---
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:828
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Comunicació prèvia per l'execució de cales, pous i sondejos, en
    terreny privat, previs a l'actuació definitiva.
  sentences:
  - Quin és el requisit per a l'execució de les obres en terreny privat?
  - Quin és el propòsit del tràmit de rectificació de dades personals?
  - Quin és el requisit per a la crema en zones de conservació?
- source_sentence: En el mateix tràmit també es pot actualitzar el canvi de domicili
    o dades personals, si escau.
  sentences:
  - Quins tributs puc domiciliar amb aquest tràmit?
  - Quin és el compromís del titular de l'activitat en la Declaració responsable?
  - Quin és el tràmit que permet actualitzar les dades personals?
- source_sentence: El reconeixement administratiu del dret comunicat es produeix salvat
    el dret de propietat, sens perjudici del de tercers ni de les competències d’altres
    organismes i administracions.
  sentences:
  - Quin és el tràmit que permet una major transparència en la gestió dels animals
    domèstics?
  - Quin és el requisit per considerar una tala de masses arbòries?
  - Quin és el reconeixement administratiu del dret comunicat?
- source_sentence: El seu objecte és que -prèviament a la seva execució material-
    l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament,
    així com a les ordenances municipals.
  sentences:
  - Quin és el resultat de rectificar les meves dades personals?
  - Quin és el paper de les llicències urbanístiques en la instal·lació de construccions
    auxiliars o mòduls prefabricats?
  - Quin és l'objectiu de l'Ajuntament en aquest tràmit?
- source_sentence: 'Permet sol·licitar l’autorització per a l’ús comú especial de
    la via pública per a reserves temporals d’estacionament i espai públic per: càrrega/descàrrega
    de materials diversos davant d''una obra;'
  sentences:
  - Quin és el propòsit de les actuacions de manteniment d'elements de façana i cobertes?
  - Quin és el tràmit per canviar el domicili del permís de conducció i del permís
    de circulació?
  - Quins són els materials que es poden càrregar/descarregar en l'ocupació i reserves
    temporals amb càrrega/descàrrega de materials?
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: 0.1956521739130435
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5434782608695652
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6739130434782609
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7717391304347826
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1956521739130435
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18115942028985504
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13478260869565215
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07717391304347823
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1956521739130435
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5434782608695652
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6739130434782609
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7717391304347826
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.48504415203944085
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.39229641131815035
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4002530280745044
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.1956521739130435
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5543478260869565
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6739130434782609
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7717391304347826
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1956521739130435
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18478260869565213
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13478260869565215
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07717391304347823
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1956521739130435
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5543478260869565
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6739130434782609
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7717391304347826
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.48804421462232656
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3962215320910973
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.404212372178018
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.20652173913043478
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5434782608695652
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6521739130434783
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7608695652173914
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.20652173913043478
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18115942028985504
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13043478260869562
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07608695652173911
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.20652173913043478
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5434782608695652
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6521739130434783
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7608695652173914
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4840641874049137
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.39500086266390616
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4031258766496075
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.18478260869565216
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5434782608695652
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6521739130434783
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.75
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.18478260869565216
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18115942028985504
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13043478260869562
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07499999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.18478260869565216
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5434782608695652
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6521739130434783
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.75
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4702420475154915
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3799301242236025
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.38860307402910876
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.22826086956521738
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5434782608695652
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6956521739130435
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.782608695652174
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.22826086956521738
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18115942028985504
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13913043478260867
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07826086956521737
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.22826086956521738
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5434782608695652
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6956521739130435
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.782608695652174
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5045819494113778
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.41489820565907526
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4206777643300118
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.17391304347826086
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4891304347826087
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6630434782608695
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7608695652173914
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17391304347826086
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.16304347826086954
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1326086956521739
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07608695652173911
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17391304347826086
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4891304347826087
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6630434782608695
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7608695652173914
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4628441336923734
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.36670548654244295
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.37290616382203134
      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/sqv-v3")
# Run inference
sentences = [
    "Permet sol·licitar l’autorització per a l’ús comú especial de la via pública per a reserves temporals d’estacionament i espai públic per: càrrega/descàrrega de materials diversos davant d'una obra;",
    "Quins són els materials que es poden càrregar/descarregar en l'ocupació i reserves temporals amb càrrega/descàrrega de materials?",
    'Quin és el tràmit per canviar el domicili del permís de conducció i del permís de circulació?',
]
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   | 0.1957     |
| cosine_accuracy@3   | 0.5435     |
| cosine_accuracy@5   | 0.6739     |
| cosine_accuracy@10  | 0.7717     |
| cosine_precision@1  | 0.1957     |
| cosine_precision@3  | 0.1812     |
| cosine_precision@5  | 0.1348     |
| cosine_precision@10 | 0.0772     |
| cosine_recall@1     | 0.1957     |
| cosine_recall@3     | 0.5435     |
| cosine_recall@5     | 0.6739     |
| cosine_recall@10    | 0.7717     |
| cosine_ndcg@10      | 0.485      |
| cosine_mrr@10       | 0.3923     |
| **cosine_map@100**  | **0.4003** |

#### 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   | 0.1957     |
| cosine_accuracy@3   | 0.5543     |
| cosine_accuracy@5   | 0.6739     |
| cosine_accuracy@10  | 0.7717     |
| cosine_precision@1  | 0.1957     |
| cosine_precision@3  | 0.1848     |
| cosine_precision@5  | 0.1348     |
| cosine_precision@10 | 0.0772     |
| cosine_recall@1     | 0.1957     |
| cosine_recall@3     | 0.5543     |
| cosine_recall@5     | 0.6739     |
| cosine_recall@10    | 0.7717     |
| cosine_ndcg@10      | 0.488      |
| cosine_mrr@10       | 0.3962     |
| **cosine_map@100**  | **0.4042** |

#### 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   | 0.2065     |
| cosine_accuracy@3   | 0.5435     |
| cosine_accuracy@5   | 0.6522     |
| cosine_accuracy@10  | 0.7609     |
| cosine_precision@1  | 0.2065     |
| cosine_precision@3  | 0.1812     |
| cosine_precision@5  | 0.1304     |
| cosine_precision@10 | 0.0761     |
| cosine_recall@1     | 0.2065     |
| cosine_recall@3     | 0.5435     |
| cosine_recall@5     | 0.6522     |
| cosine_recall@10    | 0.7609     |
| cosine_ndcg@10      | 0.4841     |
| cosine_mrr@10       | 0.395      |
| **cosine_map@100**  | **0.4031** |

#### 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   | 0.1848     |
| cosine_accuracy@3   | 0.5435     |
| cosine_accuracy@5   | 0.6522     |
| cosine_accuracy@10  | 0.75       |
| cosine_precision@1  | 0.1848     |
| cosine_precision@3  | 0.1812     |
| cosine_precision@5  | 0.1304     |
| cosine_precision@10 | 0.075      |
| cosine_recall@1     | 0.1848     |
| cosine_recall@3     | 0.5435     |
| cosine_recall@5     | 0.6522     |
| cosine_recall@10    | 0.75       |
| cosine_ndcg@10      | 0.4702     |
| cosine_mrr@10       | 0.3799     |
| **cosine_map@100**  | **0.3886** |

#### 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   | 0.2283     |
| cosine_accuracy@3   | 0.5435     |
| cosine_accuracy@5   | 0.6957     |
| cosine_accuracy@10  | 0.7826     |
| cosine_precision@1  | 0.2283     |
| cosine_precision@3  | 0.1812     |
| cosine_precision@5  | 0.1391     |
| cosine_precision@10 | 0.0783     |
| cosine_recall@1     | 0.2283     |
| cosine_recall@3     | 0.5435     |
| cosine_recall@5     | 0.6957     |
| cosine_recall@10    | 0.7826     |
| cosine_ndcg@10      | 0.5046     |
| cosine_mrr@10       | 0.4149     |
| **cosine_map@100**  | **0.4207** |

#### 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   | 0.1739     |
| cosine_accuracy@3   | 0.4891     |
| cosine_accuracy@5   | 0.663      |
| cosine_accuracy@10  | 0.7609     |
| cosine_precision@1  | 0.1739     |
| cosine_precision@3  | 0.163      |
| cosine_precision@5  | 0.1326     |
| cosine_precision@10 | 0.0761     |
| cosine_recall@1     | 0.1739     |
| cosine_recall@3     | 0.4891     |
| cosine_recall@5     | 0.663      |
| cosine_recall@10    | 0.7609     |
| cosine_ndcg@10      | 0.4628     |
| cosine_mrr@10       | 0.3667     |
| **cosine_map@100**  | **0.3729** |

<!--
## 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: 828 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 828 samples:
  |         | positive                                                                           | anchor                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 41.95 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.81 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                   | anchor                                                                                                                         |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
  | <code>Consultar l'estat tributari d'un contribuent. Us permet consultar l'estat dels rebuts i liquidacions que estan a nom del contribuent titular d'un certificat electrònic, així com els elements que configuren el càlcul per determinar el deute tributari de cadascun d'ells.</code> | <code>Com puc consultar l'estat tributari d'un contribuent?</code>                                                             |
  | <code>L'informe facultatiu servirà per tramitar una autorització de residència temporal per arrelament social.</code>                                                                                                                                                                      | <code>Quin és el tràmit relacionat amb la residència a l'Ajuntament?</code>                                                    |
  | <code>Aquesta targeta, és el document que dona dret a persones físiques o jurídiques titulars de vehicles adaptats destinats al transport col·lectiu de persones amb discapacitat...</code>                                                                                                | <code>Quin és el benefici de tenir la targeta d'aparcament de transport col·lectiu per a les persones amb discapacitat?</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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9231  | 3      | -             | 0.3914                  | 0.3466                 | 0.3625                 | 0.3778                 | 0.3067                | 0.3810                 |
| 1.8462  | 6      | -             | 0.3835                  | 0.3940                 | 0.3789                 | 0.3857                 | 0.3407                | 0.3808                 |
| 2.7692  | 9      | -             | 0.4028                  | 0.4159                 | 0.3961                 | 0.4098                 | 0.3803                | 0.4029                 |
| 3.0769  | 10     | 3.1546        | -                       | -                      | -                      | -                      | -                     | -                      |
| **4.0** | **13** | **-**         | **0.3992**              | **0.4209**             | **0.3905**             | **0.4121**             | **0.3806**            | **0.4009**             |
| 4.6154  | 15     | -             | 0.4003                  | 0.4207                 | 0.3886                 | 0.4031                 | 0.3729                | 0.4042                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.35.0.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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