---
base_model: BAAI/bge-m3
library_name: sentence-transformers
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:2372
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Heu de veure si és necessari un estudi d'aïllament acústic i quin
    nivell d'aïllament acústic precisa l'activitat.
  sentences:
  - Quin és el paper de les persones que resideixen amb el titular del dret d'habitatge
    en la política d'habitatge?
  - Quin és el límit de superfície per a les carpes informatives?
  - Quin és l'objectiu de l'estudi d'aïllament acústic?
- source_sentence: 'Si us voleu matricular al proper curs 2022-2023 d''arts plàstiques
    ho podeu fer a partir del 1 de juliol a les 16h, seleccionant una d''aquestes
    opcions:'
  sentences:
  - Quin és el període de matrícula per al curs 2022-2023 d'arts plàstiques?
  - Quan no cal presentar al·legacions en un expedient de baixa d'ofici?
  - Quin és l'objectiu de les al·legacions respecte a un expedient sancionador de
    l'Ordenança Municipal de Civisme i Convivència Ciutadana?
- source_sentence: Annexes Econòmics (Cooperació)
  sentences:
  - Qui és el responsable de l'elaboració de l'informe d'adequació de l'habitatge?
  - Què han de fer les persones interessades durant el tràmit d'audiència en el procés
    d'inclusió al registre municipal d'immobles desocupats?
  - Quin és l'àmbit de la cooperació econòmica?
- source_sentence: En virtut del conveni de col.laboració amb l'Atrium de Viladecans,
    tots els ciutadans que acreditin la seva residència a Viladecans es podran beneficiar
    d'un 20% de descompte en la programació de teatre, música i dansa, objecte del
    conveni.
  sentences:
  - Quin és el resultat de consultar un expedient d'activitats?
  - Quin és el format de resposta d'aquesta sol·licitud?
  - Quin és el descompte que s'aplica en la programació de teatre, música i dansa
    per als ciutadans de Viladecans?
- source_sentence: Descripció. Retorna en format JSON adequat
  sentences:
  - Quin és el contingut de l'annex específic?
  - Quin tipus d'ocupació es refereix a la renúncia de la llicència?
  - Què passa amb l'habitatge?
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.33220910623946037
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5902192242833052
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6998313659359191
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8094435075885329
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.33220910623946037
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1967397414277684
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1399662731871838
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08094435075885327
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.33220910623946037
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5902192242833052
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6998313659359191
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8094435075885329
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5625986746470664
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4843170320404718
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.49243646079034575
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.3406408094435076
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5767284991568297
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6981450252951096
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8161888701517707
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3406408094435076
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19224283305227655
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1396290050590219
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08161888701517706
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3406408094435076
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5767284991568297
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6981450252951096
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8161888701517707
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5661348054508011
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4872065633448428
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.49520736709122076
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.3305227655986509
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5801011804384486
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6947723440134908
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8161888701517707
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3305227655986509
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19336706014614952
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13895446880269813
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08161888701517707
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3305227655986509
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5801011804384486
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6947723440134908
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8161888701517707
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5629643418278626
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4829913809256133
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.49079988310494693
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.3288364249578415
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5885328836424958
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7015177065767285
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8094435075885329
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3288364249578415
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1961776278808319
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14030354131534567
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08094435075885327
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3288364249578415
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5885328836424958
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7015177065767285
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8094435075885329
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5625842077927447
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.48416981182579805
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.49201787335851555
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.3473861720067454
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.581787521079258
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6998313659359191
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.806070826306914
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3473861720067454
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19392917369308602
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1399662731871838
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0806070826306914
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3473861720067454
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.581787521079258
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6998313659359191
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.806070826306914
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.565365572327355
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4893626703070211
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.49726527073459287
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.2917369308600337
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5682967959527825
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6644182124789207
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7875210792580101
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2917369308600337
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18943226531759413
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13288364249578413
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07875210792580102
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2917369308600337
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5682967959527825
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6644182124789207
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7875210792580101
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5320349463938843
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.45117106988945077
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.45948574441166834
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-m3

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. 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:**
    - json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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/ST-tramits-SB-001-5ep")
# Run inference
sentences = [
    'Descripció. Retorna en format JSON adequat',
    "Quin és el contingut de l'annex específic?",
    "Què passa amb l'habitatge?",
]
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   | 0.3322     |
| cosine_accuracy@3   | 0.5902     |
| cosine_accuracy@5   | 0.6998     |
| cosine_accuracy@10  | 0.8094     |
| cosine_precision@1  | 0.3322     |
| cosine_precision@3  | 0.1967     |
| cosine_precision@5  | 0.14       |
| cosine_precision@10 | 0.0809     |
| cosine_recall@1     | 0.3322     |
| cosine_recall@3     | 0.5902     |
| cosine_recall@5     | 0.6998     |
| cosine_recall@10    | 0.8094     |
| cosine_ndcg@10      | 0.5626     |
| cosine_mrr@10       | 0.4843     |
| **cosine_map@100**  | **0.4924** |

#### 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   | 0.3406     |
| cosine_accuracy@3   | 0.5767     |
| cosine_accuracy@5   | 0.6981     |
| cosine_accuracy@10  | 0.8162     |
| cosine_precision@1  | 0.3406     |
| cosine_precision@3  | 0.1922     |
| cosine_precision@5  | 0.1396     |
| cosine_precision@10 | 0.0816     |
| cosine_recall@1     | 0.3406     |
| cosine_recall@3     | 0.5767     |
| cosine_recall@5     | 0.6981     |
| cosine_recall@10    | 0.8162     |
| cosine_ndcg@10      | 0.5661     |
| cosine_mrr@10       | 0.4872     |
| **cosine_map@100**  | **0.4952** |

#### 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   | 0.3305     |
| cosine_accuracy@3   | 0.5801     |
| cosine_accuracy@5   | 0.6948     |
| cosine_accuracy@10  | 0.8162     |
| cosine_precision@1  | 0.3305     |
| cosine_precision@3  | 0.1934     |
| cosine_precision@5  | 0.139      |
| cosine_precision@10 | 0.0816     |
| cosine_recall@1     | 0.3305     |
| cosine_recall@3     | 0.5801     |
| cosine_recall@5     | 0.6948     |
| cosine_recall@10    | 0.8162     |
| cosine_ndcg@10      | 0.563      |
| cosine_mrr@10       | 0.483      |
| **cosine_map@100**  | **0.4908** |

#### 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   | 0.3288    |
| cosine_accuracy@3   | 0.5885    |
| cosine_accuracy@5   | 0.7015    |
| cosine_accuracy@10  | 0.8094    |
| cosine_precision@1  | 0.3288    |
| cosine_precision@3  | 0.1962    |
| cosine_precision@5  | 0.1403    |
| cosine_precision@10 | 0.0809    |
| cosine_recall@1     | 0.3288    |
| cosine_recall@3     | 0.5885    |
| cosine_recall@5     | 0.7015    |
| cosine_recall@10    | 0.8094    |
| cosine_ndcg@10      | 0.5626    |
| cosine_mrr@10       | 0.4842    |
| **cosine_map@100**  | **0.492** |

#### 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   | 0.3474     |
| cosine_accuracy@3   | 0.5818     |
| cosine_accuracy@5   | 0.6998     |
| cosine_accuracy@10  | 0.8061     |
| cosine_precision@1  | 0.3474     |
| cosine_precision@3  | 0.1939     |
| cosine_precision@5  | 0.14       |
| cosine_precision@10 | 0.0806     |
| cosine_recall@1     | 0.3474     |
| cosine_recall@3     | 0.5818     |
| cosine_recall@5     | 0.6998     |
| cosine_recall@10    | 0.8061     |
| cosine_ndcg@10      | 0.5654     |
| cosine_mrr@10       | 0.4894     |
| **cosine_map@100**  | **0.4973** |

#### 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   | 0.2917     |
| cosine_accuracy@3   | 0.5683     |
| cosine_accuracy@5   | 0.6644     |
| cosine_accuracy@10  | 0.7875     |
| cosine_precision@1  | 0.2917     |
| cosine_precision@3  | 0.1894     |
| cosine_precision@5  | 0.1329     |
| cosine_precision@10 | 0.0788     |
| cosine_recall@1     | 0.2917     |
| cosine_recall@3     | 0.5683     |
| cosine_recall@5     | 0.6644     |
| cosine_recall@10    | 0.7875     |
| cosine_ndcg@10      | 0.532      |
| cosine_mrr@10       | 0.4512     |
| **cosine_map@100**  | **0.4595** |

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## 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 Dataset

#### json

* Dataset: json
* Size: 2,372 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 35.12 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 19.49 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                | anchor                                                                                          |
  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
  | <code>Comunicar la variació d'alguna de les següents dades del Padró Municipal d'Habitants: Nom, Cognoms, Data de naixement, DNI, Passaport, Número de permís de residència (NIE), Sexe, Municipi i/o província de naixement, Nacionalitat, Titulació acadèmica.</code> | <code>Quin és l'objectiu del canvi de dades personals en el Padró Municipal d'Habitants?</code> |
  | <code>EN QUÈ CONSISTEIX: Tramitar la sol·licitud de matrimoni civil a l'Ajuntament.</code>                                                                                                                                                                              | <code>Què és el matrimoni civil a l'Ajuntament de Sant Boi de Llobregat?</code>                 |
  | <code>En domiciliar el pagament de tributs municipals en entitats bancàries.</code>                                                                                                                                                                                     | <code>Quin és el benefici de domiciliar el pagament de tributs?</code>                          |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### 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`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `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
- `torch_empty_cache_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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | dim_1024_cosine_map@100 | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.9664     | 9      | -             | 0.4730                  | 0.4766                 | 0.4640                 | 0.4612                 | 0.4456                 | 0.4083                |
| 1.0738     | 10     | 2.6023        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.9329     | 18     | -             | 0.4951                  | 0.4966                 | 0.4977                 | 0.4773                 | 0.4849                 | 0.4501                |
| 2.1477     | 20     | 0.974         | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.8993     | 27     | -             | 0.4891                  | 0.4973                 | 0.4941                 | 0.4867                 | 0.4925                 | 0.4684                |
| 3.2215     | 30     | 0.408         | -                       | -                      | -                      | -                      | -                      | -                     |
| **3.9732** | **37** | **-**         | **0.4944**              | **0.4998**             | **0.4931**             | **0.4991**             | **0.4974**             | **0.4616**            |
| 4.2953     | 40     | 0.2718        | -                       | -                      | -                      | -                      | -                      | -                     |
| 4.8322     | 45     | -             | 0.4924                  | 0.4952                 | 0.4908                 | 0.4920                 | 0.4973                 | 0.4595                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 1.1.0.dev0
- Datasets: 3.0.1
- 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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