---
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:5520
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Queda exclosa de la prohibició, dintre de les àrees recreatives
    i d'acampada i en parcel·les de les urbanitzacions, la utilització dels fogons
    de gas i de barbacoes d'obra amb mataguspires.
  sentences:
  - Què està prohibit fer en àrees d'acampada?
  - Quin és el benefici de la reserva d'un equipament municipal?
  - Quin és el benefici de la targeta d'aparcament individual per a l'autonomia personal?
- source_sentence: Aquest tràmit permet participar en processos oberts de selecció
    i provisió de personal de l'Ajuntament, i fer el pagament de la taxa per drets
    d'examen establerta en la convocatòria.
  sentences:
  - Quin és el requisit per participar en un procés de selecció de personal de l'Ajuntament?
  - On es pot trobar la relació de requeriments de documentació per a l'ajut de menjador
    escolar?
  - Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria?
- source_sentence: Sol·licitar la cessió temporal d’un compostador domèstic.
  sentences:
  - Quin és el requisit per a la tala d'arbres aïllats en sòl urbà?
  - Quin és el paper de la persona interessada en aquest tràmit?
  - Quin és el paper del compostador domèstic en la reducció de les emissions de gasos
    d'efecte hivernacle?
- source_sentence: Matriculació a l'Escola Bressol Municipal El Patufet.
  sentences:
  - Quin és el termini màxim per a deutes de 1.500,01 fins a 6.000,00 euros en el
    criteri excepcional?
  - Quin és el lloc on es realitza el tràmit de matrícula?
  - Quin és el lloc on es realitza el taller 'Informàtica nivell bàsic'?
- source_sentence: Aquest tipus de transmissió entre cedent i cessionari només podrà
    ser de caràcter gratuït i no condicionada.
  sentences:
  - Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?
  - Quin és el propòsit de la comunicació prèvia en relació amb la intervenció definitiva?
  - Quin és el propòsit de la Deixalleria municipal?
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: 0.04782608695652174
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20869565217391303
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30869565217391304
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5565217391304348
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04782608695652174
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06956521739130433
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.061739130434782616
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.055652173913043466
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04782608695652174
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20869565217391303
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30869565217391304
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5565217391304348
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25888429095047366
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16955314009661854
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.18763324173665294
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.06086956521739131
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21304347826086956
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30434782608695654
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5565217391304348
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06086956521739131
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07101449275362319
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06086956521739131
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.055652173913043466
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06086956521739131
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21304347826086956
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30434782608695654
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5565217391304348
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2637812435357463
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.17599723947550047
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.19341889075062485
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.0782608695652174
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21739130434782608
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.34347826086956523
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5695652173913044
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0782608695652174
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07246376811594202
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06869565217391305
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05695652173913043
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0782608695652174
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21739130434782608
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.34347826086956523
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5695652173913044
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.28117776588045035
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1947342995169084
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21224466664057137
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.05217391304347826
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20869565217391303
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3173913043478261
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5130434782608696
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05217391304347826
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06956521739130433
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06347826086956522
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05130434782608694
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05217391304347826
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20869565217391303
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3173913043478261
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5130434782608696
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.24833360148474737
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16793305728088342
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1892957688791951
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.05652173913043478
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22608695652173913
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32608695652173914
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5434782608695652
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05652173913043478
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0753623188405797
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06521739130434782
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05434782608695651
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05652173913043478
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22608695652173913
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32608695652173914
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5434782608695652
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2660596038952714
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.18197895100069028
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.20038255187663148
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.05652173913043478
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21739130434782608
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3173913043478261
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5434782608695652
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05652173913043478
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07246376811594202
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06347826086956522
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.054347826086956506
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05652173913043478
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21739130434782608
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3173913043478261
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5434782608695652
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2641081743881476
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.17965838509316792
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.19707496290303578
      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/sqv-v5-10ep")
# Run inference
sentences = [
    'Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.',
    'Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?',
    'Quin és el propòsit de la Deixalleria municipal?',
]
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   | 0.0478     |
| cosine_accuracy@3   | 0.2087     |
| cosine_accuracy@5   | 0.3087     |
| cosine_accuracy@10  | 0.5565     |
| cosine_precision@1  | 0.0478     |
| cosine_precision@3  | 0.0696     |
| cosine_precision@5  | 0.0617     |
| cosine_precision@10 | 0.0557     |
| cosine_recall@1     | 0.0478     |
| cosine_recall@3     | 0.2087     |
| cosine_recall@5     | 0.3087     |
| cosine_recall@10    | 0.5565     |
| cosine_ndcg@10      | 0.2589     |
| cosine_mrr@10       | 0.1696     |
| **cosine_map@100**  | **0.1876** |

#### 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   | 0.0609     |
| cosine_accuracy@3   | 0.213      |
| cosine_accuracy@5   | 0.3043     |
| cosine_accuracy@10  | 0.5565     |
| cosine_precision@1  | 0.0609     |
| cosine_precision@3  | 0.071      |
| cosine_precision@5  | 0.0609     |
| cosine_precision@10 | 0.0557     |
| cosine_recall@1     | 0.0609     |
| cosine_recall@3     | 0.213      |
| cosine_recall@5     | 0.3043     |
| cosine_recall@10    | 0.5565     |
| cosine_ndcg@10      | 0.2638     |
| cosine_mrr@10       | 0.176      |
| **cosine_map@100**  | **0.1934** |

#### 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   | 0.0783     |
| cosine_accuracy@3   | 0.2174     |
| cosine_accuracy@5   | 0.3435     |
| cosine_accuracy@10  | 0.5696     |
| cosine_precision@1  | 0.0783     |
| cosine_precision@3  | 0.0725     |
| cosine_precision@5  | 0.0687     |
| cosine_precision@10 | 0.057      |
| cosine_recall@1     | 0.0783     |
| cosine_recall@3     | 0.2174     |
| cosine_recall@5     | 0.3435     |
| cosine_recall@10    | 0.5696     |
| cosine_ndcg@10      | 0.2812     |
| cosine_mrr@10       | 0.1947     |
| **cosine_map@100**  | **0.2122** |

#### 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   | 0.0522     |
| cosine_accuracy@3   | 0.2087     |
| cosine_accuracy@5   | 0.3174     |
| cosine_accuracy@10  | 0.513      |
| cosine_precision@1  | 0.0522     |
| cosine_precision@3  | 0.0696     |
| cosine_precision@5  | 0.0635     |
| cosine_precision@10 | 0.0513     |
| cosine_recall@1     | 0.0522     |
| cosine_recall@3     | 0.2087     |
| cosine_recall@5     | 0.3174     |
| cosine_recall@10    | 0.513      |
| cosine_ndcg@10      | 0.2483     |
| cosine_mrr@10       | 0.1679     |
| **cosine_map@100**  | **0.1893** |

#### 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   | 0.0565     |
| cosine_accuracy@3   | 0.2261     |
| cosine_accuracy@5   | 0.3261     |
| cosine_accuracy@10  | 0.5435     |
| cosine_precision@1  | 0.0565     |
| cosine_precision@3  | 0.0754     |
| cosine_precision@5  | 0.0652     |
| cosine_precision@10 | 0.0543     |
| cosine_recall@1     | 0.0565     |
| cosine_recall@3     | 0.2261     |
| cosine_recall@5     | 0.3261     |
| cosine_recall@10    | 0.5435     |
| cosine_ndcg@10      | 0.2661     |
| cosine_mrr@10       | 0.182      |
| **cosine_map@100**  | **0.2004** |

#### 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   | 0.0565     |
| cosine_accuracy@3   | 0.2174     |
| cosine_accuracy@5   | 0.3174     |
| cosine_accuracy@10  | 0.5435     |
| cosine_precision@1  | 0.0565     |
| cosine_precision@3  | 0.0725     |
| cosine_precision@5  | 0.0635     |
| cosine_precision@10 | 0.0543     |
| cosine_recall@1     | 0.0565     |
| cosine_recall@3     | 0.2174     |
| cosine_recall@5     | 0.3174     |
| cosine_recall@10    | 0.5435     |
| cosine_ndcg@10      | 0.2641     |
| cosine_mrr@10       | 0.1797     |
| **cosine_map@100**  | **0.1971** |

<!--
## 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.*
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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: 5,520 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: 5 tokens</li><li>mean: 43.78 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.5 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                    | anchor                                                                                                                                                                 |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>L’Ajuntament vol crear un banc de recursos on recollir tots els oferiments de la població i que servirà per atendre les necessitats de les famílies refugiades acollides al poble.</code>                                                                                                                                             | <code>Quin és el paper de l’Ajuntament en la integració de les persones refugiades acollides?</code>                                                                   |
  | <code>Aquest tipus d'actuació requereix la intervenció d'una persona tècnica competent que subscrigui el projecte o la documentació tècnica corresponent i que assumeixi la direcció facultativa de l'execució de les obres.</code>                                                                                                         | <code>Quin és el requisit per a la intervenció d'una persona tècnica competent en les obres d'intervenció parcial interior en edificis amb elements catalogats?</code> |
  | <code>Aquest títol, adreçat a persones empadronades a Sant Quirze del Vallès, es concedirà segons el nivell d’ingressos, la condició d’edat o de discapacitat, en base als criteris específics que recull l’ordenança reguladora del sistema de tarifació social del transport públic municipal en autobús a Sant Quirze del Vallès.</code> | <code>Quin és el benefici de la TBUS GRATUÏTA per a les persones majors?</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`: 10
- `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`: 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`: 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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4638     | 10      | 4.0375        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9275     | 20      | 3.2095        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9739     | 21      | -             | 0.1772                  | 0.1818                 | 0.1967                 | 0.1911                 | 0.1417                | 0.1750                 |
| 1.3913     | 30      | 2.1843        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8551     | 40      | 1.6095        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9942     | 43      | -             | 0.1889                  | 0.1676                 | 0.1961                 | 0.1969                 | 0.1834                | 0.1899                 |
| 2.3188     | 50      | 1.2099        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.7826     | 60      | 0.909         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9681     | 64      | -             | 0.1998                  | 0.1977                 | 0.2164                 | 0.2030                 | 0.1972                | 0.2156                 |
| 3.2464     | 70      | 0.7534        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7101     | 80      | 0.6339        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9884     | 86      | -             | 0.2049                  | 0.2024                 | 0.1989                 | 0.1935                 | 0.2046                | 0.1949                 |
| 4.1739     | 90      | 0.5423        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.6377     | 100     | 0.5135        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9623     | 107     | -             | 0.1967                  | 0.2199                 | 0.1892                 | 0.2113                 | 0.1957                | 0.2037                 |
| 5.1014     | 110     | 0.4563        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.5652     | 120     | 0.3837        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.9826     | 129     | -             | 0.2026                  | 0.1898                 | 0.1903                 | 0.2035                 | 0.2034                | 0.2187                 |
| 6.0290     | 130     | 0.3991        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.4928     | 140     | 0.3996        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.9565     | 150     | 0.3225        | 0.2053                  | 0.1866                 | 0.2046                 | 0.2083                 | 0.1822                | 0.2086                 |
| 7.4203     | 160     | 0.3407        | -                       | -                      | -                      | -                      | -                     | -                      |
| 7.8841     | 170     | 0.2982        | -                       | -                      | -                      | -                      | -                     | -                      |
| **7.9768** | **172** | **-**         | **0.2092**              | **0.2197**             | **0.2005**             | **0.2178**             | **0.2063**            | **0.2042**             |
| 8.3478     | 180     | 0.3169        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.8116     | 190     | 0.2799        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.9971     | 194     | -             | 0.2053                  | 0.2215                 | 0.1929                 | 0.2191                 | 0.2106                | 0.2170                 |
| 9.2754     | 200     | 0.312         | -                       | -                      | -                      | -                      | -                     | -                      |
| 9.7391     | 210     | 0.2684        | 0.1876                  | 0.2004                 | 0.1893                 | 0.2122                 | 0.1971                | 0.1934                 |

* 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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