sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:6749 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:6749
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: La presentació de la comunicació prèvia, acompanyada de la documentació
    exigida, habilita a la persona interessada a executar els actes que s'hi descriuen,
    des del dia de la seva presentació, sens perjudici de les facultats de comprovació,
    control i inspecció de l'Ajuntament.
  sentences:
  - Quin és el resultat de la llicència d'usos i obres provisionals en relació amb
    altres autoritzacions administratives?
  - Quin és el paper de la persona interessada en aquest tràmit?
  - Quin és el tipus d'impost que es beneficia d'aquest tràmit?
- source_sentence: L'aportació de residus a la Deixalleria municipal us permet obtenir
    una bonificació de la taxa de residus del 15%.
  sentences:
  - Quin és el benefici de la Deixalleria municipal?
  - Quin és el benefici de tenir un volant de convivència?
  - Quin és el benefici de tenir el certificat del nombre d’habitants i habitatges
    del Padró d’Habitants?
- source_sentence: La presentació de la comunicació prèvia, acompanyada de la documentació
    exigida, habilita a la persona interessada a executar els actes que s'hi descriuen,
    des del dia de la seva presentació, sens perjudici de les facultats de comprovació,
    control i inspecció de l’Ajuntament.
  sentences:
  - Quin és el resultat de la presentació de la documentació exigida?
  - Quina és la condició per a la concessió de la bonificació?
  - On es troben els drets funeraris que es volen canviar?
- source_sentence: Renovació de concessió de drets funeraris a llarg termini (cementiri)
  sentences:
  - Quin és el requisit per aturar o estacionar el vehicle amb la targeta d'aparcament
    de transport col·lectiu?
  - Quin és el benefici de la concessió de drets funeraris a llarg termini?
  - Quin és el tipus de residus que es requereixen per a la bonificació?
- source_sentence: La presentació de la sol·licitud no dona dret al muntatge de la
    parada.
  sentences:
  - Quin és el motiu per canviar la persona titular dels drets funeraris?
  - Quin és el propòsit de la reunió informativa i de coordinació?
  - Quin és el requisit per a la presentació de la sol·licitud d’autorització?
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.044
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.116
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3506666666666667
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.044
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03866666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.036
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03506666666666667
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.044
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.116
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3506666666666667
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16592235166459846
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11099682539682543
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.13414156200645738
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.04133333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.116
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.17866666666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3626666666666667
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04133333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03866666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03573333333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03626666666666667
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04133333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.116
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.17866666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3626666666666667
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16902152680215465
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11157989417989429
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.13412743689937764
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.04666666666666667
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.116
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.17866666666666667
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.356
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04666666666666667
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03866666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03573333333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03560000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04666666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.116
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.17866666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.356
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16772455344289713
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11209576719576728
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.13459804045251053
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.03866666666666667
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.10666666666666667
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.17066666666666666
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3413333333333333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.03866666666666667
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.035555555555555556
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.034133333333333335
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.034133333333333335
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03866666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.10666666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.17066666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3413333333333333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.15868936356762114
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.10455608465608475
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.12901246498692368
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.04933333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.12266666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.19866666666666666
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.36666666666666664
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04933333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.040888888888888884
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.039733333333333336
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03666666666666667
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04933333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12266666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.19866666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.36666666666666664
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.17594327999948436
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11901798941798955
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14198426639116846
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.037333333333333336
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.09466666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.15733333333333333
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.34
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.037333333333333336
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03155555555555555
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03146666666666667
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.034
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.037333333333333336
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.09466666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.15733333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.34
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1535334048621682
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.09865185185185205
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.12262604132052936
      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/sqv2")
# Run inference
sentences = [
    'La presentació de la sol·licitud no dona dret al muntatge de la parada.',
    'Quin és el requisit per a la presentació de la sol·licitud d’autorització?',
    'Quin és el motiu per canviar la persona titular dels drets funeraris?',
]
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   | 0.044      |
| cosine_accuracy@3   | 0.116      |
| cosine_accuracy@5   | 0.18       |
| cosine_accuracy@10  | 0.3507     |
| cosine_precision@1  | 0.044      |
| cosine_precision@3  | 0.0387     |
| cosine_precision@5  | 0.036      |
| cosine_precision@10 | 0.0351     |
| cosine_recall@1     | 0.044      |
| cosine_recall@3     | 0.116      |
| cosine_recall@5     | 0.18       |
| cosine_recall@10    | 0.3507     |
| cosine_ndcg@10      | 0.1659     |
| cosine_mrr@10       | 0.111      |
| **cosine_map@100**  | **0.1341** |

#### 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   | 0.0413     |
| cosine_accuracy@3   | 0.116      |
| cosine_accuracy@5   | 0.1787     |
| cosine_accuracy@10  | 0.3627     |
| cosine_precision@1  | 0.0413     |
| cosine_precision@3  | 0.0387     |
| cosine_precision@5  | 0.0357     |
| cosine_precision@10 | 0.0363     |
| cosine_recall@1     | 0.0413     |
| cosine_recall@3     | 0.116      |
| cosine_recall@5     | 0.1787     |
| cosine_recall@10    | 0.3627     |
| cosine_ndcg@10      | 0.169      |
| cosine_mrr@10       | 0.1116     |
| **cosine_map@100**  | **0.1341** |

#### 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   | 0.0467     |
| cosine_accuracy@3   | 0.116      |
| cosine_accuracy@5   | 0.1787     |
| cosine_accuracy@10  | 0.356      |
| cosine_precision@1  | 0.0467     |
| cosine_precision@3  | 0.0387     |
| cosine_precision@5  | 0.0357     |
| cosine_precision@10 | 0.0356     |
| cosine_recall@1     | 0.0467     |
| cosine_recall@3     | 0.116      |
| cosine_recall@5     | 0.1787     |
| cosine_recall@10    | 0.356      |
| cosine_ndcg@10      | 0.1677     |
| cosine_mrr@10       | 0.1121     |
| **cosine_map@100**  | **0.1346** |

#### 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   | 0.0387    |
| cosine_accuracy@3   | 0.1067    |
| cosine_accuracy@5   | 0.1707    |
| cosine_accuracy@10  | 0.3413    |
| cosine_precision@1  | 0.0387    |
| cosine_precision@3  | 0.0356    |
| cosine_precision@5  | 0.0341    |
| cosine_precision@10 | 0.0341    |
| cosine_recall@1     | 0.0387    |
| cosine_recall@3     | 0.1067    |
| cosine_recall@5     | 0.1707    |
| cosine_recall@10    | 0.3413    |
| cosine_ndcg@10      | 0.1587    |
| cosine_mrr@10       | 0.1046    |
| **cosine_map@100**  | **0.129** |

#### 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   | 0.0493    |
| cosine_accuracy@3   | 0.1227    |
| cosine_accuracy@5   | 0.1987    |
| cosine_accuracy@10  | 0.3667    |
| cosine_precision@1  | 0.0493    |
| cosine_precision@3  | 0.0409    |
| cosine_precision@5  | 0.0397    |
| cosine_precision@10 | 0.0367    |
| cosine_recall@1     | 0.0493    |
| cosine_recall@3     | 0.1227    |
| cosine_recall@5     | 0.1987    |
| cosine_recall@10    | 0.3667    |
| cosine_ndcg@10      | 0.1759    |
| cosine_mrr@10       | 0.119     |
| **cosine_map@100**  | **0.142** |

#### 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   | 0.0373     |
| cosine_accuracy@3   | 0.0947     |
| cosine_accuracy@5   | 0.1573     |
| cosine_accuracy@10  | 0.34       |
| cosine_precision@1  | 0.0373     |
| cosine_precision@3  | 0.0316     |
| cosine_precision@5  | 0.0315     |
| cosine_precision@10 | 0.034      |
| cosine_recall@1     | 0.0373     |
| cosine_recall@3     | 0.0947     |
| cosine_recall@5     | 0.1573     |
| cosine_recall@10    | 0.34       |
| cosine_ndcg@10      | 0.1535     |
| cosine_mrr@10       | 0.0987     |
| **cosine_map@100**  | **0.1226** |

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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: 6,749 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: 6 tokens</li><li>mean: 42.03 tokens</li><li>max: 106 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.32 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                            | anchor                                                                                       |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|
  | <code>Aquest tràmit us permet compensar deutes de naturalesa pública a favor de l'Ajuntament, sigui quin sigui el seu estat (voluntari/executiu), amb crèdits reconeguts per aquest a favor del mateix deutor, i que el seu estat sigui pendent de pagament.</code> | <code>Quin és el benefici de la compensació de deutes amb crèdits?</code>                    |
  | <code>El seu objecte és que -prèviament a la seva execució material- l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament, així com a les ordenances municipals sobre l’ús del sòl i edificació.</code>                                     | <code>Quin és el paper de les ordenances municipals en aquest tràmit?</code>                 |
  | <code>Comunicació prèvia del manteniment en espais, zones o instal·lacions comunitàries interiors dels edificis (reparació i/o millora de materials).</code>                                                                                                        | <code>Quin és el límit del manteniment en espais comunitaris interiors dels edificis?</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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3791     | 10      | 3.0867        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.7583     | 20      | 2.4414        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9858     | 26      | -             | 0.1266                  | 0.1255                 | 0.1232                 | 0.1257                 | 0.1091                | 0.1345                 |
| 1.1351     | 30      | 1.7091        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.5142     | 40      | 1.2495        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8934     | 50      | 0.9813        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9692     | 52      | -             | 0.1315                  | 0.1325                 | 0.1285                 | 0.1328                 | 0.1218                | 0.1309                 |
| 2.2701     | 60      | 0.6918        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.6493     | 70      | 0.7146        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9905     | 79      | -             | 0.1370                  | 0.1344                 | 0.1355                 | 0.1338                 | 0.1269                | 0.1363                 |
| 3.0261     | 80      | 0.6002        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4052     | 90      | 0.4816        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7844     | 100     | 0.4949        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9739     | 105     | -             | 0.1357                  | 0.1393                 | 0.1302                 | 0.1347                 | 0.1204                | 0.1354                 |
| 4.1611     | 110     | 0.474         | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.5403     | 120     | 0.4692        | -                       | -                      | -                      | -                      | -                     | -                      |
| **4.9194** | **130** | **0.4484**    | **0.1341**              | **0.142**              | **0.129**              | **0.1346**             | **0.1226**            | **0.1341**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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