sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:2844 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:2844
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: La unió d’aquests dos documents conforma l’Informe d’Avaluació
    de l’Edifici (IAE).
  sentences:
  - Quin és el requisit per a rebre els ajuts econòmics per a les empreses?
  - Quin és el resultat de la unió de la Inspecció Tècnica de l’Edifici (ITE) i dels
    certificats energètics?
  - Quin és el termini per sol·licitar la renovació del carnet de persona cuidadora?
- source_sentence: La Inspecció Tècnica dels Edificis (ITE) permet identificar les
    oportunitats de millora de l'eficiència energètica i implementar mesures de rehabilitació.
  sentences:
  - Quin és el benefici de l'activitat del Viver dels Avis de Sitges per a la qualitat
    de vida?
  - Com puc saber si puc ser cuidador?
  - Quin és el paper de la Inspecció Tècnica dels Edificis (ITE) en la millora de
    l'eficiència energètica?
- source_sentence: A les zones blaves els parquímetres i serveis de pagament reconeixen
    les matricules dels vehicles acreditats.
  sentences:
  - Quin és el paper de la mediació en una denúncia?
  - Quin és el paper de les persones físiques?
  - Quin és el procediment per estacionar a les zones blaves amb l'acreditació de
    resident?
- source_sentence: Els establiments oberts al públic destinats a espectacles cinematogràfics.
    Els establiments oberts al públic destinats a espectacles públics i activitats
    recreatives musicals amb un aforament autoritzat fins a 150 persones.
  sentences:
  - Quin és el resultat esperat després de la intervenció de l'Ajuntament en les denúncies
    sanitàries?
  - Quin és el requisit de superfície construïda per als restaurants musicals?
  - Quins establiments oberts al públic han de comunicar la seva obertura a l'Ajuntament?
- source_sentence: El Decret 97/2002, de 5 de març, regula la concessió de la targeta
    d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar
    el desplaçament de les persones amb mobilitat reduïda.
  sentences:
  - Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?
  - Quin és el paper de la Junta de Govern Local?
  - Quin és l'organisme que emet el certificat de serveis prestats?
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: 0.11814345991561181
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23277074542897327
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3129395218002813
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4644163150492264
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11814345991561181
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07759024847632442
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06258790436005626
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.046441631504922636
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11814345991561181
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23277074542897327
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3129395218002813
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4644163150492264
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26553370933458276
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20527392672962277
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22599508422976106
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.11575246132208157
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2289732770745429
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3112517580872011
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.46568213783403656
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11575246132208157
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07632442569151429
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.062250351617440226
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04656821378340366
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11575246132208157
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2289732770745429
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3112517580872011
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.46568213783403656
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26414039995115557
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20311873507021158
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22355973027797246
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.11912798874824192
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23277074542897327
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.31758087201125174
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.46582278481012657
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11912798874824192
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07759024847632444
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06351617440225035
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04658227848101265
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11912798874824192
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23277074542897327
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.31758087201125174
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.46582278481012657
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26671990925029193
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20635646194717913
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22673055490318922
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.11533052039381153
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22658227848101264
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30857946554149085
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.45668073136427567
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11533052039381153
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07552742616033756
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06171589310829817
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04566807313642757
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11533052039381153
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22658227848101264
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30857946554149085
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.45668073136427567
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26044811042246035
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20098218471636187
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22169039893772347
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.11181434599156118
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22334739803094233
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30253164556962026
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.45288326300984527
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11181434599156118
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07444913267698076
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06050632911392405
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.045288326300984526
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11181434599156118
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22334739803094233
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30253164556962026
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.45288326300984527
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2566428043422134
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19724806331346384
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21784479785600805
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.10689170182841069
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21251758087201125
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.28846694796061884
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.42967651195499296
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10689170182841069
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07083919362400375
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05769338959212378
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0429676511954993
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10689170182841069
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21251758087201125
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.28846694796061884
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.42967651195499296
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2438421466584992
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1875642957604982
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2080904354707231
      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-006-5ep")
# Run inference
sentences = [
    'El Decret 97/2002, de 5 de març, regula la concessió de la targeta d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar el desplaçament de les persones amb mobilitat reduïda.',
    "Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?",
    'Quin és el paper de la Junta de Govern Local?',
]
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   | 0.1181    |
| cosine_accuracy@3   | 0.2328    |
| cosine_accuracy@5   | 0.3129    |
| cosine_accuracy@10  | 0.4644    |
| cosine_precision@1  | 0.1181    |
| cosine_precision@3  | 0.0776    |
| cosine_precision@5  | 0.0626    |
| cosine_precision@10 | 0.0464    |
| cosine_recall@1     | 0.1181    |
| cosine_recall@3     | 0.2328    |
| cosine_recall@5     | 0.3129    |
| cosine_recall@10    | 0.4644    |
| cosine_ndcg@10      | 0.2655    |
| cosine_mrr@10       | 0.2053    |
| **cosine_map@100**  | **0.226** |

#### 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   | 0.1158     |
| cosine_accuracy@3   | 0.229      |
| cosine_accuracy@5   | 0.3113     |
| cosine_accuracy@10  | 0.4657     |
| cosine_precision@1  | 0.1158     |
| cosine_precision@3  | 0.0763     |
| cosine_precision@5  | 0.0623     |
| cosine_precision@10 | 0.0466     |
| cosine_recall@1     | 0.1158     |
| cosine_recall@3     | 0.229      |
| cosine_recall@5     | 0.3113     |
| cosine_recall@10    | 0.4657     |
| cosine_ndcg@10      | 0.2641     |
| cosine_mrr@10       | 0.2031     |
| **cosine_map@100**  | **0.2236** |

#### 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   | 0.1191     |
| cosine_accuracy@3   | 0.2328     |
| cosine_accuracy@5   | 0.3176     |
| cosine_accuracy@10  | 0.4658     |
| cosine_precision@1  | 0.1191     |
| cosine_precision@3  | 0.0776     |
| cosine_precision@5  | 0.0635     |
| cosine_precision@10 | 0.0466     |
| cosine_recall@1     | 0.1191     |
| cosine_recall@3     | 0.2328     |
| cosine_recall@5     | 0.3176     |
| cosine_recall@10    | 0.4658     |
| cosine_ndcg@10      | 0.2667     |
| cosine_mrr@10       | 0.2064     |
| **cosine_map@100**  | **0.2267** |

#### 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   | 0.1153     |
| cosine_accuracy@3   | 0.2266     |
| cosine_accuracy@5   | 0.3086     |
| cosine_accuracy@10  | 0.4567     |
| cosine_precision@1  | 0.1153     |
| cosine_precision@3  | 0.0755     |
| cosine_precision@5  | 0.0617     |
| cosine_precision@10 | 0.0457     |
| cosine_recall@1     | 0.1153     |
| cosine_recall@3     | 0.2266     |
| cosine_recall@5     | 0.3086     |
| cosine_recall@10    | 0.4567     |
| cosine_ndcg@10      | 0.2604     |
| cosine_mrr@10       | 0.201      |
| **cosine_map@100**  | **0.2217** |

#### 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   | 0.1118     |
| cosine_accuracy@3   | 0.2233     |
| cosine_accuracy@5   | 0.3025     |
| cosine_accuracy@10  | 0.4529     |
| cosine_precision@1  | 0.1118     |
| cosine_precision@3  | 0.0744     |
| cosine_precision@5  | 0.0605     |
| cosine_precision@10 | 0.0453     |
| cosine_recall@1     | 0.1118     |
| cosine_recall@3     | 0.2233     |
| cosine_recall@5     | 0.3025     |
| cosine_recall@10    | 0.4529     |
| cosine_ndcg@10      | 0.2566     |
| cosine_mrr@10       | 0.1972     |
| **cosine_map@100**  | **0.2178** |

#### 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   | 0.1069     |
| cosine_accuracy@3   | 0.2125     |
| cosine_accuracy@5   | 0.2885     |
| cosine_accuracy@10  | 0.4297     |
| cosine_precision@1  | 0.1069     |
| cosine_precision@3  | 0.0708     |
| cosine_precision@5  | 0.0577     |
| cosine_precision@10 | 0.043      |
| cosine_recall@1     | 0.1069     |
| cosine_recall@3     | 0.2125     |
| cosine_recall@5     | 0.2885     |
| cosine_recall@10    | 0.4297     |
| cosine_ndcg@10      | 0.2438     |
| cosine_mrr@10       | 0.1876     |
| **cosine_map@100**  | **0.2081** |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 2,844 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                           | anchor                                                                             |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 3 tokens</li><li>mean: 49.45 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 tokens</li><li>max: 45 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>Per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural, les entitats o associacions culturals de Sitges han de tenir una seu social a la ciutat de Sitges i estar inscrites en el Registre d'Entitats de la Generalitat de Catalunya.</code>    | <code>Quin és el requisit per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural?</code> |
  | <code>La cessió entre tercers, només es contempla en el cas de sepultures de construcció particular que hagin estat donades d'alta amb una anterioritat de 10 anys a la data de sol·licitud de la cessió.</code>                                                                  | <code>Quin és el paper de la persona que, legalment hi tingui dret, en la cessió entre tercers?</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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8989     | 10     | 3.2114        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9888     | 11     | -             | 0.2144                  | 0.2008                 | 0.2070                 | 0.2126                 | 0.1842                | 0.2126                 |
| 1.7978     | 20     | 1.5622        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9775     | 22     | -             | 0.2179                  | 0.2101                 | 0.2169                 | 0.2180                 | 0.2012                | 0.2193                 |
| 2.6966     | 30     | 0.7882        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9663     | 33     | -             | 0.2239                  | 0.2162                 | 0.2220                 | 0.2238                 | 0.2070                | 0.2222                 |
| 3.5955     | 40     | 0.4956        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9551     | 44     | -             | 0.2270                  | 0.2177                 | 0.2231                 | 0.2278                 | 0.2084                | 0.2255                 |
| 4.4944     | 50     | 0.392         | -                       | -                      | -                      | -                      | -                     | -                      |
| **4.9438** | **55** | **-**         | **0.226**               | **0.2178**             | **0.2217**             | **0.2267**             | **0.2081**            | **0.2236**             |

* 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->