
#### 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 | 0.5366 |
| cosine_accuracy@3 | 0.8079 |
| cosine_accuracy@5 | 0.8415 |
| cosine_accuracy@10 | 0.8933 |
| cosine_precision@1 | 0.5366 |
| cosine_precision@3 | 0.2693 |
| cosine_precision@5 | 0.1683 |
| cosine_precision@10 | 0.0893 |
| cosine_recall@1 | 0.5366 |
| cosine_recall@3 | 0.8079 |
| cosine_recall@5 | 0.8415 |
| cosine_recall@10 | 0.8933 |
| cosine_ndcg@10 | 0.7282 |
| cosine_mrr@10 | 0.6737 |
| **cosine_map@100** | **0.6781** |
#### 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 | 0.5518 |
| cosine_accuracy@3 | 0.8079 |
| cosine_accuracy@5 | 0.8476 |
| cosine_accuracy@10 | 0.8902 |
| cosine_precision@1 | 0.5518 |
| cosine_precision@3 | 0.2693 |
| cosine_precision@5 | 0.1695 |
| cosine_precision@10 | 0.089 |
| cosine_recall@1 | 0.5518 |
| cosine_recall@3 | 0.8079 |
| cosine_recall@5 | 0.8476 |
| cosine_recall@10 | 0.8902 |
| cosine_ndcg@10 | 0.7326 |
| cosine_mrr@10 | 0.6805 |
| **cosine_map@100** | **0.6848** |
#### 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 | 0.5518 |
| cosine_accuracy@3 | 0.7835 |
| cosine_accuracy@5 | 0.8384 |
| cosine_accuracy@10 | 0.8841 |
| cosine_precision@1 | 0.5518 |
| cosine_precision@3 | 0.2612 |
| cosine_precision@5 | 0.1677 |
| cosine_precision@10 | 0.0884 |
| cosine_recall@1 | 0.5518 |
| cosine_recall@3 | 0.7835 |
| cosine_recall@5 | 0.8384 |
| cosine_recall@10 | 0.8841 |
| cosine_ndcg@10 | 0.7255 |
| cosine_mrr@10 | 0.6738 |
| **cosine_map@100** | **0.6784** |
#### 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 | 0.5244 |
| cosine_accuracy@3 | 0.7683 |
| cosine_accuracy@5 | 0.8201 |
| cosine_accuracy@10 | 0.878 |
| cosine_precision@1 | 0.5244 |
| cosine_precision@3 | 0.2561 |
| cosine_precision@5 | 0.164 |
| cosine_precision@10 | 0.0878 |
| cosine_recall@1 | 0.5244 |
| cosine_recall@3 | 0.7683 |
| cosine_recall@5 | 0.8201 |
| cosine_recall@10 | 0.878 |
| cosine_ndcg@10 | 0.709 |
| cosine_mrr@10 | 0.6541 |
| **cosine_map@100** | **0.6583** |
#### 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 | 0.503 |
| cosine_accuracy@3 | 0.7256 |
| cosine_accuracy@5 | 0.7896 |
| cosine_accuracy@10 | 0.8567 |
| cosine_precision@1 | 0.503 |
| cosine_precision@3 | 0.2419 |
| cosine_precision@5 | 0.1579 |
| cosine_precision@10 | 0.0857 |
| cosine_recall@1 | 0.503 |
| cosine_recall@3 | 0.7256 |
| cosine_recall@5 | 0.7896 |
| cosine_recall@10 | 0.8567 |
| cosine_ndcg@10 | 0.6822 |
| cosine_mrr@10 | 0.626 |
| **cosine_map@100** | **0.631** |
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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`: 8
- `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`: 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`: 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 | 1.6729 | - | - | - | - | - | - | - |
| 0.8649 | 10 | 1.0155 | - | - | - | - | - | - | - |
| 0.9514 | 11 | - | 0.5773 | 0.6769 | 0.6526 | 0.6771 | 0.6782 | 0.5960 | 0.6752 |
| 1.2973 | 15 | 0.8661 | - | - | - | - | - | - | - |
| 1.7297 | 20 | 0.4311 | - | - | - | - | - | - | - |
| 1.9892 | 23 | - | 0.4496 | 0.6637 | 0.6494 | 0.6749 | 0.6729 | 0.6203 | 0.6656 |
| 2.1622 | 25 | 0.3745 | - | - | - | - | - | - | - |
| 2.5946 | 30 | 0.19 | - | - | - | - | - | - | - |
| 2.9405 | 34 | - | 0.4119 | 0.6714 | 0.6530 | 0.6777 | 0.6753 | 0.6162 | 0.6746 |
| 3.0270 | 35 | 0.1448 | - | - | - | - | - | - | - |
| 3.4595 | 40 | 0.0926 | - | - | - | - | - | - | - |
| 3.8919 | 45 | 0.0536 | - | - | - | - | - | - | - |
| 3.9784 | 46 | - | 0.3744 | 0.6852 | 0.6585 | 0.6778 | 0.6827 | 0.6273 | 0.6811 |
| 4.3243 | 50 | 0.0583 | - | - | - | - | - | - | - |
| 4.7568 | 55 | 0.0377 | - | - | - | - | - | - | - |
| 4.9297 | 57 | - | 0.3594 | 0.6829 | 0.6523 | 0.6786 | 0.6837 | 0.6302 | 0.6772 |
| 5.1892 | 60 | 0.0401 | - | - | - | - | - | - | - |
| 5.6216 | 65 | 0.0294 | - | - | - | - | - | - | - |
| 5.9676 | 69 | - | 0.3519 | 0.6831 | 0.6567 | 0.6774 | 0.6859 | 0.6329 | 0.6800 |
| 6.0541 | 70 | 0.0288 | - | - | - | - | - | - | - |
| 6.4865 | 75 | 0.0273 | - | - | - | - | - | - | - |
| 6.9189 | 80 | 0.0227 | 0.3513 | 0.6807 | 0.6551 | 0.6757 | 0.6832 | 0.6298 | 0.6781 |
| 7.3514 | 85 | 0.0223 | - | - | - | - | - | - | - |
| **7.6108** | **88** | **-** | **0.3523** | **0.6813** | **0.6583** | **0.6784** | **0.6848** | **0.631** | **0.6781** |
* 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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