---
base_model: BAAI/bge-m3
datasets: []
language: []
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:9593
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Aquest tràmit permet a la nova persona titular sol·licitar el canvi
    de nom d'una llicència de gual, sempre que no variïn la utilització ni les característiques
    de la llicència concedida prèviament, i s’acompleixen les ordenances vigents.
  sentences:
  - Quin és el resultat de la presentació del tràmit de comunicació d'inici i modificació
    substancial d'activitat en un establiment?
  - Quin és el benefici per a les entitats especialitzades de la gestió delegada?
  - Necessito canviar el titular de la meva llicència de gual
- source_sentence: Mitjançant aquest tràmit la persona interessada posa en coneixement
    de l'Ajuntament l'inici o modificació substancial d'una activitat econòmica, de
    les incloses en l'annex de la Llei de facilitació de l'activitat econòmica, i
    hi adjunta el projecte i el certificat tècnic acreditatiu del compliment dels
    requisits necessaris que estableix la normativa vigent per a l'exercici de l'activitat.
  sentences:
  - Quins canvis es poden fer en els tanques?
  - Què és necessari per gaudir d'exempció de les taxes per recollida d'escombraries?
  - Quin és el resultat de la presentació del certificat tècnic acreditatiu?
- source_sentence: La instal·lació i utilització d’una grua torre està subjecta a
    l’obtenció d’una llicència municipal.
  sentences:
  - Quin és el propòsit de la Declaració de baixa de la Taxa pel servei municipal
    complementari de recollida, tractament i eliminació de residus comercials?
  - Quin és el paper de la persona beneficiària en el pagament de l'ajut de lloguer
    just?
  - Què és necessari per a la instal·lació i utilització d'una grua torre?
- source_sentence: El procediment d'adjudicació serà mitjançant concurs públic, amb
    la presentació de la sol·licitud dins del termini establert per cada convocatòria,
    amb la priorització de casos amb seguiment social i educatiu a persones i famílies
    en situació de vulnerabilitat social i econòmica.
  sentences:
  - Quins són els casos que tenen prioritat en l'adjudicació dels habitatges del Fons
    d'Habitatges d'Inclusió Social?
  - Quin és el paper del certificat del nombre d'habitatges en el tràmit d'obertura
    d'una oficina de farmàcia?
  - Quin és el paper de la Junta de Govern Local en relació amb les garanties?
- source_sentence: Els comerciants locals han de sol·licitar els ajuts per al projecte
    de la targeta de fidelització dins del termini establert per l'Ajuntament de Sitges.
  sentences:
  - Quin és el paper de la persona cuidadora en la gestió de les emergències en la
    colònia felina?
  - Quin és el termini perquè els comerciants locals puguin sol·licitar els ajuts
    per al projecte de la targeta de fidelització?
  - Quin és el règim especial al qual han d'estar inscrites les persones per rebre
    els ajuts?
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: 0.0600375234521576
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.1303939962476548
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1801125703564728
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.32833020637898686
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0600375234521576
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04346466541588492
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.036022514071294566
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03283302063789869
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0600375234521576
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1303939962476548
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1801125703564728
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.32833020637898686
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16801025559505256
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.12051319276929036
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14641981337897508
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.05909943714821764
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.12195121951219512
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18105065666041276
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3302063789868668
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05909943714821764
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04065040650406503
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03621013133208256
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03302063789868668
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05909943714821764
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12195121951219512
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18105065666041276
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3302063789868668
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1674921436005172
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.119329044938801
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14541664461952028
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.058161350844277676
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.12851782363977485
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1904315196998124
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.32645403377110693
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.058161350844277676
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04283927454659161
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03808630393996248
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03264540337711069
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.058161350844277676
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12851782363977485
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1904315196998124
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.32645403377110693
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16736509943357222
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11985169302242468
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14638786229645445
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.054409005628517824
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11913696060037524
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18198874296435272
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3170731707317073
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.054409005628517824
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03971232020012507
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.036397748592870545
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03170731707317073
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.054409005628517824
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11913696060037524
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18198874296435272
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3170731707317073
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16104635688777047
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11454927186634503
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14146334434951485
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.054409005628517824
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.12195121951219512
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18198874296435272
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.31144465290806755
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.054409005628517824
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.04065040650406503
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.03639774859287054
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.031144465290806757
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.054409005628517824
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12195121951219512
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18198874296435272
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.31144465290806755
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.15963450508596505
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11438361773727633
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.14164175280264735
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.05065666041275797
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.11444652908067542
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.18292682926829268
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3076923076923077
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05065666041275797
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0381488430268918
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.036585365853658534
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.030769230769230767
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05065666041275797
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.11444652908067542
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.18292682926829268
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3076923076923077
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.1558660768539628
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11049949373120106
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.13758639006498824
      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). 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:** Unknown -->
<!-- - **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/sitgrsBAAIbge-m3-300824v2")
# Run inference
sentences = [
    "Els comerciants locals han de sol·licitar els ajuts per al projecte de la targeta de fidelització dins del termini establert per l'Ajuntament de Sitges.",
    'Quin és el termini perquè els comerciants locals puguin sol·licitar els ajuts per al projecte de la targeta de fidelització?',
    'Quin és el paper de la persona cuidadora en la gestió de les emergències en la colònia felina?',
]
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   | 0.06       |
| cosine_accuracy@3   | 0.1304     |
| cosine_accuracy@5   | 0.1801     |
| cosine_accuracy@10  | 0.3283     |
| cosine_precision@1  | 0.06       |
| cosine_precision@3  | 0.0435     |
| cosine_precision@5  | 0.036      |
| cosine_precision@10 | 0.0328     |
| cosine_recall@1     | 0.06       |
| cosine_recall@3     | 0.1304     |
| cosine_recall@5     | 0.1801     |
| cosine_recall@10    | 0.3283     |
| cosine_ndcg@10      | 0.168      |
| cosine_mrr@10       | 0.1205     |
| **cosine_map@100**  | **0.1464** |

#### 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   | 0.0591     |
| cosine_accuracy@3   | 0.122      |
| cosine_accuracy@5   | 0.1811     |
| cosine_accuracy@10  | 0.3302     |
| cosine_precision@1  | 0.0591     |
| cosine_precision@3  | 0.0407     |
| cosine_precision@5  | 0.0362     |
| cosine_precision@10 | 0.033      |
| cosine_recall@1     | 0.0591     |
| cosine_recall@3     | 0.122      |
| cosine_recall@5     | 0.1811     |
| cosine_recall@10    | 0.3302     |
| cosine_ndcg@10      | 0.1675     |
| cosine_mrr@10       | 0.1193     |
| **cosine_map@100**  | **0.1454** |

#### 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   | 0.0582     |
| cosine_accuracy@3   | 0.1285     |
| cosine_accuracy@5   | 0.1904     |
| cosine_accuracy@10  | 0.3265     |
| cosine_precision@1  | 0.0582     |
| cosine_precision@3  | 0.0428     |
| cosine_precision@5  | 0.0381     |
| cosine_precision@10 | 0.0326     |
| cosine_recall@1     | 0.0582     |
| cosine_recall@3     | 0.1285     |
| cosine_recall@5     | 0.1904     |
| cosine_recall@10    | 0.3265     |
| cosine_ndcg@10      | 0.1674     |
| cosine_mrr@10       | 0.1199     |
| **cosine_map@100**  | **0.1464** |

#### 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   | 0.0544     |
| cosine_accuracy@3   | 0.1191     |
| cosine_accuracy@5   | 0.182      |
| cosine_accuracy@10  | 0.3171     |
| cosine_precision@1  | 0.0544     |
| cosine_precision@3  | 0.0397     |
| cosine_precision@5  | 0.0364     |
| cosine_precision@10 | 0.0317     |
| cosine_recall@1     | 0.0544     |
| cosine_recall@3     | 0.1191     |
| cosine_recall@5     | 0.182      |
| cosine_recall@10    | 0.3171     |
| cosine_ndcg@10      | 0.161      |
| cosine_mrr@10       | 0.1145     |
| **cosine_map@100**  | **0.1415** |

#### 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   | 0.0544     |
| cosine_accuracy@3   | 0.122      |
| cosine_accuracy@5   | 0.182      |
| cosine_accuracy@10  | 0.3114     |
| cosine_precision@1  | 0.0544     |
| cosine_precision@3  | 0.0407     |
| cosine_precision@5  | 0.0364     |
| cosine_precision@10 | 0.0311     |
| cosine_recall@1     | 0.0544     |
| cosine_recall@3     | 0.122      |
| cosine_recall@5     | 0.182      |
| cosine_recall@10    | 0.3114     |
| cosine_ndcg@10      | 0.1596     |
| cosine_mrr@10       | 0.1144     |
| **cosine_map@100**  | **0.1416** |

#### 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   | 0.0507     |
| cosine_accuracy@3   | 0.1144     |
| cosine_accuracy@5   | 0.1829     |
| cosine_accuracy@10  | 0.3077     |
| cosine_precision@1  | 0.0507     |
| cosine_precision@3  | 0.0381     |
| cosine_precision@5  | 0.0366     |
| cosine_precision@10 | 0.0308     |
| cosine_recall@1     | 0.0507     |
| cosine_recall@3     | 0.1144     |
| cosine_recall@5     | 0.1829     |
| cosine_recall@10    | 0.3077     |
| cosine_ndcg@10      | 0.1559     |
| cosine_mrr@10       | 0.1105     |
| **cosine_map@100**  | **0.1376** |

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## Bias, Risks and Limitations

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 9,593 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: 3 tokens</li><li>mean: 49.72 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.22 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                           | anchor                                                                                                  |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
  | <code>Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament l’inici o modificació substancial d’una activitat econòmica.</code>      | <code>Quin és el paper de l'Ajuntament en la comunicació de modificació d'activitat?</code>             |
  | <code>El Carnet Blau és un carnet personal i intransferible que acredita el compliment dels requisits per a gaudir d'un conjunt de descomptes i avantatges.</code> | <code>Quin és el propòsit del Carnet Blau en relació amb els descomptes?</code>                         |
  | <code>Bonificació del 25% de l'import corresponent al consum d'aigua, la conservació d'escomeses, aforaments i comptadors així com els drets de connexió.</code>   | <code>Quin és l'objectiu de la bonificació de la taxa per distribució i subministrament d'aigua?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2667     | 10      | 3.4587        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.5333     | 20      | 2.8693        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.8        | 30      | 2.3094        | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9867     | 37      | -             | 0.1331                  | 0.1252                 | 0.1322                 | 0.1337                 | 0.1128                | 0.1347                 |
| 1.0667     | 40      | 1.6196        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.3333     | 50      | 1.1926        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.6        | 60      | 0.9497        | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.8667     | 70      | 0.882         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.0        | 75      | -             | 0.1372                  | 0.1272                 | 0.1298                 | 0.1365                 | 0.1212                | 0.1369                 |
| 2.1333     | 80      | 0.5621        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.4        | 90      | 0.4454        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.6667     | 100     | 0.4143        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9333     | 110     | 0.4014        | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9867     | 112     | -             | 0.1365                  | 0.1282                 | 0.1329                 | 0.1437                 | 0.1259                | 0.1390                 |
| 3.2        | 120     | 0.2863        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4667     | 130     | 0.1977        | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7333     | 140     | 0.2411        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.0        | 150     | 0.222         | 0.1355                  | 0.1308                 | 0.1378                 | 0.1346                 | 0.1239                | 0.1362                 |
| 4.2667     | 160     | 0.1705        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.5333     | 170     | 0.1522        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.8        | 180     | 0.1606        | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9867     | 187     | -             | 0.1441                  | 0.1305                 | 0.1344                 | 0.1373                 | 0.1356                | 0.1409                 |
| 5.0667     | 190     | 0.1281        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.3333     | 200     | 0.1099        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.6        | 210     | 0.0921        | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.8667     | 220     | 0.114         | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.0        | 225     | -             | 0.1371                  | 0.1361                 | 0.1331                 | 0.1371                 | 0.1351                | 0.1421                 |
| 6.1333     | 230     | 0.0703        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.4        | 240     | 0.0746        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.6667     | 250     | 0.0734        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.9333     | 260     | 0.0803        | -                       | -                      | -                      | -                      | -                     | -                      |
| 6.9867     | 262     | -             | 0.1447                  | 0.1400                 | 0.1422                 | 0.1397                 | 0.1376                | 0.1395                 |
| 7.2        | 270     | 0.0684        | -                       | -                      | -                      | -                      | -                     | -                      |
| 7.4667     | 280     | 0.0493        | -                       | -                      | -                      | -                      | -                     | -                      |
| 7.7333     | 290     | 0.0531        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.0        | 300     | 0.0705        | 0.1410                  | 0.1411                 | 0.1379                 | 0.1372                 | 0.1372                | 0.1380                 |
| 8.2667     | 310     | 0.0495        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.5333     | 320     | 0.0478        | -                       | -                      | -                      | -                      | -                     | -                      |
| 8.8        | 330     | 0.0455        | -                       | -                      | -                      | -                      | -                     | -                      |
| **8.9867** | **337** | **-**         | **0.1463**              | **0.1456**             | **0.1416**             | **0.1445**             | **0.1408**            | **0.1427**             |
| 9.0667     | 340     | 0.0495        | -                       | -                      | -                      | -                      | -                     | -                      |
| 9.3333     | 350     | 0.0457        | -                       | -                      | -                      | -                      | -                     | -                      |
| 9.6        | 360     | 0.0487        | -                       | -                      | -                      | -                      | -                     | -                      |
| 9.8667     | 370     | 0.0568        | 0.1464                  | 0.1416                 | 0.1415                 | 0.1464                 | 0.1376                | 0.1454                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.0.dev0
- Datasets: 2.21.0
- 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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