sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:2884 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:2884
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'P.2 El contingut mínim del projecte és: a) Memòria justificativa,
    amb: - La descripció de la finca o finques d''origen amb indicació de les seves
    superfícies i llindars. - La descripció de les finques resultants, la seva superfície
    i els seus llindars...'
  sentences:
  - Quin és el format de sortida de la informació sobre aquesta ciutat?
  - Quins són els requisits bàsics per sol·licitar la subvenció?
  - Quin és el contingut mínim del projecte de parcel·lació?
- source_sentence: 'La Comissió de Garanties té dues funcions: aclarir els dubtes
    interpretatius que es plantegin en l''aplicació del mateix.'
  sentences:
  - Quines són les dues funcions de la Comissió de Garanties?
  - Quin és el propòsit d'una llicència d'obres mitjanes en relació amb els moviments
    de terres?
  - Quin és el nom del conjunt d'habitatges que es troba al terme municipal de Viladecans?
- source_sentence: 'No cal presentar al·legacions en els següents casos: En el cas
    que la baixa s’hagués iniciat per manca de confirmació bastarà amb realitzar el
    tràmit de confirmació per que l’expedient de baixa s’arxivi, sempre i quan continuï
    residint al mateix domicili.'
  sentences:
  - És necessari que una persona tècnica professional empleni els documents d'autocontrol?
  - Quin és el tema principal de la secció d'horari d'obertura i tancament?
  - Quan no cal presentar al·legacions en un expedient de baixa d'ofici?
- source_sentence: L'Ajuntament de Sant Boi obre convocatòria de concessió de beques
    per col·laborar en el finançament de projectes i activitats dels i de les joves
    del municipi en diferents àmbits i promoure i facilitar els processos d'emancipació
    juvenils i garantir la igualtat d'oportunitats i la cohesió social entre la població
    jove.
  sentences:
  - Quin és el propòsit del servei de llista d'espera?
  - Quin és el problema que es tracta en aquest apartat?
  - Quin és l'objectiu de les beques per a joves 2024 de l'Ajuntament de Sant Boi?
- source_sentence: Empadronament d'un/a menor en un domicili diferent al domicili
    dels progenitors - Amb autorització de les persones progenitores
  sentences:
  - Quin és el límit de temps màxim per al període de funcionament en proves?
  - Què es necessita per participar en aquest procediment de selecció?
  - Quin és el resultat de l'empadronament d'un/a menor en un domicili diferent al
    dels progenitors amb autorització?
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: 0.3883495145631068
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6310679611650486
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7198335644937587
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8183079056865464
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3883495145631068
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.21035598705501618
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1439667128987517
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08183079056865464
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3883495145631068
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6310679611650486
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7198335644937587
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8183079056865464
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.596832375022475
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5265262091891769
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5337741877067146
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.37447988904299584
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6227461858529819
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.723994452149792
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8210818307905686
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.37447988904299584
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.207582061950994
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1447988904299584
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08210818307905685
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.37447988904299584
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6227461858529819
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.723994452149792
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8210818307905686
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5927947036265483
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5201010501287889
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5274048711370899
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.37309292649098474
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6213592233009708
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7184466019417476
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.826629680998613
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.37309292649098474
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2071197411003236
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1436893203883495
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08266296809986129
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.37309292649098474
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6213592233009708
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7184466019417476
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.826629680998613
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5933965794382484
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5193294146137418
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5262147141098168
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.39528432732316227
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6185852981969486
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6962552011095701
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8252427184466019
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.39528432732316227
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20619509939898292
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.139251040221914
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0825242718446602
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.39528432732316227
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6185852981969486
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6962552011095701
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8252427184466019
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5982896106972676
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5270165995200669
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.533875073833905
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.3828016643550624
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6033287101248266
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7059639389736477
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8155339805825242
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3828016643550624
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20110957004160887
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14119278779472955
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08155339805825243
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3828016643550624
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6033287101248266
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7059639389736477
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8155339805825242
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.589596475804869
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5181840697444022
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5258716600846131
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.37031900138696255
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5686546463245492
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6851595006934813
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7891816920943134
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.37031900138696255
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18955154877484973
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13703190013869623
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07891816920943133
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.37031900138696255
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5686546463245492
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6851595006934813
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7891816920943134
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5679462834016797
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.49845397706007927
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5067836651151116
      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-SB-003-5ep")
# Run inference
sentences = [
    "Empadronament d'un/a menor en un domicili diferent al domicili dels progenitors - Amb autorització de les persones progenitores",
    "Quin és el resultat de l'empadronament d'un/a menor en un domicili diferent al dels progenitors amb autorització?",
    'Quin és el límit de temps màxim per al període de funcionament en proves?',
]
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   | 0.3883     |
| cosine_accuracy@3   | 0.6311     |
| cosine_accuracy@5   | 0.7198     |
| cosine_accuracy@10  | 0.8183     |
| cosine_precision@1  | 0.3883     |
| cosine_precision@3  | 0.2104     |
| cosine_precision@5  | 0.144      |
| cosine_precision@10 | 0.0818     |
| cosine_recall@1     | 0.3883     |
| cosine_recall@3     | 0.6311     |
| cosine_recall@5     | 0.7198     |
| cosine_recall@10    | 0.8183     |
| cosine_ndcg@10      | 0.5968     |
| cosine_mrr@10       | 0.5265     |
| **cosine_map@100**  | **0.5338** |

#### 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   | 0.3745     |
| cosine_accuracy@3   | 0.6227     |
| cosine_accuracy@5   | 0.724      |
| cosine_accuracy@10  | 0.8211     |
| cosine_precision@1  | 0.3745     |
| cosine_precision@3  | 0.2076     |
| cosine_precision@5  | 0.1448     |
| cosine_precision@10 | 0.0821     |
| cosine_recall@1     | 0.3745     |
| cosine_recall@3     | 0.6227     |
| cosine_recall@5     | 0.724      |
| cosine_recall@10    | 0.8211     |
| cosine_ndcg@10      | 0.5928     |
| cosine_mrr@10       | 0.5201     |
| **cosine_map@100**  | **0.5274** |

#### 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   | 0.3731     |
| cosine_accuracy@3   | 0.6214     |
| cosine_accuracy@5   | 0.7184     |
| cosine_accuracy@10  | 0.8266     |
| cosine_precision@1  | 0.3731     |
| cosine_precision@3  | 0.2071     |
| cosine_precision@5  | 0.1437     |
| cosine_precision@10 | 0.0827     |
| cosine_recall@1     | 0.3731     |
| cosine_recall@3     | 0.6214     |
| cosine_recall@5     | 0.7184     |
| cosine_recall@10    | 0.8266     |
| cosine_ndcg@10      | 0.5934     |
| cosine_mrr@10       | 0.5193     |
| **cosine_map@100**  | **0.5262** |

#### 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   | 0.3953     |
| cosine_accuracy@3   | 0.6186     |
| cosine_accuracy@5   | 0.6963     |
| cosine_accuracy@10  | 0.8252     |
| cosine_precision@1  | 0.3953     |
| cosine_precision@3  | 0.2062     |
| cosine_precision@5  | 0.1393     |
| cosine_precision@10 | 0.0825     |
| cosine_recall@1     | 0.3953     |
| cosine_recall@3     | 0.6186     |
| cosine_recall@5     | 0.6963     |
| cosine_recall@10    | 0.8252     |
| cosine_ndcg@10      | 0.5983     |
| cosine_mrr@10       | 0.527      |
| **cosine_map@100**  | **0.5339** |

#### 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   | 0.3828     |
| cosine_accuracy@3   | 0.6033     |
| cosine_accuracy@5   | 0.706      |
| cosine_accuracy@10  | 0.8155     |
| cosine_precision@1  | 0.3828     |
| cosine_precision@3  | 0.2011     |
| cosine_precision@5  | 0.1412     |
| cosine_precision@10 | 0.0816     |
| cosine_recall@1     | 0.3828     |
| cosine_recall@3     | 0.6033     |
| cosine_recall@5     | 0.706      |
| cosine_recall@10    | 0.8155     |
| cosine_ndcg@10      | 0.5896     |
| cosine_mrr@10       | 0.5182     |
| **cosine_map@100**  | **0.5259** |

#### 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   | 0.3703     |
| cosine_accuracy@3   | 0.5687     |
| cosine_accuracy@5   | 0.6852     |
| cosine_accuracy@10  | 0.7892     |
| cosine_precision@1  | 0.3703     |
| cosine_precision@3  | 0.1896     |
| cosine_precision@5  | 0.137      |
| cosine_precision@10 | 0.0789     |
| cosine_recall@1     | 0.3703     |
| cosine_recall@3     | 0.5687     |
| cosine_recall@5     | 0.6852     |
| cosine_recall@10    | 0.7892     |
| cosine_ndcg@10      | 0.5679     |
| cosine_mrr@10       | 0.4985     |
| **cosine_map@100**  | **0.5068** |

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 2,884 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: 36.18 tokens</li><li>max: 194 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 19.77 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                              | anchor                                                                                                   |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|
  | <code>I assessorem per l'optimització dels contractes de subministraments energètics.</code>                                                                                          | <code>Quin és el resultat esperat del servei de millora dels contractes de serveis de llum i gas?</code> |
  | <code>Retorna en format JSON adequat</code>                                                                                                                                           | <code>Quin és el format de sortida del qüestionari de projectes específics?</code>                       |
  | <code>Aula Mentor és un programa d'ajuda a l'alumne que té com a objectiu principal donar suport als estudiants en la seva formació i desenvolupament personal i professional.</code> | <code>Quin és el format del programa Aula Mentor?</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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8840     | 10     | 2.6418        | -                       | -                      | -                      | -                      | -                      | -                     |
| 0.9724     | 11     | -             | 0.4986                  | 0.5108                 | 0.5014                 | 0.4934                 | 0.4779                 | 0.4351                |
| 1.7680     | 20     | 1.1708        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.9448     | 22     | -             | 0.5197                  | 0.5248                 | 0.5195                 | 0.5290                 | 0.5052                 | 0.4904                |
| 2.6519     | 30     | 0.5531        | -                       | -                      | -                      | -                      | -                      | -                     |
| 2.9171     | 33     | -             | 0.5304                  | 0.5274                 | 0.5196                 | 0.5279                 | 0.5234                 | 0.4947                |
| 3.5359     | 40     | 0.2859        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.9779     | 45     | -             | 0.5256                  | 0.5292                 | 0.5206                 | 0.5313                 | 0.5174                 | 0.5046                |
| 4.4199     | 50     | 0.2144        | -                       | -                      | -                      | -                      | -                      | -                     |
| **4.8619** | **55** | **-**         | **0.5338**              | **0.5274**             | **0.5262**             | **0.5339**             | **0.5259**             | **0.5068**            |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 1.1.0.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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