---
base_model: BAAI/bge-m3
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:3755
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: En el cas que la persona beneficiària mantingui les condicions
    d’elegibilitat es podrà concedir la pròrroga de la prestació sempre que la persona
    interessada ho sol·liciti i ho permetin les dotacions pressupostàries de cada
    exercici.
  sentences:
  - Quin és el benefici de l'ajut a la consolidació d'empreses?
  - Quin és el requisit per a la persona beneficiària?
  - Quin és el benefici del Registre municipal d'entitats per a l'Ajuntament?
- source_sentence: Aquest tràmit permet la presentació de les sol·licituds per a l’atorgament
    de llicències d’aprofitament especial sense transformació del domini públic marítim
    terrestre consistent en la instal·lació i explotació d'escola per oferir activitats
    nàutiques, amb zona d’avarada, durant la temporada.
  sentences:
  - Quin és el propòsit de la llicència d'aprofitament especial sense transformació
    del domini públic marítim terrestre?
  - Quin és el termini per a presentar les sol·licituds de subvencions per a projectes
    i activitats a entitats de l'àmbit de drets civils?
  - Quin és el lloc on es realitzen les activitats amb aquest permís?
- source_sentence: en cas de compliment dels requisits establerts (persones residents,
    titulars de plaça d'aparcament, autotaxis, establiments hotelers)
  sentences:
  - Quin és el paper de l'administració en la justificació del projecte/activitat
    subvencionada?
  - Quin és el benefici de ser un autotaxi?
  - Quin és el benefici per als establiments de la instal·lació de terrasses o vetlladors?
- source_sentence: La convocatòria és el document que estableix les condicions i els
    requisits per a poder sol·licitar les subvencions pel suport educatiu a les escoles
    públiques de Sitges.
  sentences:
  - Quin és el paper de la convocatòria en les subvencions pel suport educatiu a les
    escoles públiques de Sitges?
  - Quin és el benefici de la consulta prèvia de classificació d'activitat per a l'Ajuntament
    de Sitges?
  - Quin és el tipus d'ocupació de la via pública que es pot realitzar amb aquest
    permís?
- source_sentence: Cal revisar la informació i els terminis de la convocatòria específica
    de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.
  sentences:
  - Quin és el document que es necessita per acreditar l'any de construcció i l'adequació
    a la legalitat urbanística d'un immoble?
  - Quin és el paper de l'Ajuntament en la gestió de les activitats per temporades?
  - On es pot trobar la informació sobre els terminis de presentació d'al·legacions
    en un procés de selecció de personal de l'Ajuntament de Sitges?
model-index:
- name: BGE SITGES  CAT
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.12679425837320574
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21291866028708134
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30861244019138756
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49521531100478466
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12679425837320574
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07097288676236044
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06172248803827751
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049521531100478466
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12679425837320574
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21291866028708134
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30861244019138756
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49521531100478466
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.27514703200596163
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20944786207944124
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23684652150885108
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.11961722488038277
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20574162679425836
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.31100478468899523
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49760765550239233
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11961722488038277
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06858054226475278
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06220095693779904
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04976076555023923
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11961722488038277
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20574162679425836
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.31100478468899523
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49760765550239233
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2725409285822112
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2052479684058634
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23218215402287107
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.12440191387559808
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.215311004784689
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.33014354066985646
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5047846889952153
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12440191387559808
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07177033492822966
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0660287081339713
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.050478468899521525
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12440191387559808
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.215311004784689
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.33014354066985646
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5047846889952153
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2802134368260993
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21296422875370263
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23912050845024263
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.11961722488038277
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23205741626794257
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32057416267942584
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.47607655502392343
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11961722488038277
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07735247208931419
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06411483253588517
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04760765550239234
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11961722488038277
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23205741626794257
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32057416267942584
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.47607655502392343
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2689946292721634
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20637104123946248
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23511603125214608
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.11961722488038277
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21770334928229665
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3253588516746411
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11961722488038277
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07256778309409888
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06507177033492824
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049999999999999996
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11961722488038277
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21770334928229665
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3253588516746411
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2754707963170229
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20811498443077409
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23411435647414974
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.1291866028708134
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21291866028708134
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32057416267942584
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.48086124401913877
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1291866028708134
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07097288676236044
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06411483253588518
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04808612440191388
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1291866028708134
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21291866028708134
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32057416267942584
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.48086124401913877
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2704775725936489
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20746753246753263
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23395020532132502
      name: Cosine Map@100
---

# BGE SITGES  CAT

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:** 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': 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/SITGES-BAAI3")
# Run inference
sentences = [
    "Cal revisar la informació i els terminis de la convocatòria específica de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.",
    "On es pot trobar la informació sobre els terminis de presentació d'al·legacions en un procés de selecció de personal de l'Ajuntament de Sitges?",
    "Quin és el document que es necessita per acreditar l'any de construcció i l'adequació a la legalitat urbanística d'un immoble?",
]
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   | 0.1268     |
| cosine_accuracy@3   | 0.2129     |
| cosine_accuracy@5   | 0.3086     |
| cosine_accuracy@10  | 0.4952     |
| cosine_precision@1  | 0.1268     |
| cosine_precision@3  | 0.071      |
| cosine_precision@5  | 0.0617     |
| cosine_precision@10 | 0.0495     |
| cosine_recall@1     | 0.1268     |
| cosine_recall@3     | 0.2129     |
| cosine_recall@5     | 0.3086     |
| cosine_recall@10    | 0.4952     |
| cosine_ndcg@10      | 0.2751     |
| cosine_mrr@10       | 0.2094     |
| **cosine_map@100**  | **0.2368** |

#### 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   | 0.1196     |
| cosine_accuracy@3   | 0.2057     |
| cosine_accuracy@5   | 0.311      |
| cosine_accuracy@10  | 0.4976     |
| cosine_precision@1  | 0.1196     |
| cosine_precision@3  | 0.0686     |
| cosine_precision@5  | 0.0622     |
| cosine_precision@10 | 0.0498     |
| cosine_recall@1     | 0.1196     |
| cosine_recall@3     | 0.2057     |
| cosine_recall@5     | 0.311      |
| cosine_recall@10    | 0.4976     |
| cosine_ndcg@10      | 0.2725     |
| cosine_mrr@10       | 0.2052     |
| **cosine_map@100**  | **0.2322** |

#### 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   | 0.1244     |
| cosine_accuracy@3   | 0.2153     |
| cosine_accuracy@5   | 0.3301     |
| cosine_accuracy@10  | 0.5048     |
| cosine_precision@1  | 0.1244     |
| cosine_precision@3  | 0.0718     |
| cosine_precision@5  | 0.066      |
| cosine_precision@10 | 0.0505     |
| cosine_recall@1     | 0.1244     |
| cosine_recall@3     | 0.2153     |
| cosine_recall@5     | 0.3301     |
| cosine_recall@10    | 0.5048     |
| cosine_ndcg@10      | 0.2802     |
| cosine_mrr@10       | 0.213      |
| **cosine_map@100**  | **0.2391** |

#### 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   | 0.1196     |
| cosine_accuracy@3   | 0.2321     |
| cosine_accuracy@5   | 0.3206     |
| cosine_accuracy@10  | 0.4761     |
| cosine_precision@1  | 0.1196     |
| cosine_precision@3  | 0.0774     |
| cosine_precision@5  | 0.0641     |
| cosine_precision@10 | 0.0476     |
| cosine_recall@1     | 0.1196     |
| cosine_recall@3     | 0.2321     |
| cosine_recall@5     | 0.3206     |
| cosine_recall@10    | 0.4761     |
| cosine_ndcg@10      | 0.269      |
| cosine_mrr@10       | 0.2064     |
| **cosine_map@100**  | **0.2351** |

#### 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   | 0.1196     |
| cosine_accuracy@3   | 0.2177     |
| cosine_accuracy@5   | 0.3254     |
| cosine_accuracy@10  | 0.5        |
| cosine_precision@1  | 0.1196     |
| cosine_precision@3  | 0.0726     |
| cosine_precision@5  | 0.0651     |
| cosine_precision@10 | 0.05       |
| cosine_recall@1     | 0.1196     |
| cosine_recall@3     | 0.2177     |
| cosine_recall@5     | 0.3254     |
| cosine_recall@10    | 0.5        |
| cosine_ndcg@10      | 0.2755     |
| cosine_mrr@10       | 0.2081     |
| **cosine_map@100**  | **0.2341** |

#### 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   | 0.1292    |
| cosine_accuracy@3   | 0.2129    |
| cosine_accuracy@5   | 0.3206    |
| cosine_accuracy@10  | 0.4809    |
| cosine_precision@1  | 0.1292    |
| cosine_precision@3  | 0.071     |
| cosine_precision@5  | 0.0641    |
| cosine_precision@10 | 0.0481    |
| cosine_recall@1     | 0.1292    |
| cosine_recall@3     | 0.2129    |
| cosine_recall@5     | 0.3206    |
| cosine_recall@10    | 0.4809    |
| cosine_ndcg@10      | 0.2705    |
| cosine_mrr@10       | 0.2075    |
| **cosine_map@100**  | **0.234** |

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## 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`: 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`: 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`: 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 |
|:----------:|:------:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3404     | 5      | 3.3256        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.6809     | 10     | 2.2115        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9532     | 14     | -             | 1.2963     | 0.2260                  | 0.2148                 | 0.2144                 | 0.2258                 | 0.2069                | 0.2252                 |
| 1.0213     | 15     | 1.7921        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.3617     | 20     | 1.2295        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.7021     | 25     | 0.9048        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9745     | 29     | -             | 0.8667     | 0.2311                  | 0.2267                 | 0.2292                 | 0.2279                 | 0.2121                | 0.2278                 |
| 2.0426     | 30     | 0.7256        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.3830     | 35     | 0.5252        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.7234     | 40     | 0.4648        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| **2.9957** | **44** | **-**         | **0.692**  | **0.2311**              | **0.2243**             | **0.2332**             | **0.2319**             | **0.2211**            | **0.2354**             |
| 3.0638     | 45     | 0.3518        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4043     | 50     | 0.321         | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7447     | 55     | 0.2923        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9489     | 58     | -             | 0.6514     | 0.2343                  | 0.2210                 | 0.2293                 | 0.2338                 | 0.2242                | 0.2331                 |
| 4.0851     | 60     | 0.2522        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.4255     | 65     | 0.2445        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.7660     | 70     | 0.2358        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9702     | 73     | -             | 0.6481     | 0.2348                  | 0.2239                 | 0.2252                 | 0.2332                 | 0.2167                | 0.2298                 |
| 5.1064     | 75     | 0.2301        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.4468     | 80     | 0.2262        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.7191     | 84     | -             | 0.6460     | 0.2430                  | 0.2308                 | 0.2343                 | 0.2408                 | 0.2212                | 0.2378                 |
| 0.3404     | 5      | 0.1585        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.6809     | 10     | 0.1465        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9532     | 14     | -             | 0.6325     | 0.2407                  | 0.2255                 | 0.2328                 | 0.2333                 | 0.2266                | 0.2429                 |
| 1.0213     | 15     | 0.1411        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.3617     | 20     | 0.079         | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.7021     | 25     | 0.1159        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9745     | 29     | -             | 0.6772     | 0.2361                  | 0.2287                 | 0.2252                 | 0.2325                 | 0.2228                | 0.2387                 |
| 2.0426     | 30     | 0.0838        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.3830     | 35     | 0.0647        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.7234     | 40     | 0.0752        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| **2.9957** | **44** | **-**         | **0.6668** | **0.2304**              | **0.2354**             | **0.2304**             | **0.2344**             | **0.2155**            | **0.2321**             |
| 3.0638     | 45     | 0.0706        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4043     | 50     | 0.0478        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7447     | 55     | 0.0768        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9489     | 58     | -             | 0.6040     | 0.2318                  | 0.2293                 | 0.2292                 | 0.2305                 | 0.2165                | 0.2264                 |
| 4.0851     | 60     | 0.0793        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.4255     | 65     | 0.0559        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.7660     | 70     | 0.0654        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9702     | 73     | -             | 0.6105     | 0.2328                  | 0.2328                 | 0.2313                 | 0.2364                 | 0.2279                | 0.2320                 |
| 5.1064     | 75     | 0.0734        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.4468     | 80     | 0.0616        | -          | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.7191     | 84     | -             | 0.6107     | 0.2368                  | 0.2341                 | 0.2351                 | 0.2391                 | 0.2340                | 0.2322                 |

* 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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