---
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:4091
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Aquest tràmit permet formalitzar la matrícula a les llars d’infants
    municipals, si l'infant ha estat admès al període de preinscripcions.
  sentences:
  - Quin és el tràmit que es realitza abans de la matrícula?
  - Quin és el propòsit de l'Ajuntament en aquest tràmit?
  - Què es pot fer amb les exclusions indegudes al Cens Electoral?
- source_sentence: També cal que facis aquest tràmit per revocar o modificar les dades
    de correu electrònic i/o telèfon mòbil facilitades per portar a terme les notificacions.
  sentences:
  - Què passa si vull canviar la meva adreça de correu electrònic?
  - Quin és el resultat de no comunicar la finalització de les obres en el termini
    establert?
  - Quin és el procés de selecció de personal de l'Ajuntament de Viladecavalls?
- source_sentence: Aquest tràmit et permet comunicar a l'ajuntament de Viladecavalls,
    l'actuació en representació fer efectuar un tràmit, d'acord a l'article 5 de la
    Llei 39/2015,d'1 d'octubre, del Procediment Administratiu Comú de les Administracions
    Públiques.
  sentences:
  - Quin és el registre que es relaciona amb les dades que es modifiquen?
  - Quan es pot consultar la llista definitiva d'admessos?
  - Quin és el paper de fer efectuar un tràmit en representació a tercers?
- source_sentence: La taxa per la prestació del Servei de Gestió dels Residus Municipals.
  sentences:
  - Quins són els motius per inscriure's al Servei Local d'Ocupació?
  - Quin és el document que es necessita per a la sol·licitud de volants col·lectius
    o de convivència?
  - Quin és el paper de la taxa d'escombraries en aquest procés?
- source_sentence: S'ha de comunicar la realització de focs d’esbarjo i qualsevol
    mena de crema de vegetació agrària en microexplotacions o petites explotacions
    agràries...
  sentences:
  - Què cal fer si no has rebut el document per pagar IVTM o IBI?
  - Quin és el tipus de explotacions agràries que estan subjectes a la comunicació
    de focs d'esbarjo o cremes de vegetació agrària en microexplotacions?
  - Quin és el paper de les bases de la convocatòria en la sol·licitud de subvenció?
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: 0.12408759124087591
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22627737226277372
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3357664233576642
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5328467153284672
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12408759124087591
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0754257907542579
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06715328467153285
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05328467153284672
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12408759124087591
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22627737226277372
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3357664233576642
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5328467153284672
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.28998901896488977
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21748928281774996
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24037395859471752
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.1386861313868613
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26277372262773724
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3357664233576642
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5693430656934306
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1386861313868613
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08759124087591241
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06715328467153284
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05693430656934306
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1386861313868613
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26277372262773724
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3357664233576642
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5693430656934306
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.31363827421519996
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23752751708956085
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2568041111732728
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.1386861313868613
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.27007299270072993
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3795620437956204
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5693430656934306
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.1386861313868613
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0900243309002433
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07591240875912408
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05693430656934306
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.1386861313868613
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.27007299270072993
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3795620437956204
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5693430656934306
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.317041085199572
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.24058046576294745
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2615607719139071
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.12408759124087591
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2773722627737226
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.32116788321167883
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5182481751824818
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12408759124087591
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09245742092457421
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06423357664233577
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.051824817518248176
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12408759124087591
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2773722627737226
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.32116788321167883
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5182481751824818
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.29042019634687105
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2218456725755996
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24399596123266679
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.10948905109489052
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.25547445255474455
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.40145985401459855
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5401459854014599
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10948905109489052
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08515815085158149
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08029197080291971
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05401459854014598
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10948905109489052
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.25547445255474455
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.40145985401459855
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5401459854014599
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2983398214582463
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.22380952380952376
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2454078859030295
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.10948905109489052
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.20437956204379562
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3284671532846715
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5547445255474452
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.10948905109489052
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06812652068126519
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06569343065693431
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05547445255474452
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.10948905109489052
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.20437956204379562
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3284671532846715
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5547445255474452
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.28965339873789575
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21023635731664925
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22988556376565739
      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/ST-tramits-VL-001-5ep")
# Run inference
sentences = [
    "S'ha de comunicar la realització de focs d’esbarjo i qualsevol mena de crema de vegetació agrària en microexplotacions o petites explotacions agràries...",
    "Quin és el tipus de explotacions agràries que estan subjectes a la comunicació de focs d'esbarjo o cremes de vegetació agrària en microexplotacions?",
    'Quin és el paper de les bases de la convocatòria en la sol·licitud de subvenció?',
]
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   | 0.1241     |
| cosine_accuracy@3   | 0.2263     |
| cosine_accuracy@5   | 0.3358     |
| cosine_accuracy@10  | 0.5328     |
| cosine_precision@1  | 0.1241     |
| cosine_precision@3  | 0.0754     |
| cosine_precision@5  | 0.0672     |
| cosine_precision@10 | 0.0533     |
| cosine_recall@1     | 0.1241     |
| cosine_recall@3     | 0.2263     |
| cosine_recall@5     | 0.3358     |
| cosine_recall@10    | 0.5328     |
| cosine_ndcg@10      | 0.29       |
| cosine_mrr@10       | 0.2175     |
| **cosine_map@100**  | **0.2404** |

#### 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   | 0.1387     |
| cosine_accuracy@3   | 0.2628     |
| cosine_accuracy@5   | 0.3358     |
| cosine_accuracy@10  | 0.5693     |
| cosine_precision@1  | 0.1387     |
| cosine_precision@3  | 0.0876     |
| cosine_precision@5  | 0.0672     |
| cosine_precision@10 | 0.0569     |
| cosine_recall@1     | 0.1387     |
| cosine_recall@3     | 0.2628     |
| cosine_recall@5     | 0.3358     |
| cosine_recall@10    | 0.5693     |
| cosine_ndcg@10      | 0.3136     |
| cosine_mrr@10       | 0.2375     |
| **cosine_map@100**  | **0.2568** |

#### 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   | 0.1387     |
| cosine_accuracy@3   | 0.2701     |
| cosine_accuracy@5   | 0.3796     |
| cosine_accuracy@10  | 0.5693     |
| cosine_precision@1  | 0.1387     |
| cosine_precision@3  | 0.09       |
| cosine_precision@5  | 0.0759     |
| cosine_precision@10 | 0.0569     |
| cosine_recall@1     | 0.1387     |
| cosine_recall@3     | 0.2701     |
| cosine_recall@5     | 0.3796     |
| cosine_recall@10    | 0.5693     |
| cosine_ndcg@10      | 0.317      |
| cosine_mrr@10       | 0.2406     |
| **cosine_map@100**  | **0.2616** |

#### 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   | 0.1241    |
| cosine_accuracy@3   | 0.2774    |
| cosine_accuracy@5   | 0.3212    |
| cosine_accuracy@10  | 0.5182    |
| cosine_precision@1  | 0.1241    |
| cosine_precision@3  | 0.0925    |
| cosine_precision@5  | 0.0642    |
| cosine_precision@10 | 0.0518    |
| cosine_recall@1     | 0.1241    |
| cosine_recall@3     | 0.2774    |
| cosine_recall@5     | 0.3212    |
| cosine_recall@10    | 0.5182    |
| cosine_ndcg@10      | 0.2904    |
| cosine_mrr@10       | 0.2218    |
| **cosine_map@100**  | **0.244** |

#### 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   | 0.1095     |
| cosine_accuracy@3   | 0.2555     |
| cosine_accuracy@5   | 0.4015     |
| cosine_accuracy@10  | 0.5401     |
| cosine_precision@1  | 0.1095     |
| cosine_precision@3  | 0.0852     |
| cosine_precision@5  | 0.0803     |
| cosine_precision@10 | 0.054      |
| cosine_recall@1     | 0.1095     |
| cosine_recall@3     | 0.2555     |
| cosine_recall@5     | 0.4015     |
| cosine_recall@10    | 0.5401     |
| cosine_ndcg@10      | 0.2983     |
| cosine_mrr@10       | 0.2238     |
| **cosine_map@100**  | **0.2454** |

#### 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   | 0.1095     |
| cosine_accuracy@3   | 0.2044     |
| cosine_accuracy@5   | 0.3285     |
| cosine_accuracy@10  | 0.5547     |
| cosine_precision@1  | 0.1095     |
| cosine_precision@3  | 0.0681     |
| cosine_precision@5  | 0.0657     |
| cosine_precision@10 | 0.0555     |
| cosine_recall@1     | 0.1095     |
| cosine_recall@3     | 0.2044     |
| cosine_recall@5     | 0.3285     |
| cosine_recall@10    | 0.5547     |
| cosine_ndcg@10      | 0.2897     |
| cosine_mrr@10       | 0.2102     |
| **cosine_map@100**  | **0.2299** |

<!--
## Bias, Risks and Limitations

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

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 4,091 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: 39.34 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.77 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                               | anchor                                                                                                               |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
  | <code>Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació prèvia corresponent.</code> | <code>Quin és el resultat esperat després d'obtenir l'informe previ en matèria d'incendis?</code>                    |
  | <code>El certificat tècnic és un requisit per a l'exercici d'una activitat econòmica innòcua.</code>                                                                                   | <code>Quin és el paper del certificat tècnic en la Declaració responsable d'obertura?</code>                         |
  | <code>El document necessari per realitzar l'autoliquidació de taxa per llicència de primera ocupació és la llicència de primera ocupació de l'immoble.</code>                          | <code>Quin és el document necessari per realitzar l'autoliquidació de taxa per llicència de primera ocupació?</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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.625      | 10     | 4.3533        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.0        | 16     | -             | 0.2076                  | 0.2123                 | 0.2055                 | 0.1996                 | 0.2188                 | 0.1861                |
| 1.2461     | 20     | 2.4149        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.8711     | 30     | 1.1968        | -                       | -                      | -                      | -                      | -                      | -                     |
| 1.9961     | 32     | -             | 0.2056                  | 0.2318                 | 0.2363                 | 0.1932                 | 0.2330                 | 0.2255                |
| 2.4922     | 40     | 0.7983        | -                       | -                      | -                      | -                      | -                      | -                     |
| **2.9922** | **48** | **-**         | **0.2322**              | **0.2512**             | **0.2514**             | **0.2385**             | **0.2437**             | **0.2489**            |
| 3.1133     | 50     | 0.4869        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.7383     | 60     | 0.3793        | -                       | -                      | -                      | -                      | -                      | -                     |
| 3.9883     | 64     | -             | 0.2414                  | 0.2364                 | 0.2365                 | 0.2244                 | 0.2167                 | 0.2190                |
| 4.3594     | 70     | 0.3421        | -                       | -                      | -                      | -                      | -                      | -                     |
| 4.9844     | 80     | 0.2925        | 0.2404                  | 0.2568                 | 0.2616                 | 0.2440                 | 0.2454                 | 0.2299                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.1.0.dev0
- Datasets: 3.1.0
- 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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