sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:9717 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:9717
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Per accedir a un habitatge amb protecció oficial al municipi de
    Sitges s'ha d'estar inscrit en el Registre municipal de sol·licitants.
  sentences:
  - Quin és el motiu perquè la renovació de la inscripció en el Registre municipal
    de sol·licitants d'habitatge amb protecció oficial de Sitges és necessària?
  - Quin és el sector que es veu afectat per la disminució d'ingressos?
  - Quin és el propòsit de la descripció de l'activitat?
- source_sentence: Aquest tràmit permet presentar ofertes i/o pressupostos sol·licitats
    per l'Ajuntament de Sitges en procediments de contractes menors.
  sentences:
  - Quin és el requisit per a sol·licitar l'ajut econòmic a l'Ajuntament de Sitges?
  - Què passa amb la llicència de gual quan es vol reduir les característiques físiques?
  - Quin és el propòsit del tràmit de presentació d'ofertes?
- source_sentence: Estudis universitaris fins al grau de llicenciatura
  sentences:
  - Quin és el propòsit de la subvenció per a les persones autònomes?
  - Quin és el requisit per als establiments oberts al públic destinats a espectacles
    públics i activitats recreatives musicals?
  - Quins estudis universitaris es poden fer amb aquesta ajuda?
- source_sentence: Les entitats especialitzades i acreditades com a proveïdores de
    la Xarxa de Serveis Socials d'Atenció Pública interesades en la la gestió delegada
    dels serveis públics de l'Ajuntament de Sitges així determinats, poden presentar-se
    a les respectives convocatòries per a l'adjudicació.
  sentences:
  - On comencen i acaben les activitats de l'Estiu Jove?
  - Quin és el benefici per a l'Ajuntament de Sitges de la gestió delegada?
  - Quin és el paper de l’organització en la valoració d'una proposta?
- source_sentence: Publicada la llista d'infants admesos i exclosos a les estades
    esportives, s'obre un termini perquè les persones admeses puguin demanar qualsevol
    canvi a la sol·licitud inicial.
  sentences:
  - Quin és el contingut del volant històric de convivència?
  - Quin és el període en què es pot demanar un canvi a la sol·licitud inicial?
  - Quin és el paper de les escoles de Sitges en les activitats de foment de l'esport
    escolar
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: 0.10126582278481013
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.18565400843881857
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.24472573839662448
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34177215189873417
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10126582278481013
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.061884669479606184
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0489451476793249
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03417721518987342
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10126582278481013
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.18565400843881857
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.24472573839662448
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34177215189873417
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20497940546236365
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1631588641082312
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.18190274772574827
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.0970464135021097
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.18143459915611815
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2616033755274262
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34177215189873417
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0970464135021097
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06047819971870604
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.052320675105485236
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.034177215189873406
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0970464135021097
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.18143459915611815
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2616033755274262
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34177215189873417
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20447797235629017
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16207219878105952
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.18114215201809386
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.08438818565400844
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.17721518987341772
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.23628691983122363
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34177215189873417
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08438818565400844
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05907172995780591
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04725738396624472
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03417721518987342
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08438818565400844
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.17721518987341772
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.23628691983122363
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34177215189873417
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.19477348596574798
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.15014232134485297
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16826302734813764
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.0759493670886076
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.16877637130801687
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.23628691983122363
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34177215189873417
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0759493670886076
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05625879043600562
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04725738396624473
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.034177215189873406
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0759493670886076
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.16877637130801687
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.23628691983122363
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34177215189873417
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18887676996048183
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14248208425423614
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.15960797563687307
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.08016877637130802
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.16455696202531644
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2320675105485232
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.32489451476793246
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08016877637130802
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05485232067510549
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.046413502109704644
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.032489451476793246
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08016877637130802
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.16455696202531644
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2320675105485232
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.32489451476793246
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.18920967116655296
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14736454356707523
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1622413863660417
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.046413502109704644
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.1518987341772152
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.21940928270042195
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.270042194092827
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.046413502109704644
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.050632911392405056
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04388185654008439
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0270042194092827
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.046413502109704644
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1518987341772152
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.21940928270042195
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.270042194092827
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.15109586098353134
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11371308016877635
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.12600329900444687
      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/sitges-v2-5ep")
# Run inference
sentences = [
    "Publicada la llista d'infants admesos i exclosos a les estades esportives, s'obre un termini perquè les persones admeses puguin demanar qualsevol canvi a la sol·licitud inicial.",
    'Quin és el període en què es pot demanar un canvi a la sol·licitud inicial?',
    'Quin és el contingut del volant històric de convivència?',
]
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>
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### Out-of-Scope Use

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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   | 0.1013     |
| cosine_accuracy@3   | 0.1857     |
| cosine_accuracy@5   | 0.2447     |
| cosine_accuracy@10  | 0.3418     |
| cosine_precision@1  | 0.1013     |
| cosine_precision@3  | 0.0619     |
| cosine_precision@5  | 0.0489     |
| cosine_precision@10 | 0.0342     |
| cosine_recall@1     | 0.1013     |
| cosine_recall@3     | 0.1857     |
| cosine_recall@5     | 0.2447     |
| cosine_recall@10    | 0.3418     |
| cosine_ndcg@10      | 0.205      |
| cosine_mrr@10       | 0.1632     |
| **cosine_map@100**  | **0.1819** |

#### 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   | 0.097      |
| cosine_accuracy@3   | 0.1814     |
| cosine_accuracy@5   | 0.2616     |
| cosine_accuracy@10  | 0.3418     |
| cosine_precision@1  | 0.097      |
| cosine_precision@3  | 0.0605     |
| cosine_precision@5  | 0.0523     |
| cosine_precision@10 | 0.0342     |
| cosine_recall@1     | 0.097      |
| cosine_recall@3     | 0.1814     |
| cosine_recall@5     | 0.2616     |
| cosine_recall@10    | 0.3418     |
| cosine_ndcg@10      | 0.2045     |
| cosine_mrr@10       | 0.1621     |
| **cosine_map@100**  | **0.1811** |

#### 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   | 0.0844     |
| cosine_accuracy@3   | 0.1772     |
| cosine_accuracy@5   | 0.2363     |
| cosine_accuracy@10  | 0.3418     |
| cosine_precision@1  | 0.0844     |
| cosine_precision@3  | 0.0591     |
| cosine_precision@5  | 0.0473     |
| cosine_precision@10 | 0.0342     |
| cosine_recall@1     | 0.0844     |
| cosine_recall@3     | 0.1772     |
| cosine_recall@5     | 0.2363     |
| cosine_recall@10    | 0.3418     |
| cosine_ndcg@10      | 0.1948     |
| cosine_mrr@10       | 0.1501     |
| **cosine_map@100**  | **0.1683** |

#### 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   | 0.0759     |
| cosine_accuracy@3   | 0.1688     |
| cosine_accuracy@5   | 0.2363     |
| cosine_accuracy@10  | 0.3418     |
| cosine_precision@1  | 0.0759     |
| cosine_precision@3  | 0.0563     |
| cosine_precision@5  | 0.0473     |
| cosine_precision@10 | 0.0342     |
| cosine_recall@1     | 0.0759     |
| cosine_recall@3     | 0.1688     |
| cosine_recall@5     | 0.2363     |
| cosine_recall@10    | 0.3418     |
| cosine_ndcg@10      | 0.1889     |
| cosine_mrr@10       | 0.1425     |
| **cosine_map@100**  | **0.1596** |

#### 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   | 0.0802     |
| cosine_accuracy@3   | 0.1646     |
| cosine_accuracy@5   | 0.2321     |
| cosine_accuracy@10  | 0.3249     |
| cosine_precision@1  | 0.0802     |
| cosine_precision@3  | 0.0549     |
| cosine_precision@5  | 0.0464     |
| cosine_precision@10 | 0.0325     |
| cosine_recall@1     | 0.0802     |
| cosine_recall@3     | 0.1646     |
| cosine_recall@5     | 0.2321     |
| cosine_recall@10    | 0.3249     |
| cosine_ndcg@10      | 0.1892     |
| cosine_mrr@10       | 0.1474     |
| **cosine_map@100**  | **0.1622** |

#### 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   | 0.0464    |
| cosine_accuracy@3   | 0.1519    |
| cosine_accuracy@5   | 0.2194    |
| cosine_accuracy@10  | 0.27      |
| cosine_precision@1  | 0.0464    |
| cosine_precision@3  | 0.0506    |
| cosine_precision@5  | 0.0439    |
| cosine_precision@10 | 0.027     |
| cosine_recall@1     | 0.0464    |
| cosine_recall@3     | 0.1519    |
| cosine_recall@5     | 0.2194    |
| cosine_recall@10    | 0.27      |
| cosine_ndcg@10      | 0.1511    |
| cosine_mrr@10       | 0.1137    |
| **cosine_map@100**  | **0.126** |

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## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 9,717 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: 8 tokens</li><li>mean: 49.79 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.83 tokens</li><li>max: 43 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 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 requisit per a obtenir les subvencions per a projectes i activitats 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 requisit per a obtenir les subvencions per a projectes i activitats esportives?</code> |
  | <code>No es proporciona informació sobre el requisit principal per obtenir el certificat.</code>                                                                                                                                                                                                                                                                                                     | <code>Quin és el requisit principal per obtenir el certificat?</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 |
|:-------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2632  | 10     | 3.2527        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.5263  | 20     | 1.9679        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.7895  | 30     | 1.8319        | -                       | -                      | -                      | -                      | -                     | -                      |
| **1.0** | **38** | **-**         | **0.1819**              | **0.1622**             | **0.1596**             | **0.1683**             | **0.126**             | **0.1811**             |
| 1.0526  | 40     | 1.3358        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.3158  | 50     | 1.1166        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.5789  | 60     | 0.8715        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8421  | 70     | 0.8801        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.0     | 76     | -             | 0.1819                  | 0.1622                 | 0.1596                 | 0.1683                 | 0.1260                | 0.1811                 |
| 2.1053  | 80     | 0.6515        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.3684  | 90     | 0.536         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.6316  | 100    | 0.4682        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.8947  | 110    | 0.4686        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.0     | 114    | -             | 0.1819                  | 0.1622                 | 0.1596                 | 0.1683                 | 0.1260                | 0.1811                 |
| 3.1579  | 120    | 0.3161        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4211  | 130    | 0.3554        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.6842  | 140    | 0.2886        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9474  | 150    | 0.2616        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.0     | 152    | -             | 0.1819                  | 0.1622                 | 0.1596                 | 0.1683                 | 0.1260                | 0.1811                 |
| 4.2105  | 160    | 0.1902        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.4737  | 170    | 0.1894        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.7368  | 180    | 0.1858        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.0     | 190    | 0.1939        | 0.1819                  | 0.1622                 | 0.1596                 | 0.1683                 | 0.1260                | 0.1811                 |

* 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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