sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:5520 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-m3 base_model:finetune:BAAI/bge-m3 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-m3
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5520
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Pagar un rebut o una liquidació pendent de pagament
  sentences:
  - Què és el tràmit per pagar un rebut o liquidació?
  - Quin és el tràmit que permet la inscripció d'una entitat o associació?
  - Quin és el límit de temps per a la instal·lació de tanques provisionals?
- source_sentence: Mitjançant decret de data 11/10/2022 núm. 202204494 s'inicia el
    procés de concurrència competitiva per accedir a les parades vacants del mercat
    de les Fonts.
  sentences:
  - Quin és el mercat on es va iniciar el procés de concurrència competitiva per accedir
    a les parades vacants?
  - Puc sol·licitar un certificat històric d'empadronament per a una persona que ja
    no viu al municipi?
  - Necessito obtenir un duplicat del títol de dret funerari perquè he perdut l'original
- source_sentence: Comunicar les dades per realitzar la notificació electrònica de
    tots els procediments en què l’obligat legal sigui titular o part implicada, i
    hagi de ser notificat o notificada.
  sentences:
  - Quin és el paper de l'Ajuntament en la inspecció de les condicions específiques?
  - Quin és el tràmit relacionat amb la targeta ciutadana de serveis?
  - Qui és el titular o part implicada en els procediments?
- source_sentence: Aquest tràmit permet sol·licitar l'informe municipal sobre la integració
    social de persones estrangeres.
  sentences:
  - Puc canviar la concessió del meu dret funerari per una raó específica?
  - Quin és el procediment per a obtenir l'informe d'inserció social?
  - Quin és el propòsit de la formació en higiene alimentària
- source_sentence: Permet tramitar la baixa de les activitats esportives municipals.
  sentences:
  - Quin és el procés per a donar de baixa una activitat esportiva?
  - On es pot recollir els dorsals el dia de la cursa?
  - Quin és el benefici fiscal que es pot obtenir?
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: 0.1
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22608695652173913
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30434782608695654
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4956521739130435
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0753623188405797
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.060869565217391314
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04956521739130433
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22608695652173913
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30434782608695654
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4956521739130435
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2644535096144644
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19486714975845426
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21422014718167715
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.1
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21304347826086956
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49130434782608695
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07101449275362319
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06000000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04913043478260868
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21304347826086956
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49130434782608695
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2611989525147102
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19224465148378198
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21168860407432996
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.09565217391304348
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.25217391304347825
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3217391304347826
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5043478260869565
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.09565217391304348
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08405797101449275
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06434782608695652
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05043478260869564
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.09565217391304348
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.25217391304347825
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3217391304347826
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5043478260869565
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2736727362077943
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20330400276052454
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2225493022129085
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.09130434782608696
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.24347826086956523
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32608695652173914
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4782608695652174
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.09130434782608696
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08115942028985507
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06521739130434782
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04782608695652173
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.09130434782608696
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.24347826086956523
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32608695652173914
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4782608695652174
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25842339032219125
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19112146307798494
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21262325852877148
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.09565217391304348
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2217391304347826
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32608695652173914
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5130434782608696
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.09565217391304348
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07391304347826087
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06521739130434782
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05130434782608694
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.09565217391304348
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2217391304347826
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32608695652173914
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5130434782608696
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2703816814799584
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1968685300207041
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21575875323163748
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.10434782608695652
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.23478260869565218
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3217391304347826
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49130434782608695
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10434782608695652
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0782608695652174
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06434782608695652
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049130434782608694
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10434782608695652
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23478260869565218
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3217391304347826
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49130434782608695
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.268671836286108
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20097135955831624
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22058427749634182
      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-v5-5ep")
# Run inference
sentences = [
    'Permet tramitar la baixa de les activitats esportives municipals.',
    'Quin és el procés per a donar de baixa una activitat esportiva?',
    'Quin és el benefici fiscal que es pot obtenir?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
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### Out-of-Scope Use

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

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.1        |
| cosine_accuracy@3   | 0.2261     |
| cosine_accuracy@5   | 0.3043     |
| cosine_accuracy@10  | 0.4957     |
| cosine_precision@1  | 0.1        |
| cosine_precision@3  | 0.0754     |
| cosine_precision@5  | 0.0609     |
| cosine_precision@10 | 0.0496     |
| cosine_recall@1     | 0.1        |
| cosine_recall@3     | 0.2261     |
| cosine_recall@5     | 0.3043     |
| cosine_recall@10    | 0.4957     |
| cosine_ndcg@10      | 0.2645     |
| cosine_mrr@10       | 0.1949     |
| **cosine_map@100**  | **0.2142** |

#### 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   | 0.1        |
| cosine_accuracy@3   | 0.213      |
| cosine_accuracy@5   | 0.3        |
| cosine_accuracy@10  | 0.4913     |
| cosine_precision@1  | 0.1        |
| cosine_precision@3  | 0.071      |
| cosine_precision@5  | 0.06       |
| cosine_precision@10 | 0.0491     |
| cosine_recall@1     | 0.1        |
| cosine_recall@3     | 0.213      |
| cosine_recall@5     | 0.3        |
| cosine_recall@10    | 0.4913     |
| cosine_ndcg@10      | 0.2612     |
| cosine_mrr@10       | 0.1922     |
| **cosine_map@100**  | **0.2117** |

#### 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   | 0.0957     |
| cosine_accuracy@3   | 0.2522     |
| cosine_accuracy@5   | 0.3217     |
| cosine_accuracy@10  | 0.5043     |
| cosine_precision@1  | 0.0957     |
| cosine_precision@3  | 0.0841     |
| cosine_precision@5  | 0.0643     |
| cosine_precision@10 | 0.0504     |
| cosine_recall@1     | 0.0957     |
| cosine_recall@3     | 0.2522     |
| cosine_recall@5     | 0.3217     |
| cosine_recall@10    | 0.5043     |
| cosine_ndcg@10      | 0.2737     |
| cosine_mrr@10       | 0.2033     |
| **cosine_map@100**  | **0.2225** |

#### 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   | 0.0913     |
| cosine_accuracy@3   | 0.2435     |
| cosine_accuracy@5   | 0.3261     |
| cosine_accuracy@10  | 0.4783     |
| cosine_precision@1  | 0.0913     |
| cosine_precision@3  | 0.0812     |
| cosine_precision@5  | 0.0652     |
| cosine_precision@10 | 0.0478     |
| cosine_recall@1     | 0.0913     |
| cosine_recall@3     | 0.2435     |
| cosine_recall@5     | 0.3261     |
| cosine_recall@10    | 0.4783     |
| cosine_ndcg@10      | 0.2584     |
| cosine_mrr@10       | 0.1911     |
| **cosine_map@100**  | **0.2126** |

#### 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   | 0.0957     |
| cosine_accuracy@3   | 0.2217     |
| cosine_accuracy@5   | 0.3261     |
| cosine_accuracy@10  | 0.513      |
| cosine_precision@1  | 0.0957     |
| cosine_precision@3  | 0.0739     |
| cosine_precision@5  | 0.0652     |
| cosine_precision@10 | 0.0513     |
| cosine_recall@1     | 0.0957     |
| cosine_recall@3     | 0.2217     |
| cosine_recall@5     | 0.3261     |
| cosine_recall@10    | 0.513      |
| cosine_ndcg@10      | 0.2704     |
| cosine_mrr@10       | 0.1969     |
| **cosine_map@100**  | **0.2158** |

#### 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   | 0.1043     |
| cosine_accuracy@3   | 0.2348     |
| cosine_accuracy@5   | 0.3217     |
| cosine_accuracy@10  | 0.4913     |
| cosine_precision@1  | 0.1043     |
| cosine_precision@3  | 0.0783     |
| cosine_precision@5  | 0.0643     |
| cosine_precision@10 | 0.0491     |
| cosine_recall@1     | 0.1043     |
| cosine_recall@3     | 0.2348     |
| cosine_recall@5     | 0.3217     |
| cosine_recall@10    | 0.4913     |
| cosine_ndcg@10      | 0.2687     |
| cosine_mrr@10       | 0.201      |
| **cosine_map@100**  | **0.2206** |

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## Bias, Risks and Limitations

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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: 43.7 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.51 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`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4638     | 10      | 4.122         | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9275     | 20      | 2.7131        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9739     | 21      | -             | 0.2085                  | 0.1973                 | 0.1884                 | 0.2087                 | 0.1886                | 0.2177                 |
| 1.3913     | 30      | 1.6964        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8551     | 40      | 1.2311        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9942     | 43      | -             | 0.2148                  | 0.2135                 | 0.2170                 | 0.2351                 | 0.2091                | 0.2386                 |
| 2.3188     | 50      | 0.9216        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.7826     | 60      | 0.737         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9681     | 64      | -             | 0.2145                  | 0.2058                 | 0.2072                 | 0.2277                 | 0.2127                | 0.2085                 |
| 3.2464     | 70      | 0.6678        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7101     | 80      | 0.555         | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9884     | 86      | -             | 0.2028                  | 0.2154                 | 0.2117                 | 0.2331                 | 0.2113                | 0.2028                 |
| 4.1739     | 90      | 0.5542        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.6377     | 100     | 0.5058        | -                       | -                      | -                      | -                      | -                     | -                      |
| **4.8696** | **105** | **-**         | **0.2142**              | **0.2158**             | **0.2126**             | **0.2225**             | **0.2206**            | **0.2117**             |

* 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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