sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:6692 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:6692
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: La inscripció en aquest registre caduca en el termini d'un any,
    llevat que sigui renovada abans del transcurs d'aquest termini mitjançant la presentació
    d'una declaració responsable sobre el compliment dels requisits exigits.
  sentences:
  - Quin és el requisit per a la sol·licitud del volant d'empadronament?
  - Què passa si no es renova la inscripció en el Registre municipal de sol·licitants?
  - Quin és el segon objectiu que han de tenir els projectes/activitats per a rebre
    aquesta subvenció?
- source_sentence: 'AVÍS: Places exhaurides de l''activitat de psicomotricitat fins
    nou avís. Les persones interessades poden contactar amb el Departament d''Esports,
    el qual obrirà un llistat d''espera, si escau.'
  sentences:
  - Què passa si les places de Psicomotricitat estan exhaurides?
  - Quin és el paper del tractament en la declaració?
  - Quin és el període de temps que es requereix per a la venda d'articles d'artesania?
- source_sentence: El registre de noves patents en relació a les noves línies d’actuació
    és una despesa subvencionable per a la reactivació i adaptació del negoci post
    COVID19.
  sentences:
  - Quins són els tipus de despeses que es poden finançar amb les subvencions?
  - Quin és el paper de les organitzacions membres del Consell de Cooperació en els
    projectes de cooperació internacional?
  - Quin és el propòsit del registre de noves patents en relació a les noves línies
    d’actuació?
- source_sentence: 'Justificació de les subvencions atorgades per l''Ajuntament de
    Sitges per les activitats culturals incloses dins els següents tipus: Activitats
    de difusió cultural. Iniciatives de recuperació i difusió del patrimoni cultural,
    tradicional i popular. Activitats de formació no reglada i de recerca. Activitats
    d''animació socio-cultural.'
  sentences:
  - Quins són els residus que es recullen en el servei municipal complementari?
  - Quin és el paper de l'expedient d'ajut a la contractació laboral de persones en
    la contractació laboral?
  - Quin és el paper de les activitats d'animació socio-cultural?
- source_sentence: La comunicació és un element important en la cura dels gats, ja
    que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats
    competents i amb els altres implicats en la cura dels animals.
  sentences:
  - Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments
    oberts al públic i les activitats recreatives?
  - Quin és el paper de la comunicació en la cura dels gats?
  - Quin és el benefici de la llicència de gual per a la persona titular?
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: 0.1589958158995816
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.303347280334728
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3723849372384937
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5188284518828452
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1589958158995816
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.101115760111576
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07447698744769873
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05188284518828451
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1589958158995816
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.303347280334728
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3723849372384937
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5188284518828452
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.31740141154907076
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2560196254233912
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.27634436521904066
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.15690376569037656
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.29707112970711297
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3807531380753138
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5083682008368201
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.15690376569037656
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09902370990237098
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07615062761506276
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.050836820083682004
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.15690376569037656
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.29707112970711297
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3807531380753138
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5083682008368201
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3138709871801379
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.25412432755528996
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.27566053318396105
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.17364016736401675
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3138075313807531
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.39539748953974896
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5376569037656904
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.17364016736401675
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.10460251046025104
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07907949790794978
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05376569037656903
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.17364016736401675
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3138075313807531
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.39539748953974896
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5376569037656904
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.33244445391299926
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2700023245002324
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.29010151423672403
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.1506276150627615
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2907949790794979
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.401673640167364
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5355648535564853
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1506276150627615
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09693165969316596
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0803347280334728
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05355648535564853
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1506276150627615
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2907949790794979
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.401673640167364
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5355648535564853
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3189819772344188
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.25269392973367877
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2728848917988661
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.16736401673640167
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3200836820083682
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.41631799163179917
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5481171548117155
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.16736401673640167
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.10669456066945607
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08326359832635982
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05481171548117154
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16736401673640167
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3200836820083682
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.41631799163179917
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5481171548117155
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3353691502747181
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.26997077771136346
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2891803614784421
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.15481171548117154
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.28451882845188287
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3514644351464435
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5209205020920502
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.15481171548117154
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09483960948396093
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07029288702928871
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.052092050209205015
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.15481171548117154
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.28451882845188287
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3514644351464435
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5209205020920502
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3116868900381799
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2481885501759978
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2685744617473963
      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-SITGES-007-5ep")
# Run inference
sentences = [
    'La comunicació és un element important en la cura dels gats, ja que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats competents i amb els altres implicats en la cura dels animals.',
    'Quin és el paper de la comunicació en la cura dels gats?',
    'Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments oberts al públic i les activitats recreatives?',
]
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.*
-->

## 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   | 0.159      |
| cosine_accuracy@3   | 0.3033     |
| cosine_accuracy@5   | 0.3724     |
| cosine_accuracy@10  | 0.5188     |
| cosine_precision@1  | 0.159      |
| cosine_precision@3  | 0.1011     |
| cosine_precision@5  | 0.0745     |
| cosine_precision@10 | 0.0519     |
| cosine_recall@1     | 0.159      |
| cosine_recall@3     | 0.3033     |
| cosine_recall@5     | 0.3724     |
| cosine_recall@10    | 0.5188     |
| cosine_ndcg@10      | 0.3174     |
| cosine_mrr@10       | 0.256      |
| **cosine_map@100**  | **0.2763** |

#### 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   | 0.1569     |
| cosine_accuracy@3   | 0.2971     |
| cosine_accuracy@5   | 0.3808     |
| cosine_accuracy@10  | 0.5084     |
| cosine_precision@1  | 0.1569     |
| cosine_precision@3  | 0.099      |
| cosine_precision@5  | 0.0762     |
| cosine_precision@10 | 0.0508     |
| cosine_recall@1     | 0.1569     |
| cosine_recall@3     | 0.2971     |
| cosine_recall@5     | 0.3808     |
| cosine_recall@10    | 0.5084     |
| cosine_ndcg@10      | 0.3139     |
| cosine_mrr@10       | 0.2541     |
| **cosine_map@100**  | **0.2757** |

#### 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   | 0.1736     |
| cosine_accuracy@3   | 0.3138     |
| cosine_accuracy@5   | 0.3954     |
| cosine_accuracy@10  | 0.5377     |
| cosine_precision@1  | 0.1736     |
| cosine_precision@3  | 0.1046     |
| cosine_precision@5  | 0.0791     |
| cosine_precision@10 | 0.0538     |
| cosine_recall@1     | 0.1736     |
| cosine_recall@3     | 0.3138     |
| cosine_recall@5     | 0.3954     |
| cosine_recall@10    | 0.5377     |
| cosine_ndcg@10      | 0.3324     |
| cosine_mrr@10       | 0.27       |
| **cosine_map@100**  | **0.2901** |

#### 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   | 0.1506     |
| cosine_accuracy@3   | 0.2908     |
| cosine_accuracy@5   | 0.4017     |
| cosine_accuracy@10  | 0.5356     |
| cosine_precision@1  | 0.1506     |
| cosine_precision@3  | 0.0969     |
| cosine_precision@5  | 0.0803     |
| cosine_precision@10 | 0.0536     |
| cosine_recall@1     | 0.1506     |
| cosine_recall@3     | 0.2908     |
| cosine_recall@5     | 0.4017     |
| cosine_recall@10    | 0.5356     |
| cosine_ndcg@10      | 0.319      |
| cosine_mrr@10       | 0.2527     |
| **cosine_map@100**  | **0.2729** |

#### 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   | 0.1674     |
| cosine_accuracy@3   | 0.3201     |
| cosine_accuracy@5   | 0.4163     |
| cosine_accuracy@10  | 0.5481     |
| cosine_precision@1  | 0.1674     |
| cosine_precision@3  | 0.1067     |
| cosine_precision@5  | 0.0833     |
| cosine_precision@10 | 0.0548     |
| cosine_recall@1     | 0.1674     |
| cosine_recall@3     | 0.3201     |
| cosine_recall@5     | 0.4163     |
| cosine_recall@10    | 0.5481     |
| cosine_ndcg@10      | 0.3354     |
| cosine_mrr@10       | 0.27       |
| **cosine_map@100**  | **0.2892** |

#### 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   | 0.1548     |
| cosine_accuracy@3   | 0.2845     |
| cosine_accuracy@5   | 0.3515     |
| cosine_accuracy@10  | 0.5209     |
| cosine_precision@1  | 0.1548     |
| cosine_precision@3  | 0.0948     |
| cosine_precision@5  | 0.0703     |
| cosine_precision@10 | 0.0521     |
| cosine_recall@1     | 0.1548     |
| cosine_recall@3     | 0.2845     |
| cosine_recall@5     | 0.3515     |
| cosine_recall@10    | 0.5209     |
| cosine_ndcg@10      | 0.3117     |
| cosine_mrr@10       | 0.2482     |
| **cosine_map@100**  | **0.2686** |

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## Bias, Risks and Limitations

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 6,692 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: 6 tokens</li><li>mean: 44.83 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.89 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                  | anchor                                                                                                      |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|
  | <code>Els residus comercials o industrials assimilables als municipals que hauran d'acreditar si disposen d'un gestor autoritzat per a la gestió dels residus.</code>                                                     | <code>Quins són els residus que es recullen en el servei municipal complementari?</code>                    |
  | <code>L'Ajuntament de Sitges ofereix ajuts econòmics a famílies amb recursos insuficients per accedir a la realització d'activitats de lleure...</code>                                                                   | <code>Quin és el paper de l'Ajuntament de Sitges en la promoció de l'educació no formal i de lleure?</code> |
  | <code>Permet comunicar les intervencions necessàries per executar una instal·lació/remodelació d’autoconsum amb energia solar fotovoltaica amb una potència instal·lada inferior a 100 kWp en sòl urbà consolidat.</code> | <code>Quin és el propòsit de la remodelació d'una instal·lació d'autoconsum?</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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3819     | 10      | 3.3449        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.7637     | 20      | 2.0557        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9928     | 26      | -             | 0.2440                  | 0.2408                 | 0.2590                 | 0.2439                 | 0.2379                | 0.2512                 |
| 1.1456     | 30      | 1.4634        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.5274     | 40      | 0.8163        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9093     | 50      | 0.6103        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9857     | 52      | -             | 0.2621                  | 0.2683                 | 0.2483                 | 0.2629                 | 0.2404                | 0.2472                 |
| 2.2912     | 60      | 0.4854        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.6730     | 70      | 0.2796        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9785     | 78      | -             | 0.2701                  | 0.2697                 | 0.2761                 | 0.2845                 | 0.2673                | 0.2709                 |
| 3.0549     | 80      | 0.2458        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4368     | 90      | 0.2616        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.8186     | 100     | 0.174         | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9714     | 104     | -             | 0.2729                  | 0.2863                 | 0.2858                 | 0.2853                 | 0.2656                | 0.2752                 |
| 4.2005     | 110     | 0.1841        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.5823     | 120     | 0.1668        | -                       | -                      | -                      | -                      | -                     | -                      |
| **4.9642** | **130** | **0.1484**    | **0.2763**              | **0.2892**             | **0.2729**             | **0.2901**             | **0.2686**            | **0.2757**             |

* 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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