---
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:8769
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Aquelles persones que fan un ús regular i continuat de la deixalleria
    municipal poden gaudir d’una bonificació del 20% sobre la quota de les taxes per
    recollida, tractament i eliminació d'escombraries i altres residus urbans domiciliaris.
  sentences:
  - Quin és el contingut dels documents dirigits a l'Ajuntament de Sitges?
  - Quin és el benefici de la deixalleria municipal?
  - Quin és el mètode de pagament dels ajuts atorgats en cas de normalitat?
- source_sentence: Les subvencions per al desenvolupament i/o consolidació de sectors
    econòmics del municipi tenen com a objectiu generar un benefici ambiental per
    al municipi, a través de la promoció de pràctiques sostenibles.
  sentences:
  - Quin és el requisit per a la llicència per a la modificació d'un règim de propietat
    horitzontal?
  - Quin és el benefici ambiental esperat de les subvencions per al desenvolupament
    i/o consolidació de sectors econòmics del municipi?
  - Quin és el propòsit de la liquidació de l'import corresponent a l'exercici?
- source_sentence: Aquelles persones que s'hagin inscrit a les estades esportives
    organitzades per l'Ajuntament de Sitges i que formin part d'una unitat familiar
    amb uns ingressos bruts mensuals, que una vegada dividits pel nombre de membres,
    siguin inferiors entre una i dues terceres parts de l'IPREM, poden sol·licitar
    una reducció de la quota d'aquestes activitats o l'aplicació de la corresponent
    tarifa bonificada establerta en les ordenances dels preus públics.
  sentences:
  - Quin és el benefici de les subvencions per a les entitats culturals?
  - Quin és el paper de l'IPREM en la sol·licitud de reducció de la quota d'una estada
    esportiva?
  - Quin és el paper de l'Ajuntament en la resolució d'una situació sanitària no adequada
    en un domini particular?
- source_sentence: La inscripció al cens municipal facilita la recuperació d’aquests
    animals en cas de pèrdua alhora que permet a l’Ajuntament disposar de les dades
    necessàries en cas que s’hagin de realitzar campanyes sanitàries.
  sentences:
  - Quin és el tipus de serveis auxiliars que es consideren despeses elegibles?
  - Quin és el benefici d'estacionar a les zones verdes per als residents?
  - Quin és el motiu pel qual es crea el cens municipal d’animals de companyia?
- source_sentence: A la nostra vila hi ha veïns i veïnes que els agradaria tornar
    a fer de pagès o provar-ho per primera vegada.
  sentences:
  - Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?
  - Quin és el propòsit del carnet de conductor de taxi?
  - Quin és el paper de les persones en relació amb les indemnitzacions?
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: 0.11054852320675106
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2270042194092827
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30548523206751055
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4531645569620253
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11054852320675106
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07566807313642755
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06109704641350212
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04531645569620253
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11054852320675106
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2270042194092827
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30548523206751055
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4531645569620253
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25622764604771076
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1965350612818966
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21859411055862238
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.11561181434599156
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2320675105485232
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.31139240506329113
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.44556962025316454
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11561181434599156
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07735583684950773
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06227848101265824
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.044556962025316456
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11561181434599156
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2320675105485232
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.31139240506329113
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.44556962025316454
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2579660315889156
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20086732301922164
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22344331787470567
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.10379746835443038
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2210970464135021
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2970464135021097
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.43966244725738396
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10379746835443038
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07369901547116735
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05940928270042194
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.043966244725738395
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10379746835443038
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2210970464135021
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2970464135021097
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.43966244725738396
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2473619714740055
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.18892840399169497
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21182552044674802
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.10042194092827005
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21518987341772153
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2978902953586498
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4438818565400844
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10042194092827005
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07172995780590716
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05957805907172995
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04438818565400844
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10042194092827005
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21518987341772153
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2978902953586498
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4438818565400844
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2479637375723138
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.18831156653941447
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21130848497160895
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.10886075949367088
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22616033755274262
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3029535864978903
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4413502109704641
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10886075949367088
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07538677918424753
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.060590717299578066
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04413502109704641
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10886075949367088
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22616033755274262
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3029535864978903
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4413502109704641
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25366131313332974
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19639441430580665
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2187767008895725
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.09367088607594937
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2742616033755274
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4177215189873418
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.09367088607594937
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05485232067510549
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04177215189873418
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.09367088607594937
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2742616033755274
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4177215189873418
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.23046340016141767
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1738279418659165
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.19782551958501599
      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-003-5ep")
# Run inference
sentences = [
    'A la nostra vila hi ha veïns i veïnes que els agradaria tornar a fer de pagès o provar-ho per primera vegada.',
    "Quin és l'objectiu principal de l'activitat del Viver dels Avis de Sitges?",
    'Quin és el paper de les persones en relació amb les indemnitzacions?',
]
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   | 0.1105     |
| cosine_accuracy@3   | 0.227      |
| cosine_accuracy@5   | 0.3055     |
| cosine_accuracy@10  | 0.4532     |
| cosine_precision@1  | 0.1105     |
| cosine_precision@3  | 0.0757     |
| cosine_precision@5  | 0.0611     |
| cosine_precision@10 | 0.0453     |
| cosine_recall@1     | 0.1105     |
| cosine_recall@3     | 0.227      |
| cosine_recall@5     | 0.3055     |
| cosine_recall@10    | 0.4532     |
| cosine_ndcg@10      | 0.2562     |
| cosine_mrr@10       | 0.1965     |
| **cosine_map@100**  | **0.2186** |

#### 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   | 0.1156     |
| cosine_accuracy@3   | 0.2321     |
| cosine_accuracy@5   | 0.3114     |
| cosine_accuracy@10  | 0.4456     |
| cosine_precision@1  | 0.1156     |
| cosine_precision@3  | 0.0774     |
| cosine_precision@5  | 0.0623     |
| cosine_precision@10 | 0.0446     |
| cosine_recall@1     | 0.1156     |
| cosine_recall@3     | 0.2321     |
| cosine_recall@5     | 0.3114     |
| cosine_recall@10    | 0.4456     |
| cosine_ndcg@10      | 0.258      |
| cosine_mrr@10       | 0.2009     |
| **cosine_map@100**  | **0.2234** |

#### 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   | 0.1038     |
| cosine_accuracy@3   | 0.2211     |
| cosine_accuracy@5   | 0.297      |
| cosine_accuracy@10  | 0.4397     |
| cosine_precision@1  | 0.1038     |
| cosine_precision@3  | 0.0737     |
| cosine_precision@5  | 0.0594     |
| cosine_precision@10 | 0.044      |
| cosine_recall@1     | 0.1038     |
| cosine_recall@3     | 0.2211     |
| cosine_recall@5     | 0.297      |
| cosine_recall@10    | 0.4397     |
| cosine_ndcg@10      | 0.2474     |
| cosine_mrr@10       | 0.1889     |
| **cosine_map@100**  | **0.2118** |

#### 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   | 0.1004     |
| cosine_accuracy@3   | 0.2152     |
| cosine_accuracy@5   | 0.2979     |
| cosine_accuracy@10  | 0.4439     |
| cosine_precision@1  | 0.1004     |
| cosine_precision@3  | 0.0717     |
| cosine_precision@5  | 0.0596     |
| cosine_precision@10 | 0.0444     |
| cosine_recall@1     | 0.1004     |
| cosine_recall@3     | 0.2152     |
| cosine_recall@5     | 0.2979     |
| cosine_recall@10    | 0.4439     |
| cosine_ndcg@10      | 0.248      |
| cosine_mrr@10       | 0.1883     |
| **cosine_map@100**  | **0.2113** |

#### 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   | 0.1089     |
| cosine_accuracy@3   | 0.2262     |
| cosine_accuracy@5   | 0.303      |
| cosine_accuracy@10  | 0.4414     |
| cosine_precision@1  | 0.1089     |
| cosine_precision@3  | 0.0754     |
| cosine_precision@5  | 0.0606     |
| cosine_precision@10 | 0.0441     |
| cosine_recall@1     | 0.1089     |
| cosine_recall@3     | 0.2262     |
| cosine_recall@5     | 0.303      |
| cosine_recall@10    | 0.4414     |
| cosine_ndcg@10      | 0.2537     |
| cosine_mrr@10       | 0.1964     |
| **cosine_map@100**  | **0.2188** |

#### 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   | 0.0937     |
| cosine_accuracy@3   | 0.2        |
| cosine_accuracy@5   | 0.2743     |
| cosine_accuracy@10  | 0.4177     |
| cosine_precision@1  | 0.0937     |
| cosine_precision@3  | 0.0667     |
| cosine_precision@5  | 0.0549     |
| cosine_precision@10 | 0.0418     |
| cosine_recall@1     | 0.0937     |
| cosine_recall@3     | 0.2        |
| cosine_recall@5     | 0.2743     |
| cosine_recall@10    | 0.4177     |
| cosine_ndcg@10      | 0.2305     |
| cosine_mrr@10       | 0.1738     |
| **cosine_map@100**  | **0.1978** |

<!--
## 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: 8,769 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: 5 tokens</li><li>mean: 49.22 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                             | anchor                                                                             |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges.</code>                                                                                                                    | <code>Quin és el benefici de les subvencions per a les entitats esportives?</code> |
  | <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el període d'execució dels projectes i activitats esportives?</code> |
  | <code>Certificat on s'indica el nombre d'habitatges que configuren el padró de l'Impost sobre Béns Immobles del municipi o bé d'una part d'aquest.</code>                                                                                                                                                                                                                                            | <code>Quin és el contingut del certificat del nombre d'habitatges?</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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2914     | 10      | 3.6318        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.5829     | 20      | 2.329         | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.8743     | 30      | 1.5614        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9909     | 34      | -             | 0.2055                  | 0.1998                 | 0.2020                 | 0.2001                 | 0.1903                | 0.2019                 |
| 1.1658     | 40      | 1.2383        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.4572     | 50      | 0.9323        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.7486     | 60      | 0.6616        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9818     | 68      | -             | 0.2244                  | 0.2063                 | 0.2223                 | 0.2166                 | 0.2011                | 0.2235                 |
| 2.0401     | 70      | 0.5545        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.3315     | 80      | 0.5043        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.6230     | 90      | 0.3542        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9144     | 100     | 0.3095        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9727     | 102     | -             | 0.2224                  | 0.2046                 | 0.2170                 | 0.2100                 | 0.1986                | 0.2144                 |
| 3.2058     | 110     | 0.2863        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4973     | 120     | 0.2329        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7887     | 130     | 0.2353        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9927     | 137     | -             | 0.2197                  | 0.2112                 | 0.2098                 | 0.2154                 | 0.1949                | 0.2178                 |
| 4.0801     | 140     | 0.1759        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.3716     | 150     | 0.2308        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.6630     | 160     | 0.1656        | -                       | -                      | -                      | -                      | -                     | -                      |
| **4.9545** | **170** | **0.1812**    | **0.2186**              | **0.2188**             | **0.2113**             | **0.2118**             | **0.1978**            | **0.2234**             |

* 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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