sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:81 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss es arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:81
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Disposeu del servei OAC360º d'assistència en la tramitació electrònica
    amb el que podeu contactar de dilluns a divendres de 08:00 a 20:00 a través del
    tel. 935 955 094, del correu oac360@sitges.cat, o del servei Truca'm, omplint
    el formulari perquè us truquin.
  sentences:
  - Com es pot demanar la comunicació prèvia d'obres per instal·lacions de plaques
    solars en sol urbà?
  - Quin és el correu electrònic per contactar amb el servei OAC360º?
  - Quin és l'efecte del silenci administratiu?
- source_sentence: Positiu, llevat els casos en els quals manquin informes preceptius
    i vinculants d’altres administracions o d’aquells en els què es transfereixin
    al sol·licitant facultats contràries al planejament i la legislació urbanística.
  sentences:
  - Quin és el document que cal aportar per a aquest tràmit?
  - Quin és el lloc on es pot tramitar la presentació de justificants de pagament
    per als ajuts del lloguer just dels habitatges?
  - Quin és el sentit del silenci administratiu per a la comunicació prèvia d'obres
    per instal·lacions de plaques solars en sol urbà?
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: 0.1111111111111111
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4444444444444444
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7777777777777778
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1111111111111111
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08888888888888889
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07777777777777778
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1111111111111111
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4444444444444444
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7777777777777778
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.37561164042849293
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2550705467372134
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.26453109424123916
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.1111111111111111
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4444444444444444
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7777777777777778
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1111111111111111
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08888888888888889
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07777777777777778
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1111111111111111
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4444444444444444
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7777777777777778
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.37561164042849293
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2550705467372134
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.26591710758377424
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.1111111111111111
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4444444444444444
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7777777777777778
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1111111111111111
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08888888888888889
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07777777777777778
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1111111111111111
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4444444444444444
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7777777777777778
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.36941287151905455
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.24828042328042324
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.25912698412698415
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.1111111111111111
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4444444444444444
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6666666666666666
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1111111111111111
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08888888888888889
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06666666666666668
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1111111111111111
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4444444444444444
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6666666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.33724514013077883
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23796296296296296
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2553057025279247
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.1111111111111111
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5555555555555556
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7777777777777778
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1111111111111111
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1111111111111111
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07777777777777778
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1111111111111111
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5555555555555556
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7777777777777778
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3920021980903836
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.27248677248677244
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2795432240996757
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.2222222222222222
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4444444444444444
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5555555555555556
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2222222222222222
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08888888888888889
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05555555555555555
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2222222222222222
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4444444444444444
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5555555555555556
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3626677657118585
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3029100529100529
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.32598958775429365
      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("adriansanz/bge-m3-es-legal-tmp-6")
# Run inference
sentences = [
    'Positiu, llevat els casos en els quals manquin informes preceptius i vinculants d’altres administracions o d’aquells en els què es transfereixin al sol·licitant facultats contràries al planejament i la legislació urbanística.',
    "Quin és el sentit del silenci administratiu per a la comunicació prèvia d'obres per instal·lacions de plaques solars en sol urbà?",
    'Quin és el lloc on es pot tramitar la presentació de justificants de pagament per als ajuts del lloguer just dels habitatges?',
]
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.*
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## 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   | 0.1111     |
| cosine_accuracy@3   | 0.3333     |
| cosine_accuracy@5   | 0.4444     |
| cosine_accuracy@10  | 0.7778     |
| cosine_precision@1  | 0.1111     |
| cosine_precision@3  | 0.1111     |
| cosine_precision@5  | 0.0889     |
| cosine_precision@10 | 0.0778     |
| cosine_recall@1     | 0.1111     |
| cosine_recall@3     | 0.3333     |
| cosine_recall@5     | 0.4444     |
| cosine_recall@10    | 0.7778     |
| cosine_ndcg@10      | 0.3756     |
| cosine_mrr@10       | 0.2551     |
| **cosine_map@100**  | **0.2645** |

#### 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   | 0.1111     |
| cosine_accuracy@3   | 0.3333     |
| cosine_accuracy@5   | 0.4444     |
| cosine_accuracy@10  | 0.7778     |
| cosine_precision@1  | 0.1111     |
| cosine_precision@3  | 0.1111     |
| cosine_precision@5  | 0.0889     |
| cosine_precision@10 | 0.0778     |
| cosine_recall@1     | 0.1111     |
| cosine_recall@3     | 0.3333     |
| cosine_recall@5     | 0.4444     |
| cosine_recall@10    | 0.7778     |
| cosine_ndcg@10      | 0.3756     |
| cosine_mrr@10       | 0.2551     |
| **cosine_map@100**  | **0.2659** |

#### 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   | 0.1111     |
| cosine_accuracy@3   | 0.3333     |
| cosine_accuracy@5   | 0.4444     |
| cosine_accuracy@10  | 0.7778     |
| cosine_precision@1  | 0.1111     |
| cosine_precision@3  | 0.1111     |
| cosine_precision@5  | 0.0889     |
| cosine_precision@10 | 0.0778     |
| cosine_recall@1     | 0.1111     |
| cosine_recall@3     | 0.3333     |
| cosine_recall@5     | 0.4444     |
| cosine_recall@10    | 0.7778     |
| cosine_ndcg@10      | 0.3694     |
| cosine_mrr@10       | 0.2483     |
| **cosine_map@100**  | **0.2591** |

#### 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   | 0.1111     |
| cosine_accuracy@3   | 0.3333     |
| cosine_accuracy@5   | 0.4444     |
| cosine_accuracy@10  | 0.6667     |
| cosine_precision@1  | 0.1111     |
| cosine_precision@3  | 0.1111     |
| cosine_precision@5  | 0.0889     |
| cosine_precision@10 | 0.0667     |
| cosine_recall@1     | 0.1111     |
| cosine_recall@3     | 0.3333     |
| cosine_recall@5     | 0.4444     |
| cosine_recall@10    | 0.6667     |
| cosine_ndcg@10      | 0.3372     |
| cosine_mrr@10       | 0.238      |
| **cosine_map@100**  | **0.2553** |

#### 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   | 0.1111     |
| cosine_accuracy@3   | 0.3333     |
| cosine_accuracy@5   | 0.5556     |
| cosine_accuracy@10  | 0.7778     |
| cosine_precision@1  | 0.1111     |
| cosine_precision@3  | 0.1111     |
| cosine_precision@5  | 0.1111     |
| cosine_precision@10 | 0.0778     |
| cosine_recall@1     | 0.1111     |
| cosine_recall@3     | 0.3333     |
| cosine_recall@5     | 0.5556     |
| cosine_recall@10    | 0.7778     |
| cosine_ndcg@10      | 0.392      |
| cosine_mrr@10       | 0.2725     |
| **cosine_map@100**  | **0.2795** |

#### 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   | 0.2222    |
| cosine_accuracy@3   | 0.3333    |
| cosine_accuracy@5   | 0.4444    |
| cosine_accuracy@10  | 0.5556    |
| cosine_precision@1  | 0.2222    |
| cosine_precision@3  | 0.1111    |
| cosine_precision@5  | 0.0889    |
| cosine_precision@10 | 0.0556    |
| cosine_recall@1     | 0.2222    |
| cosine_recall@3     | 0.3333    |
| cosine_recall@5     | 0.4444    |
| cosine_recall@10    | 0.5556    |
| cosine_ndcg@10      | 0.3627    |
| cosine_mrr@10       | 0.3029    |
| **cosine_map@100**  | **0.326** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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`: 6
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `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`: 6
- `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`: False
- `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 |
|:-------:|:-----:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 1.0     | 1     | -             | 3.7675     | 0.2475                  | 0.2919                 | 0.2372                 | 0.2511                 | 0.2510                | 0.2468                 |
| **2.0** | **2** | **-**         | **3.9701** | **0.2533**              | **0.3028**             | **0.2473**             | **0.2601**             | **0.3449**            | **0.2716**             |
| 3.0     | 4     | -             | 4.1211     | 0.2645                  | 0.2704                 | 0.2548                 | 0.2614                 | 0.3283                | 0.2654                 |
| 4.0     | 5     | 1.8359        | 4.0228     | 0.2645                  | 0.2789                 | 0.2553                 | 0.2619                 | 0.3260                | 0.2659                 |
| 5.0     | 6     | -             | 3.9758     | 0.2645                  | 0.2795                 | 0.2553                 | 0.2591                 | 0.3260                | 0.2659                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.1+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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