---
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:5175
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Caldrà executar l'obra comunicada prèviament d'acord amb les condicions
    específiques que es contenen en el model normalitzat CT02.
  sentences:
  - Quin és el propòsit de la instal·lació d'un circ sense animals a la via pública?
  - Quin és el destinatari de les dades bloquejades?
  - Quin és el format de presentació de la comunicació prèvia?
- source_sentence: Armes utilitzables en activitats lúdico-esportives d’airsoft i
    paintball...
  sentences:
  - Quin és el paper de l'AFA en la venda de llibres?
  - Quin és el benefici de tenir dades personals correctes?
  - Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria?
- source_sentence: En les activitats sotmeses al règim d’autorització ambiental o
    llicència municipal d’activitat (Annex I o Annex II de la Llei 20/2009) cal demanar
    aquest certificat previ a la presentació de la sol·licitud d’autorització ambiental
    o llicència municipal.
  sentences:
  - Quin és el benefici de tenir el certificat de compatibilitat urbanística en les
    activitats sotmeses a llicència municipal d’activitat?
  - Com puc controlar la recepció de propaganda electoral per correu?
  - Quin és el benefici de la cessió d'un compostador domèstic per a l'entorn?
- source_sentence: La persona interessada posa en coneixement de l’Administració,
    les actuacions urbanístiques que pretén dur a terme consistents en l'apuntalament
    o reforç provisional d'estructures existents fins a la intervenció definitiva.
  sentences:
  - Qui pot participar en el Consell d'Adolescents?
  - Quin és el resultat de la presentació de la comunicació prèvia?
  - Quin és el paper de la persona interessada en relació amb la presentació de la
    comunicació prèvia?
- source_sentence: La persona consumidora presenti la reclamació davant de l'entitat
    acreditada en un termini superior a un any des de la data en què va presentar
    la reclamació a l'empresa.
  sentences:
  - Quin és el tràmit per inscriure'm al Padró d'Habitants sense tenir constància
    de la meva anterior residència?
  - Quin és el resultat de la modificació substancial de la llicència d'obres en relació
    a les autoritzacions administratives?
  - Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?
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: 0.057391304347826085
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.15304347826086956
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.23478260869565218
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.41739130434782606
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.057391304347826085
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.051014492753623186
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04695652173913043
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04173913043478261
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.057391304347826085
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15304347826086956
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.23478260869565218
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.41739130434782606
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20551130934080394
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14188060731539
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16516795239083046
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.05565217391304348
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.16
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.24
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.40695652173913044
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05565217391304348
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.048
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04069565217391305
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05565217391304348
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.16
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.24
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.40695652173913044
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20158774447839253
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.13959282263630102
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16377775492511307
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.06956521739130435
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.16695652173913045
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.24869565217391304
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4260869565217391
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06956521739130435
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05565217391304348
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04973913043478261
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.042608695652173914
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06956521739130435
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.16695652173913045
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.24869565217391304
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4260869565217391
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.21580306349457917
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1526128364389235
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1754746652296583
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.05565217391304348
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.16695652173913045
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.25217391304347825
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.42434782608695654
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05565217391304348
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05565217391304348
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05043478260869566
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.042434782608695654
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05565217391304348
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.16695652173913045
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.25217391304347825
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.42434782608695654
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2100045076980214
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14526432022084196
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1684764968624273
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.06086956521739131
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.1617391304347826
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2608695652173913
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4434782608695652
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06086956521739131
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05391304347826087
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.05217391304347826
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04434782608695652
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06086956521739131
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1617391304347826
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2608695652173913
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4434782608695652
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.21805066438366894
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.15018150448585244
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.17220421856187046
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.06086956521739131
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.15478260869565216
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.24521739130434783
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.42782608695652175
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06086956521739131
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.05159420289855072
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.04904347826086957
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.042782608695652175
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06086956521739131
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15478260869565216
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.24521739130434783
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.42782608695652175
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.21079002748958972
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.14568875086266406
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.16756200348857653
      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-v4-10ep")
# Run inference
sentences = [
    "La persona consumidora presenti la reclamació davant de l'entitat acreditada en un termini superior a un any des de la data en què va presentar la reclamació a l'empresa.",
    "Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?",
    "Quin és el resultat de la modificació substancial de la llicència d'obres en relació a les autoritzacions administratives?",
]
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.*
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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   | 0.0574     |
| cosine_accuracy@3   | 0.153      |
| cosine_accuracy@5   | 0.2348     |
| cosine_accuracy@10  | 0.4174     |
| cosine_precision@1  | 0.0574     |
| cosine_precision@3  | 0.051      |
| cosine_precision@5  | 0.047      |
| cosine_precision@10 | 0.0417     |
| cosine_recall@1     | 0.0574     |
| cosine_recall@3     | 0.153      |
| cosine_recall@5     | 0.2348     |
| cosine_recall@10    | 0.4174     |
| cosine_ndcg@10      | 0.2055     |
| cosine_mrr@10       | 0.1419     |
| **cosine_map@100**  | **0.1652** |

#### 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   | 0.0557     |
| cosine_accuracy@3   | 0.16       |
| cosine_accuracy@5   | 0.24       |
| cosine_accuracy@10  | 0.407      |
| cosine_precision@1  | 0.0557     |
| cosine_precision@3  | 0.0533     |
| cosine_precision@5  | 0.048      |
| cosine_precision@10 | 0.0407     |
| cosine_recall@1     | 0.0557     |
| cosine_recall@3     | 0.16       |
| cosine_recall@5     | 0.24       |
| cosine_recall@10    | 0.407      |
| cosine_ndcg@10      | 0.2016     |
| cosine_mrr@10       | 0.1396     |
| **cosine_map@100**  | **0.1638** |

#### 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   | 0.0696     |
| cosine_accuracy@3   | 0.167      |
| cosine_accuracy@5   | 0.2487     |
| cosine_accuracy@10  | 0.4261     |
| cosine_precision@1  | 0.0696     |
| cosine_precision@3  | 0.0557     |
| cosine_precision@5  | 0.0497     |
| cosine_precision@10 | 0.0426     |
| cosine_recall@1     | 0.0696     |
| cosine_recall@3     | 0.167      |
| cosine_recall@5     | 0.2487     |
| cosine_recall@10    | 0.4261     |
| cosine_ndcg@10      | 0.2158     |
| cosine_mrr@10       | 0.1526     |
| **cosine_map@100**  | **0.1755** |

#### 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   | 0.0557     |
| cosine_accuracy@3   | 0.167      |
| cosine_accuracy@5   | 0.2522     |
| cosine_accuracy@10  | 0.4243     |
| cosine_precision@1  | 0.0557     |
| cosine_precision@3  | 0.0557     |
| cosine_precision@5  | 0.0504     |
| cosine_precision@10 | 0.0424     |
| cosine_recall@1     | 0.0557     |
| cosine_recall@3     | 0.167      |
| cosine_recall@5     | 0.2522     |
| cosine_recall@10    | 0.4243     |
| cosine_ndcg@10      | 0.21       |
| cosine_mrr@10       | 0.1453     |
| **cosine_map@100**  | **0.1685** |

#### 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   | 0.0609     |
| cosine_accuracy@3   | 0.1617     |
| cosine_accuracy@5   | 0.2609     |
| cosine_accuracy@10  | 0.4435     |
| cosine_precision@1  | 0.0609     |
| cosine_precision@3  | 0.0539     |
| cosine_precision@5  | 0.0522     |
| cosine_precision@10 | 0.0443     |
| cosine_recall@1     | 0.0609     |
| cosine_recall@3     | 0.1617     |
| cosine_recall@5     | 0.2609     |
| cosine_recall@10    | 0.4435     |
| cosine_ndcg@10      | 0.2181     |
| cosine_mrr@10       | 0.1502     |
| **cosine_map@100**  | **0.1722** |

#### 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   | 0.0609     |
| cosine_accuracy@3   | 0.1548     |
| cosine_accuracy@5   | 0.2452     |
| cosine_accuracy@10  | 0.4278     |
| cosine_precision@1  | 0.0609     |
| cosine_precision@3  | 0.0516     |
| cosine_precision@5  | 0.049      |
| cosine_precision@10 | 0.0428     |
| cosine_recall@1     | 0.0609     |
| cosine_recall@3     | 0.1548     |
| cosine_recall@5     | 0.2452     |
| cosine_recall@10    | 0.4278     |
| cosine_ndcg@10      | 0.2108     |
| cosine_mrr@10       | 0.1457     |
| **cosine_map@100**  | **0.1676** |

<!--
## 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.*
-->

## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 5,175 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: 43.23 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.25 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                              | anchor                                                                                           |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | <code>Aquest tràmit us permet consultar informació de les anotacions d'entrada i sortida que hi consten al registre de l'Ajuntament de Sant Quirze del Vallès.</code> | <code>Quin és el format de les dades de sortida del tràmit?</code>                               |
  | <code>Tràmit a través del qual la persona interessada posa en coneixement de l’Ajuntament la voluntat de: ... Renunciar a una llicència prèviament atorgada.</code>   | <code>Quin és el resultat de la renúncia a una llicència urbanística prèviament atorgada?</code> |
  | <code>D’acord amb el plànol d'ubicació de parades: Mercat de diumenges a Les Fonts</code>                                                                             | <code>Quin és el plànol d'ubicació de parades del mercat de diumenges a Les Fonts?</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 |
|:---------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4938    | 10      | 4.1082        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9877    | 20      | 3.2445        | 0.1490                  | 0.1440                 | 0.1466                 | 0.1546                 | 0.1249                | 0.1521                 |
| 1.4815    | 30      | 1.9296        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9753    | 40      | 1.7067        | 0.1607                  | 0.1548                 | 0.1567                 | 0.1648                 | 0.1448                | 0.1593                 |
| 2.4691    | 50      | 0.9578        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9630    | 60      | 1.003         | 0.1640                  | 0.1699                 | 0.1660                 | 0.1695                 | 0.1568                | 0.1592                 |
| 3.4568    | 70      | 0.6298        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9506    | 80      | 0.7035        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.0       | 81      | -             | 0.1707                  | 0.1657                 | 0.1769                 | 0.1690                 | 0.1610                | 0.1719                 |
| 4.4444    | 90      | 0.4606        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9383    | 100     | 0.5131        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9877    | 101     | -             | 0.1645                  | 0.1686                 | 0.1669                 | 0.1620                 | 0.1580                | 0.1722                 |
| 5.4321    | 110     | 0.3748        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.9259    | 120     | 0.4799        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.9753    | 121     | -             | 0.1670                  | 0.1670                 | 0.1725                 | 0.1711                 | 0.1628                | 0.1715                 |
| 6.4198    | 130     | 0.3237        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.9136    | 140     | 0.4132        | -                       | -                      | -                      | -                      | -                     | -                      |
| **6.963** | **141** | **-**         | **0.1746**              | **0.1757**             | **0.1697**             | **0.1746**             | **0.1655**            | **0.1746**             |
| 7.4074    | 150     | 0.3169        | -                       | -                      | -                      | -                      | -                     | -                      |
| 7.9012    | 160     | 0.3438        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.0       | 162     | -             | 0.1692                  | 0.1698                 | 0.1718                 | 0.1735                 | 0.1707                | 0.1656                 |
| 8.3951    | 170     | 0.2987        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.8889    | 180     | 0.3193        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.9877    | 182     | -             | 0.1703                  | 0.1703                 | 0.1695                 | 0.1710                 | 0.1619                | 0.1666                 |
| 9.3827    | 190     | 0.2883        | -                       | -                      | -                      | -                      | -                     | -                      |
| 9.8765    | 200     | 0.3098        | 0.1652                  | 0.1722                 | 0.1685                 | 0.1755                 | 0.1676                | 0.1638                 |

* 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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