sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:3814 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:3814
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Sol·licitud de l'informe d'integració social per a la renovació
    o modificació de la residència.
  sentences:
  - Quin és el propòsit de la renovació o modificació de la residència?
  - Quin és el paper de l'Administració en la Declaració responsable d'obertura?
  - Quin és el lloc on es pot realitzar l'ocupació de la via pública?
- source_sentence: Aquest tràmit permet obtenir la llicència d'ocupació de la via
    pública per a la instal·lació de grues desmuntables.
  sentences:
  - Quin és el propòsit de la consulta del Cens Electoral?
  - Quin és el tràmit necessari per a la instal·lació de grues desmuntables en una
    via pública?
  - Quines reclamacions es consideren en aquest tràmit?
- source_sentence: 'Bonificacions: Persones amb discapacitat: bonificació 50%. Laboratori
    d''art: Preu: 15€/mes'
  sentences:
  - Quin és el preu del curs de Laboratori d'art per a persones amb discapacitat?
  - Quin és el paper de les oficines municipals d'atenció ciutadana en la renovació
    de la inscripció padronal?
  - Quin és el període en què les entitats i associacions registrades han de notificar
    les modificacions produïdes en les dades registrals?
- source_sentence: Es tracta de la sol·licitud d'elaboració del certificat que justifica
    l'antiguitat i legalitat d'un immoble, document necessari en el moment de la venda,
    per poder-lo inscriure al Registre de la Propietat si no es va fer en finalitzar
    l'obra.
  sentences:
  - Què ha de fer el responsable en relació amb els destinataris quan es limita el
    tractament de dades personals?
  - Quin és el motiu pel qual es sol·licita el certificat d'antiguitat i legalitat
    urbanística en la venda d'un immoble?
  - Qui és el destinatari de la comunicació de canvi de titularitat d'activitats?
- source_sentence: 'Laboratori d''art: D''octubre 2024 a maig de 2025. Horari: Dilluns
    de 17.30h a 19.00h'
  sentences:
  - Quin és el dia i hora del curs de Laboratori d'art?
  - Quin és el paper dels dipòsits o fiances en la garantia d'abocament controlat
    de runes?
  - On es pot sol·licitar la reserva especial d'estacionament?
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: 0.10384615384615385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2153846153846154
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.27692307692307694
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.48846153846153845
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10384615384615385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07179487179487179
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.055384615384615386
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.048846153846153845
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10384615384615385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2153846153846154
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.27692307692307694
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.48846153846153845
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2612154031642473
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.193324175824176
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21923866500444808
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.11923076923076924
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23076923076923078
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.31153846153846154
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5307692307692308
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11923076923076924
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07692307692307693
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06230769230769231
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05307692307692307
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11923076923076924
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23076923076923078
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.31153846153846154
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5307692307692308
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2878219714456531
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21504578754578765
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23782490878695842
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.12692307692307692
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23846153846153847
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3269230769230769
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5269230769230769
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12692307692307692
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07948717948717948
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06538461538461539
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05269230769230769
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12692307692307692
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23846153846153847
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3269230769230769
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5269230769230769
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2920408684487264
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.22163461538461554
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24439125474069504
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.10384615384615385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2076923076923077
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3076923076923077
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49615384615384617
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10384615384615385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06923076923076923
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06153846153846154
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04961538461538462
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10384615384615385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2076923076923077
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3076923076923077
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49615384615384617
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26493374179245505
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.195289987789988
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22019396693132914
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.13076923076923078
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23076923076923078
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3423076923076923
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.55
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13076923076923078
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07692307692307693
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06846153846153846
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05499999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13076923076923078
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23076923076923078
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3423076923076923
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.55
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.30010874813387883
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2253495115995117
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2488774864299421
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.10384615384615385
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2230769230769231
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2846153846153846
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49230769230769234
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10384615384615385
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07435897435897434
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05692307692307692
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04923076923076923
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10384615384615385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2230769230769231
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2846153846153846
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49230769230769234
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2636327280635836
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19504273504273517
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21974930573072288
      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-MONT-001-5ep")
# Run inference
sentences = [
    "Laboratori d'art: D'octubre 2024 a maig de 2025. Horari: Dilluns de 17.30h a 19.00h",
    "Quin és el dia i hora del curs de Laboratori d'art?",
    "On es pot sol·licitar la reserva especial d'estacionament?",
]
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   | 0.1038     |
| cosine_accuracy@3   | 0.2154     |
| cosine_accuracy@5   | 0.2769     |
| cosine_accuracy@10  | 0.4885     |
| cosine_precision@1  | 0.1038     |
| cosine_precision@3  | 0.0718     |
| cosine_precision@5  | 0.0554     |
| cosine_precision@10 | 0.0488     |
| cosine_recall@1     | 0.1038     |
| cosine_recall@3     | 0.2154     |
| cosine_recall@5     | 0.2769     |
| cosine_recall@10    | 0.4885     |
| cosine_ndcg@10      | 0.2612     |
| cosine_mrr@10       | 0.1933     |
| **cosine_map@100**  | **0.2192** |

#### 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   | 0.1192     |
| cosine_accuracy@3   | 0.2308     |
| cosine_accuracy@5   | 0.3115     |
| cosine_accuracy@10  | 0.5308     |
| cosine_precision@1  | 0.1192     |
| cosine_precision@3  | 0.0769     |
| cosine_precision@5  | 0.0623     |
| cosine_precision@10 | 0.0531     |
| cosine_recall@1     | 0.1192     |
| cosine_recall@3     | 0.2308     |
| cosine_recall@5     | 0.3115     |
| cosine_recall@10    | 0.5308     |
| cosine_ndcg@10      | 0.2878     |
| cosine_mrr@10       | 0.215      |
| **cosine_map@100**  | **0.2378** |

#### 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   | 0.1269     |
| cosine_accuracy@3   | 0.2385     |
| cosine_accuracy@5   | 0.3269     |
| cosine_accuracy@10  | 0.5269     |
| cosine_precision@1  | 0.1269     |
| cosine_precision@3  | 0.0795     |
| cosine_precision@5  | 0.0654     |
| cosine_precision@10 | 0.0527     |
| cosine_recall@1     | 0.1269     |
| cosine_recall@3     | 0.2385     |
| cosine_recall@5     | 0.3269     |
| cosine_recall@10    | 0.5269     |
| cosine_ndcg@10      | 0.292      |
| cosine_mrr@10       | 0.2216     |
| **cosine_map@100**  | **0.2444** |

#### 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   | 0.1038     |
| cosine_accuracy@3   | 0.2077     |
| cosine_accuracy@5   | 0.3077     |
| cosine_accuracy@10  | 0.4962     |
| cosine_precision@1  | 0.1038     |
| cosine_precision@3  | 0.0692     |
| cosine_precision@5  | 0.0615     |
| cosine_precision@10 | 0.0496     |
| cosine_recall@1     | 0.1038     |
| cosine_recall@3     | 0.2077     |
| cosine_recall@5     | 0.3077     |
| cosine_recall@10    | 0.4962     |
| cosine_ndcg@10      | 0.2649     |
| cosine_mrr@10       | 0.1953     |
| **cosine_map@100**  | **0.2202** |

#### 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   | 0.1308     |
| cosine_accuracy@3   | 0.2308     |
| cosine_accuracy@5   | 0.3423     |
| cosine_accuracy@10  | 0.55       |
| cosine_precision@1  | 0.1308     |
| cosine_precision@3  | 0.0769     |
| cosine_precision@5  | 0.0685     |
| cosine_precision@10 | 0.055      |
| cosine_recall@1     | 0.1308     |
| cosine_recall@3     | 0.2308     |
| cosine_recall@5     | 0.3423     |
| cosine_recall@10    | 0.55       |
| cosine_ndcg@10      | 0.3001     |
| cosine_mrr@10       | 0.2253     |
| **cosine_map@100**  | **0.2489** |

#### 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   | 0.1038     |
| cosine_accuracy@3   | 0.2231     |
| cosine_accuracy@5   | 0.2846     |
| cosine_accuracy@10  | 0.4923     |
| cosine_precision@1  | 0.1038     |
| cosine_precision@3  | 0.0744     |
| cosine_precision@5  | 0.0569     |
| cosine_precision@10 | 0.0492     |
| cosine_recall@1     | 0.1038     |
| cosine_recall@3     | 0.2231     |
| cosine_recall@5     | 0.2846     |
| cosine_recall@10    | 0.4923     |
| cosine_ndcg@10      | 0.2636     |
| cosine_mrr@10       | 0.195      |
| **cosine_map@100**  | **0.2197** |

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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 Dataset

#### json

* Dataset: json
* Size: 3,814 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: 39.27 tokens</li><li>max: 165 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.67 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                               | anchor                                                                                                                                                   |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Aquest tràmit permet obtenir la llicència per a ocupació de la via pública per quioscs, casetes o parades (xurreries, gelats,...).</code>        | <code>Quins són els requisits per obtenir la llicència d'ocupació de la via pública per a gelats?</code>                                                 |
  | <code>Aquest tràmit permet obtenir la llicència d'ocupació de la via pública per a la instal·lació de grues desmuntables.</code>                       | <code>Quin és el lloc on es pot obtenir la llicència d'ocupació de la via pública per a la instal·lació de grues desmuntables en una via pública?</code> |
  | <code>L’Espai Jove de Montgat disposa de dues sales, una aula, i una sala chill-out així com jardins i serveis adreçats als joves del municipi.</code> | <code>Quin és el propòsit de l'aula de l'Espai Jove de Montgat?</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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.6695     | 10     | 3.4242        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.9372     | 14     | -             | 0.2075                  | 0.2165                 | 0.2078                 | 0.1957                 | 0.2050                 | 0.1949                |
| 1.3389     | 20     | 1.666         | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.9414     | 29     | -             | 0.2145                  | 0.2184                 | 0.2248                 | 0.2144                 | 0.2244                 | 0.2112                |
| 2.0084     | 30     | 0.7666        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.6778     | 40     | 0.4859        | -                       | -                      | -                      | -                      | -                      | -                     |
| **2.9456** | **44** | **-**         | **0.2263**              | **0.2408**             | **0.2234**             | **0.2274**             | **0.252**              | **0.2313**            |
| 3.3473     | 50     | 0.277         | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.9498     | 59     | -             | 0.2107                  | 0.2359                 | 0.2386                 | 0.2275                 | 0.2382                 | 0.2246                |
| 4.0167     | 60     | 0.2423        | -                       | -                      | -                      | -                      | -                      | -                     |
| 4.6862     | 70     | 0.2281        | 0.2192                  | 0.2378                 | 0.2444                 | 0.2202                 | 0.2489                 | 0.2197                |

* 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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