---
base_model: BAAI/bge-m3
datasets: []
language: []
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:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: L'Espai d'escalada és una instal·lació municipal en forma de túnel
    a una sala interior, amb una llargada de 10m, una amplada de 4,6m i una alçada
    de 4m.
  sentences:
  - Quin és el registre on es comprova la inscripció dels estrangers amb ciutadania
    de l'Espai Econòmic Europeu?
  - On es pot trobar les bases generals de les convocatòries de selecció de personal
    de l'Ajuntament?
  - Quina és la llargada de l'Espai d'Escalada?
- source_sentence: Les activitats s’organitzen per setmanes.
  sentences:
  - Quin és el format en què es desenvolupen les activitats de l'Estiu Jove?
  - Quin és el paper del subjecte passiu en la gestió de pagaments?
  - Quin és el benefici de les subvencions?
- source_sentence: Les Estades Esportives cerquen que els infants aprenguin a relacionar-se
    i a compartir mitjançant l'esport, experiències i vivències amb d'altres infants
    amb qui no estan en contacte durant la resta de l'any.
  sentences:
  - Quin és el propòsit de l'ajut per a la creació de noves empreses?
  - Quin és el propòsit de la llicència de parcel·lació?
  - Quin és el benefici principal de les Estades Esportives?
- source_sentence: Declaració tributària mitjançant la qual es sol·licita la baixa
    d'una activitat de la Taxa pel servei municipal complementari de recollida, tractament
    i eliminació de residus comercials.
  sentences:
  - Quin és el format de la Declaració de baixa?
  - Quin és el resultat de justificar una sol·licitud de canvi a les estades esportives?
  - Quin és el període de celebració de la Fira d'Art de Sitges?
- source_sentence: Les entitats inscrites en el Registre resten obligades a comunicar
    a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar
    la seva cancel·lació o comunicant la seva dissolució.
  sentences:
  - Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre
    municipal d'entitats?
  - Quin és el propòsit de la quota del servei de les Llars d'Infants Municipals?
  - Quin és el paper de les entitats de protecció dels animals en la gestió de les
    colònies urbanes felines?
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: 0.08620689655172414
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21551724137931033
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3275862068965517
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5107758620689655
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08620689655172414
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07183908045977011
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06551724137931034
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05107758620689654
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08620689655172414
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21551724137931033
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3275862068965517
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5107758620689655
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.26401643418499254
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1896731321839082
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2150866107809785
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.08405172413793104
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20905172413793102
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.31896551724137934
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08405172413793104
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.069683908045977
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06379310344827585
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08405172413793104
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20905172413793102
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.31896551724137934
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2594763687925116
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.18673713738368922
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21319033477988852
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.08620689655172414
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21120689655172414
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32112068965517243
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5129310344827587
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08620689655172414
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07040229885057471
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06422413793103447
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.051293103448275854
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08620689655172414
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21120689655172414
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32112068965517243
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5129310344827587
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2646539120704089
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1899279898741108
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21554766038692458
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.08189655172413793
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20474137931034483
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30603448275862066
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5043103448275862
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08189655172413793
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0682471264367816
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.061206896551724135
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05043103448275862
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08189655172413793
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20474137931034483
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30603448275862066
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5043103448275862
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25554093803691474
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1807856116584566
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.20657861277416045
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.08405172413793104
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20043103448275862
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3146551724137931
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49137931034482757
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08405172413793104
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0668103448275862
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06293103448275862
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04913793103448275
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08405172413793104
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20043103448275862
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3146551724137931
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49137931034482757
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2516576518560222
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1794651409414343
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.20584710715396837
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.07974137931034483
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2025862068965517
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3017241379310345
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4956896551724138
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.07974137931034483
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06752873563218391
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.0603448275862069
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04956896551724138
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.07974137931034483
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2025862068965517
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3017241379310345
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4956896551724138
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2527082338557514
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.17959085933223878
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2058214047481906
      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). 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:** Unknown -->
<!-- - **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/sitgrsBAAIbge-m3-290824")
# Run inference
sentences = [
    'Les entitats inscrites en el Registre resten obligades a comunicar a l’Ajuntament qualsevol modificació en les seves dades registrals, podent sol·licitar la seva cancel·lació o comunicant la seva dissolució.',
    "Quin és el procediment per cancel·lar la inscripció d'una entitat al Registre municipal d'entitats?",
    'Quin és el paper de les entitats de protecció dels animals en la gestió de les colònies urbanes felines?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.0862     |
| cosine_accuracy@3   | 0.2155     |
| cosine_accuracy@5   | 0.3276     |
| cosine_accuracy@10  | 0.5108     |
| cosine_precision@1  | 0.0862     |
| cosine_precision@3  | 0.0718     |
| cosine_precision@5  | 0.0655     |
| cosine_precision@10 | 0.0511     |
| cosine_recall@1     | 0.0862     |
| cosine_recall@3     | 0.2155     |
| cosine_recall@5     | 0.3276     |
| cosine_recall@10    | 0.5108     |
| cosine_ndcg@10      | 0.264      |
| cosine_mrr@10       | 0.1897     |
| **cosine_map@100**  | **0.2151** |

#### 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   | 0.0841     |
| cosine_accuracy@3   | 0.2091     |
| cosine_accuracy@5   | 0.319      |
| cosine_accuracy@10  | 0.5        |
| cosine_precision@1  | 0.0841     |
| cosine_precision@3  | 0.0697     |
| cosine_precision@5  | 0.0638     |
| cosine_precision@10 | 0.05       |
| cosine_recall@1     | 0.0841     |
| cosine_recall@3     | 0.2091     |
| cosine_recall@5     | 0.319      |
| cosine_recall@10    | 0.5        |
| cosine_ndcg@10      | 0.2595     |
| cosine_mrr@10       | 0.1867     |
| **cosine_map@100**  | **0.2132** |

#### 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   | 0.0862     |
| cosine_accuracy@3   | 0.2112     |
| cosine_accuracy@5   | 0.3211     |
| cosine_accuracy@10  | 0.5129     |
| cosine_precision@1  | 0.0862     |
| cosine_precision@3  | 0.0704     |
| cosine_precision@5  | 0.0642     |
| cosine_precision@10 | 0.0513     |
| cosine_recall@1     | 0.0862     |
| cosine_recall@3     | 0.2112     |
| cosine_recall@5     | 0.3211     |
| cosine_recall@10    | 0.5129     |
| cosine_ndcg@10      | 0.2647     |
| cosine_mrr@10       | 0.1899     |
| **cosine_map@100**  | **0.2155** |

#### 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   | 0.0819     |
| cosine_accuracy@3   | 0.2047     |
| cosine_accuracy@5   | 0.306      |
| cosine_accuracy@10  | 0.5043     |
| cosine_precision@1  | 0.0819     |
| cosine_precision@3  | 0.0682     |
| cosine_precision@5  | 0.0612     |
| cosine_precision@10 | 0.0504     |
| cosine_recall@1     | 0.0819     |
| cosine_recall@3     | 0.2047     |
| cosine_recall@5     | 0.306      |
| cosine_recall@10    | 0.5043     |
| cosine_ndcg@10      | 0.2555     |
| cosine_mrr@10       | 0.1808     |
| **cosine_map@100**  | **0.2066** |

#### 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   | 0.0841     |
| cosine_accuracy@3   | 0.2004     |
| cosine_accuracy@5   | 0.3147     |
| cosine_accuracy@10  | 0.4914     |
| cosine_precision@1  | 0.0841     |
| cosine_precision@3  | 0.0668     |
| cosine_precision@5  | 0.0629     |
| cosine_precision@10 | 0.0491     |
| cosine_recall@1     | 0.0841     |
| cosine_recall@3     | 0.2004     |
| cosine_recall@5     | 0.3147     |
| cosine_recall@10    | 0.4914     |
| cosine_ndcg@10      | 0.2517     |
| cosine_mrr@10       | 0.1795     |
| **cosine_map@100**  | **0.2058** |

#### 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   | 0.0797     |
| cosine_accuracy@3   | 0.2026     |
| cosine_accuracy@5   | 0.3017     |
| cosine_accuracy@10  | 0.4957     |
| cosine_precision@1  | 0.0797     |
| cosine_precision@3  | 0.0675     |
| cosine_precision@5  | 0.0603     |
| cosine_precision@10 | 0.0496     |
| cosine_recall@1     | 0.0797     |
| cosine_recall@3     | 0.2026     |
| cosine_recall@5     | 0.3017     |
| cosine_recall@10    | 0.4957     |
| cosine_ndcg@10      | 0.2527     |
| cosine_mrr@10       | 0.1796     |
| **cosine_map@100**  | **0.2058** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

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

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 4,173 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: 48.75 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.07 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                           | anchor                                                                                                          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------|
  | <code>Els ajuts per a la realització d'activitats en el lleure esportiu estan destinats a les entitats sense ànim de lucre que desenvolupen activitats esportives i de lleure.</code>                                                              | <code>Quins són els sectors que es beneficien dels ajuts?</code>                                                |
  | <code>En el certificat s'indiquen les dades de planejament vigent, classificació del sòl, qualificació urbanística, condicions de l’edificació i usos admesos referides a una finca o solar concreta.</code>                                       | <code>Quin és el contingut de les condicions de l'edificació en el certificat d'aprofitament urbanístic?</code> |
  | <code>Aportació de documentació. Ajuts per compensar la disminució d'ingressos de les empreses o establiments del sector de l'hosteleria i restauració afectats per les mesures adoptades per la situació de crisis provocada pel SARS-CoV2</code> | <code>Quin és el paper dels ajuts en la situació de crisis?</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
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: False
- `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
- `learning_rate`: 5e-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`: 10
- `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`: False
- `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
- `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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6130     | 10      | 3.0594        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9808     | 16      | -             | 0.2047                  | 0.1922                 | 0.2020                 | 0.2016                 | 0.1774                | 0.2115                 |
| 1.2261     | 20      | 1.525         | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8391     | 30      | 0.7434        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9617     | 32      | -             | 0.2186                  | 0.2003                 | 0.2102                 | 0.2092                 | 0.1870                | 0.2101                 |
| 2.4521     | 40      | 0.4451        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9425     | 48      | -             | 0.2083                  | 0.2054                 | 0.2091                 | 0.2118                 | 0.2009                | 0.2140                 |
| 3.0651     | 50      | 0.2518        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.6782     | 60      | 0.1801        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9847     | 65      | -             | 0.2135                  | 0.2071                 | 0.2037                 | 0.2115                 | 0.2030                | 0.2191                 |
| 4.2912     | 70      | 0.1483        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9042     | 80      | 0.0893        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9655     | 81      | -             | 0.2066                  | 0.2053                 | 0.2057                 | 0.2137                 | 0.1982                | 0.2176                 |
| 5.5172     | 90      | 0.0748        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.9464     | 97      | -             | 0.2171                  | 0.2113                 | 0.2086                 | 0.2178                 | 0.2120                | 0.2193                 |
| 6.1303     | 100     | 0.064         | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.7433     | 110     | 0.0458        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.9885     | 114     | -             | 0.2294                  | 0.2132                 | 0.2151                 | 0.2227                 | 0.2054                | 0.2138                 |
| 7.3563     | 120     | 0.0436        | -                       | -                      | -                      | -                      | -                     | -                      |
| 7.9693     | 130     | 0.0241        | 0.2133                  | 0.2083                 | 0.2096                 | 0.2138                 | 0.2080                | 0.2124                 |
| 8.5824     | 140     | 0.021         | -                       | -                      | -                      | -                      | -                     | -                      |
| **8.9502** | **146** | **-**         | **0.216**               | **0.2074**             | **0.2081**             | **0.2162**             | **0.2094**            | **0.2177**             |
| 9.1954     | 150     | 0.0237        | -                       | -                      | -                      | -                      | -                     | -                      |
| 9.8084     | 160     | 0.0145        | 0.2151                  | 0.2058                 | 0.2066                 | 0.2155                 | 0.2058                | 0.2132                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.0.dev0
- Datasets: 2.21.0
- 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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