sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:5214 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:5214
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Pel que fa als avals, la Junta de Govern Local en sessió celebrada
    el 4 de juliol de 2006, va aprovar els models d'aval en funció del concepte a
    garantir.
  sentences:
  - Quin és el benefici de la unitat de queixes i suggeriments per a la qualitat dels
    serveis de l'Ajuntament de Sitges?
  - Quin és el paper de la Junta de Govern Local?
  - Quin és el propòsit més important del tràmit de canvi de titular de la llicència
    de gual?
- source_sentence: Per a tenir dret a ésser inscrit en el Registre de Sol·licitants
    d'Habitatge amb Protecció Oficial s'han de complir els procediments i els requisits
    establerts per normativa.
  sentences:
  - Quin és el paper de la persona sol·licitant en la gestió de les fiances o dipòsits
    d'una llicència d'obra?
  - Quin és el benefici de complir els procediments i els requisits establerts per
    normativa?
  - Quin és el centre cultural que es troba a l'Escorxador de Sitges i ofereix activitats
    culturals?
- source_sentence: Aquest tràmit permet comunicar a l'Ajuntament de Sitges la finalització
    de les obres de nova construcció, o bé aquelles que hagin estat objecte de modificació
    substancial o d’ampliació quan per a l’autorització de les obres s’hagi exigit
    un projecte tècnic i a l’empara d’una llicència urbanística d’obra major.
  sentences:
  - Què passa si la modificació no té efectes sobre les persones o el medi ambient?
  - Quin és el requisit principal per a la gestió diària d'una colònia felina?
  - Quin és el paper del tràmit de comunicació prèvia de primera utilització i ocupació
    d'edificis i instal·lacions en el procés d'obtenció de la llicència urbanística
    d’obra major?
- source_sentence: Es tracta dels ajuts per a la realització de la Inspecció Tècnica
    de l’Edifici (ITE) conjuntament amb l’elaboració dels certificats energètics.
  sentences:
  - Quins són els tipus de garanties que es poden ingressar?
  - Quin és el procés d’elaboració dels certificats energètics?
  - Quin és el paper de la consulta prèvia de classificació d'activitat en la tramitació
    administrativa municipal?
- source_sentence: Les queixes, observacions i suggeriments són una eina important
    per a millorar la qualitat dels serveis municipals.
  sentences:
  - Quin és el propòsit dels ajuts econòmics?
  - Què és el que es busca amb les queixes, observacions i suggeriments?
  - Qui són les persones beneficiàries de l'ajut per a la creació de noves empreses?
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: 0.14367088607594936
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2818565400843882
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3930379746835443
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5664556962025317
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14367088607594936
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09395218002812938
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07860759493670887
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05664556962025316
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14367088607594936
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2818565400843882
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3930379746835443
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5664556962025317
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.32426778614918705
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.25066212912731944
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2694799737895368
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.1470464135021097
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2871308016877637
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.390084388185654
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5630801687763713
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1470464135021097
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09571026722925456
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07801687763713079
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.056308016877637125
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1470464135021097
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2871308016877637
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.390084388185654
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5630801687763713
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.32549268557195893
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.25325421940928294
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.272264774489146
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.14177215189873418
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.28375527426160335
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3890295358649789
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5620253164556962
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14177215189873418
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09458509142053445
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07780590717299578
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05620253164556962
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14177215189873418
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.28375527426160335
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3890295358649789
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5620253164556962
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.322564230377663
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.24968421405130298
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.26885741426647297
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.14345991561181434
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2831223628691983
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3850210970464135
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5550632911392405
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14345991561181434
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09437412095639944
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0770042194092827
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05550632911392406
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14345991561181434
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2831223628691983
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3850210970464135
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5550632911392405
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3205268083804564
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.24917821981113142
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2685327848764784
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.13924050632911392
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2795358649789029
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3837552742616034
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5533755274261604
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13924050632911392
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09317862165963431
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07675105485232067
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05533755274261602
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13924050632911392
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2795358649789029
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3837552742616034
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5533755274261604
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.31759054947613424
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2457681166700155
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2649300065982546
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.14029535864978904
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.27531645569620256
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.369831223628692
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5360759493670886
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.14029535864978904
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09177215189873417
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0739662447257384
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.053607594936708865
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.14029535864978904
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.27531645569620256
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.369831223628692
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5360759493670886
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3099216271465372
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.24117783470631593
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2601649646918979
      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-005-5ep")
# Run inference
sentences = [
    'Les queixes, observacions i suggeriments són una eina important per a millorar la qualitat dels serveis municipals.',
    'Què és el que es busca amb les queixes, observacions i suggeriments?',
    'Quin és el propòsit dels ajuts econòmics?',
]
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

*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   | 0.1437     |
| cosine_accuracy@3   | 0.2819     |
| cosine_accuracy@5   | 0.393      |
| cosine_accuracy@10  | 0.5665     |
| cosine_precision@1  | 0.1437     |
| cosine_precision@3  | 0.094      |
| cosine_precision@5  | 0.0786     |
| cosine_precision@10 | 0.0566     |
| cosine_recall@1     | 0.1437     |
| cosine_recall@3     | 0.2819     |
| cosine_recall@5     | 0.393      |
| cosine_recall@10    | 0.5665     |
| cosine_ndcg@10      | 0.3243     |
| cosine_mrr@10       | 0.2507     |
| **cosine_map@100**  | **0.2695** |

#### 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   | 0.147      |
| cosine_accuracy@3   | 0.2871     |
| cosine_accuracy@5   | 0.3901     |
| cosine_accuracy@10  | 0.5631     |
| cosine_precision@1  | 0.147      |
| cosine_precision@3  | 0.0957     |
| cosine_precision@5  | 0.078      |
| cosine_precision@10 | 0.0563     |
| cosine_recall@1     | 0.147      |
| cosine_recall@3     | 0.2871     |
| cosine_recall@5     | 0.3901     |
| cosine_recall@10    | 0.5631     |
| cosine_ndcg@10      | 0.3255     |
| cosine_mrr@10       | 0.2533     |
| **cosine_map@100**  | **0.2723** |

#### 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   | 0.1418     |
| cosine_accuracy@3   | 0.2838     |
| cosine_accuracy@5   | 0.389      |
| cosine_accuracy@10  | 0.562      |
| cosine_precision@1  | 0.1418     |
| cosine_precision@3  | 0.0946     |
| cosine_precision@5  | 0.0778     |
| cosine_precision@10 | 0.0562     |
| cosine_recall@1     | 0.1418     |
| cosine_recall@3     | 0.2838     |
| cosine_recall@5     | 0.389      |
| cosine_recall@10    | 0.562      |
| cosine_ndcg@10      | 0.3226     |
| cosine_mrr@10       | 0.2497     |
| **cosine_map@100**  | **0.2689** |

#### 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   | 0.1435     |
| cosine_accuracy@3   | 0.2831     |
| cosine_accuracy@5   | 0.385      |
| cosine_accuracy@10  | 0.5551     |
| cosine_precision@1  | 0.1435     |
| cosine_precision@3  | 0.0944     |
| cosine_precision@5  | 0.077      |
| cosine_precision@10 | 0.0555     |
| cosine_recall@1     | 0.1435     |
| cosine_recall@3     | 0.2831     |
| cosine_recall@5     | 0.385      |
| cosine_recall@10    | 0.5551     |
| cosine_ndcg@10      | 0.3205     |
| cosine_mrr@10       | 0.2492     |
| **cosine_map@100**  | **0.2685** |

#### 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   | 0.1392     |
| cosine_accuracy@3   | 0.2795     |
| cosine_accuracy@5   | 0.3838     |
| cosine_accuracy@10  | 0.5534     |
| cosine_precision@1  | 0.1392     |
| cosine_precision@3  | 0.0932     |
| cosine_precision@5  | 0.0768     |
| cosine_precision@10 | 0.0553     |
| cosine_recall@1     | 0.1392     |
| cosine_recall@3     | 0.2795     |
| cosine_recall@5     | 0.3838     |
| cosine_recall@10    | 0.5534     |
| cosine_ndcg@10      | 0.3176     |
| cosine_mrr@10       | 0.2458     |
| **cosine_map@100**  | **0.2649** |

#### 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   | 0.1403     |
| cosine_accuracy@3   | 0.2753     |
| cosine_accuracy@5   | 0.3698     |
| cosine_accuracy@10  | 0.5361     |
| cosine_precision@1  | 0.1403     |
| cosine_precision@3  | 0.0918     |
| cosine_precision@5  | 0.074      |
| cosine_precision@10 | 0.0536     |
| cosine_recall@1     | 0.1403     |
| cosine_recall@3     | 0.2753     |
| cosine_recall@5     | 0.3698     |
| cosine_recall@10    | 0.5361     |
| cosine_ndcg@10      | 0.3099     |
| cosine_mrr@10       | 0.2412     |
| **cosine_map@100**  | **0.2602** |

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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: 5,214 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: 49.66 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.85 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                            | anchor                                                                              |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | <code>Ajuts per la reactivació de petites empreses i persones autònomes donades d’alta al règim especial de treballadors autònoms (RETA) amb una antiguitat superior als cinc anys (COVID19)</code> | <code>Quin és el requisit per a les petites empreses per rebre ajuts?</code>        |
  | <code>En cas de no poder desenvolupar el projecte o activitat per la qual s'ha sol·licitat la subvenció, l'entitat beneficiària pot renunciar a la subvenció.</code>                                | <code>Puc renunciar a una subvenció si ja l'he rebut?</code>                        |
  | <code>L’Espai Jove de Sitges és l'equipament municipal on els joves poden dur a terme iniciatives pròpies i on també es desenvolupen d’altres impulsades per la regidoria de Joventut.</code>       | <code>Quin és el paper de la regidoria de Joventut a l'Espai Jove de Sitges?</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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4908     | 10     | 3.3699        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9816     | 20     | 1.8761        | 0.2565                  | 0.2430                 | 0.2509                 | 0.2499                 | 0.2301                | 0.2567                 |
| 1.4724     | 30     | 1.3111        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9632     | 40     | 0.8122        | 0.2636                  | 0.2578                 | 0.2629                 | 0.2639                 | 0.2486                | 0.2654                 |
| 2.4540     | 50     | 0.5903        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9448     | 60     | 0.4306        | -                       | -                      | -                      | -                      | -                     | -                      |
| **2.9939** | **61** | **-**         | **0.2661**              | **0.2636**             | **0.2648**             | **0.2659**             | **0.2544**            | **0.2694**             |
| 3.4356     | 70     | 0.3553        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9264     | 80     | 0.2925        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9755     | 81     | -             | 0.2701                  | 0.2621                 | 0.2663                 | 0.2706                 | 0.2602                | 0.2709                 |
| 4.4172     | 90     | 0.2797        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9080     | 100    | 0.267         | 0.2695                  | 0.2649                 | 0.2685                 | 0.2689                 | 0.2602                | 0.2723                 |

* 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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