sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:4173 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss ca arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base base_model:finetune:projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
datasets: []
language:
- ca
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:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Queixa: Deixar constància de la vostra disconformitat per un mal
    servei (un tracte inapropiat, un temps d''espera excessiu, etc.), sense demanar
    cap indemnització.'
  sentences:
  - Quin és el format de sortida del tràmit de baixa de la llicència de gual?
  - Quin és el tipus de venda que es realitza en els mercats setmanals?
  - Quin és el paper de la queixa en la resolució de conflictes?
- source_sentence: L'empleat que en l'exercici de les seves tasques tingui assignada
    la funció de conducció de vehicles municipals, pot sol·licitar un ajut per les
    despeses ocasionades per a la renovació del carnet de conduir (certificat mèdic
    i administratiu).
  sentences:
  - Quin és el resultat esperat de les escoles que reben les subvencions?
  - Quin és el requisit per obtenir una autorització d'estacionament?
  - Quin és el requisit per a sol·licitar l'ajut social?
- source_sentence: Aportació de documentació. Subvencions per finançar despeses d'hipoteca,
    subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats
    culturals
  sentences:
  - Quin és el propòsit de la documentació?
  - Quin és el paper del públic assistent en el Ple Municipal?
  - Quin és el paper de l'ajuntament en la renovació del carnet de persona cuidadora?
- source_sentence: la Fira de la Vila del Llibre de Sitges consistent en un conjunt
    de parades instal·lades al Passeig Marítim
  sentences:
  - Quin és el paper de la llicència de parcel·lació en la construcció d'edificacions?
  - Quin és l'objectiu del tràmit de participació en processos de selecció de personal
    de l'Ajuntament?
  - Quin és el lloc on es desenvolupa la Fira de la Vila del Llibre de Sitges?
- source_sentence: Mitjançant aquest tràmit la persona interessada posa en coneixement
    de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa
    de caràcter extraordinari...
  sentences:
  - Quin és el paper de la persona interessada en la llicència per a espectacles públics
    o activitats recreatives de caràcter extraordinari?
  - Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial
    en la gestió d'habitatges?
  - Quin és el tipus de familiars que es tenen en compte per l'ajut especial?
model-index:
- name: BGE SITGES  CAT
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.07327586206896551
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.15732758620689655
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21767241379310345
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.39439655172413796
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.07327586206896551
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05244252873563218
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.043534482758620686
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03943965517241379
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.07327586206896551
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15732758620689655
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21767241379310345
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.39439655172413796
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20125893142070614
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14385604816639316
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.17098930660026063
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.07327586206896551
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.15086206896551724
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21767241379310345
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.39439655172413796
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.07327586206896551
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.050287356321839075
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04353448275862069
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03943965517241379
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.07327586206896551
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15086206896551724
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21767241379310345
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.39439655172413796
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2016207682773376
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14438799945265474
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1715919733142084
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.07327586206896551
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.14870689655172414
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21120689655172414
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.40086206896551724
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.07327586206896551
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04956896551724138
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04224137931034483
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04008620689655173
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.07327586206896551
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.14870689655172414
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21120689655172414
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.40086206896551724
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2021149795452301
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1433856732348113
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16973847535400444
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.06896551724137931
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.14655172413793102
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21767241379310345
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.38146551724137934
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06896551724137931
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.048850574712643674
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04353448275862069
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03814655172413793
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06896551724137931
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.14655172413793102
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21767241379310345
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.38146551724137934
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19535554125135882
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1398416119321293
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16597320243564267
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.05603448275862069
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.13793103448275862
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1939655172413793
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.36853448275862066
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05603448275862069
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04597701149425287
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03879310344827586
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03685344827586207
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05603448275862069
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.13793103448275862
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1939655172413793
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.36853448275862066
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18225870966588442
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.12688492063492074
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.15425908300208627
      name: Cosine Map@100
---

# BGE SITGES  CAT

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base). It maps sentences & paragraphs to a 768-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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) <!-- at revision 3354aea2cb9d91091495e9f1e1241b488f32e47c -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** ca
- **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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/SITGES-aina4")
# Run inference
sentences = [
    "Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa de caràcter extraordinari...",
    'Quin és el paper de la persona interessada en la llicència per a espectacles públics o activitats recreatives de caràcter extraordinari?',
    "Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial en la gestió d'habitatges?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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_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   | 0.0733    |
| cosine_accuracy@3   | 0.1573    |
| cosine_accuracy@5   | 0.2177    |
| cosine_accuracy@10  | 0.3944    |
| cosine_precision@1  | 0.0733    |
| cosine_precision@3  | 0.0524    |
| cosine_precision@5  | 0.0435    |
| cosine_precision@10 | 0.0394    |
| cosine_recall@1     | 0.0733    |
| cosine_recall@3     | 0.1573    |
| cosine_recall@5     | 0.2177    |
| cosine_recall@10    | 0.3944    |
| cosine_ndcg@10      | 0.2013    |
| cosine_mrr@10       | 0.1439    |
| **cosine_map@100**  | **0.171** |

#### 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   | 0.0733     |
| cosine_accuracy@3   | 0.1509     |
| cosine_accuracy@5   | 0.2177     |
| cosine_accuracy@10  | 0.3944     |
| cosine_precision@1  | 0.0733     |
| cosine_precision@3  | 0.0503     |
| cosine_precision@5  | 0.0435     |
| cosine_precision@10 | 0.0394     |
| cosine_recall@1     | 0.0733     |
| cosine_recall@3     | 0.1509     |
| cosine_recall@5     | 0.2177     |
| cosine_recall@10    | 0.3944     |
| cosine_ndcg@10      | 0.2016     |
| cosine_mrr@10       | 0.1444     |
| **cosine_map@100**  | **0.1716** |

#### 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   | 0.0733     |
| cosine_accuracy@3   | 0.1487     |
| cosine_accuracy@5   | 0.2112     |
| cosine_accuracy@10  | 0.4009     |
| cosine_precision@1  | 0.0733     |
| cosine_precision@3  | 0.0496     |
| cosine_precision@5  | 0.0422     |
| cosine_precision@10 | 0.0401     |
| cosine_recall@1     | 0.0733     |
| cosine_recall@3     | 0.1487     |
| cosine_recall@5     | 0.2112     |
| cosine_recall@10    | 0.4009     |
| cosine_ndcg@10      | 0.2021     |
| cosine_mrr@10       | 0.1434     |
| **cosine_map@100**  | **0.1697** |

#### 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   | 0.069     |
| cosine_accuracy@3   | 0.1466    |
| cosine_accuracy@5   | 0.2177    |
| cosine_accuracy@10  | 0.3815    |
| cosine_precision@1  | 0.069     |
| cosine_precision@3  | 0.0489    |
| cosine_precision@5  | 0.0435    |
| cosine_precision@10 | 0.0381    |
| cosine_recall@1     | 0.069     |
| cosine_recall@3     | 0.1466    |
| cosine_recall@5     | 0.2177    |
| cosine_recall@10    | 0.3815    |
| cosine_ndcg@10      | 0.1954    |
| cosine_mrr@10       | 0.1398    |
| **cosine_map@100**  | **0.166** |

#### 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   | 0.056      |
| cosine_accuracy@3   | 0.1379     |
| cosine_accuracy@5   | 0.194      |
| cosine_accuracy@10  | 0.3685     |
| cosine_precision@1  | 0.056      |
| cosine_precision@3  | 0.046      |
| cosine_precision@5  | 0.0388     |
| cosine_precision@10 | 0.0369     |
| cosine_recall@1     | 0.056      |
| cosine_recall@3     | 0.1379     |
| cosine_recall@5     | 0.194      |
| cosine_recall@10    | 0.3685     |
| cosine_ndcg@10      | 0.1823     |
| cosine_mrr@10       | 0.1269     |
| **cosine_map@100**  | **0.1543** |

<!--
## Bias, Risks and Limitations

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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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3065     | 5      | 3.3947        | -          | -                      | -                      | -                      | -                     | -                      |
| 0.6130     | 10     | 2.6401        | -          | -                      | -                      | -                      | -                     | -                      |
| 0.9195     | 15     | 2.0152        | -          | -                      | -                      | -                      | -                     | -                      |
| 0.9808     | 16     | -             | 1.3404     | 0.1639                 | 0.1577                 | 0.1694                 | 0.1503                | 0.1638                 |
| 1.2261     | 20     | 1.4542        | -          | -                      | -                      | -                      | -                     | -                      |
| 1.5326     | 25     | 1.0135        | -          | -                      | -                      | -                      | -                     | -                      |
| 1.8391     | 30     | 0.8437        | -          | -                      | -                      | -                      | -                     | -                      |
| 1.9617     | 32     | -             | 0.9436     | 0.1556                 | 0.1596                 | 0.1600                 | 0.1467                | 0.1701                 |
| 2.1456     | 35     | 0.7676        | -          | -                      | -                      | -                      | -                     | -                      |
| 2.4521     | 40     | 0.5126        | -          | -                      | -                      | -                      | -                     | -                      |
| 2.7586     | 45     | 0.4358        | -          | -                      | -                      | -                      | -                     | -                      |
| 2.9425     | 48     | -             | 0.7852     | 0.1650                 | 0.1693                 | 0.1720                 | 0.1511                | 0.1686                 |
| 3.0651     | 50     | 0.4192        | -          | -                      | -                      | -                      | -                     | -                      |
| 3.3716     | 55     | 0.3429        | -          | -                      | -                      | -                      | -                     | -                      |
| 3.6782     | 60     | 0.3025        | -          | -                      | -                      | -                      | -                     | -                      |
| 3.9847     | 65     | 0.2863        | 0.7401     | 0.1646                 | 0.1706                 | 0.1759                 | 0.1480                | 0.1694                 |
| 4.2912     | 70     | 0.2474        | -          | -                      | -                      | -                      | -                     | -                      |
| 4.5977     | 75     | 0.2324        | -          | -                      | -                      | -                      | -                     | -                      |
| 4.9042     | 80     | 0.2344        | -          | -                      | -                      | -                      | -                     | -                      |
| 4.9655     | 81     | -             | 0.7217     | 0.1663                 | 0.1699                 | 0.1767                 | 0.1512                | 0.1696                 |
| 5.2107     | 85     | 0.2181        | -          | -                      | -                      | -                      | -                     | -                      |
| 5.5172     | 90     | 0.2116        | -          | -                      | -                      | -                      | -                     | -                      |
| 5.8238     | 95     | 0.1926        | -          | -                      | -                      | -                      | -                     | -                      |
| **5.8851** | **96** | **-**         | **0.7154** | **0.166**              | **0.1697**             | **0.1716**             | **0.1543**            | **0.171**              |

* 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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