sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:4173 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:cross-encoder/ms-marco-MiniLM-L-4-v2 base_model:finetune:cross-encoder/ms-marco-MiniLM-L-4-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: cross-encoder/ms-marco-MiniLM-L-4-v2
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:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Aquelles persones (físiques o jurídiques) que es disposin a exercir
    una de les següents activitats: ... Han de comunicar-ho a l''Ajuntament prèviament
    a la data prevista de la seva obertura.'
  sentences:
  - Quin és el benefici que es pretén obtenir amb aquests ajuts econòmics per a les
    empreses d'hostaleria i restauració?
  - Quin és el benefici del sistema de teleassistència per a les persones que viuen
    amb altres persones amb discapacitat?
  - Quin és el propòsit de la comunicació prèvia d'una activitat recreativa o un espectacle
    públic?
- source_sentence: Les persones titulars d’activitats que generin residus comercials
    o industrials assimilables als municipals, vindran obligats a acreditar davant
    l’Ajuntament que tenen contractat un gestor autoritzat per la recollida, tractament
    i eliminació dels residus que produeixi l’activitat corresponent.
  sentences:
  - Quin és el paper de l'Ajuntament en l'acreditació de recollida de residus?
  - Quin és el benefici de les activitats d'animació socio-cultural?
  - Quin és el benefici de l'ajut per a la creació de noves empreses?
- source_sentence: Modificació de sol·licitud de permís d'ocupació de la via pública
    per filmacions, rodatges o sessions fotogràfiques.
  sentences:
  - Quin és el grau de discapacitat mínim per a rebre l'ajut de 300€ anuals?
  - Quin és el requisit per a la constitució o modificació del règim de propietat
    horitzontal?
  - Quin és el tipus de permís que es modifica?
- source_sentence: El beneficiari és l'encarregat de complir les condicions de la
    subvenció i de presentar els informes de seguiment del projecte.
  sentences:
  - Quin és el paper del beneficiari en el procés de subvencions?
  - Quin és el càlcul dels interessos de demora en el fraccionament i l'ajornament?
  - Quin és el període de temps en què es poden efectuar les despeses mèdiques per
    a rebre l'ajuda?
- source_sentence: Aquest tràmit permet sol·licitar la llicència per a realitzar obres
    d'excavació a la via pública per a la instal·lació o reparació d'infraestructures
    de serveis i subministraments.
  sentences:
  - Quin és el paper de la via pública en aquest tràmit?
  - Quin és el requisit principal per obtenir el certificat?
  - Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?
model-index:
- name: SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.03879310344827586
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.08836206896551724
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.12284482758620689
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.1875
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.03879310344827586
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.029454022988505746
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.024568965517241383
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.01875
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03879310344827586
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.08836206896551724
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.12284482758620689
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.1875
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.10242929123982092
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.0765787493158183
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.09057915519577017
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.03879310344827586
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.08836206896551724
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.12284482758620689
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.1875
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.03879310344827586
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.029454022988505746
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.024568965517241383
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.01875
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03879310344827586
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.08836206896551724
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.12284482758620689
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.1875
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.10242929123982092
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.0765787493158183
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.09057915519577017
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.03879310344827586
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.08405172413793104
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.12931034482758622
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.1853448275862069
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.03879310344827586
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.028017241379310345
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.02586206896551724
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.01853448275862069
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.03879310344827586
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.08405172413793104
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.12931034482758622
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.1853448275862069
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.10210354316803219
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.07668821839080461
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.08986051867751395
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.034482758620689655
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.09482758620689655
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.1271551724137931
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.1853448275862069
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.034482758620689655
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.03160919540229885
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.025431034482758623
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.018534482758620692
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.034482758620689655
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.09482758620689655
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1271551724137931
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.1853448275862069
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.10100148719550092
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.07532755199781063
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.08992740582596234
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.034482758620689655
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.08405172413793104
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.10344827586206896
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.17025862068965517
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.034482758620689655
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.028017241379310345
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.020689655172413796
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.017025862068965517
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.034482758620689655
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.08405172413793104
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.10344827586206896
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.17025862068965517
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.09332320019708457
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.0700302750410509
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.08369264546406543
      name: Cosine Map@100
---

# SentenceTransformer based on cross-encoder/ms-marco-MiniLM-L-4-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2). It maps sentences & paragraphs to a 384-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:** [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) <!-- at revision 1f1ab0943a42a52afd702e7e8337bec985c189ea -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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/sitges10242608-4ep-rerankv2")
# Run inference
sentences = [
    "Aquest tràmit permet sol·licitar la llicència per a realitzar obres d'excavació a la via pública per a la instal·lació o reparació d'infraestructures de serveis i subministraments.",
    'Quin és el paper de la via pública en aquest tràmit?',
    "Quin és l'objectiu de presentar una denúncia per presumpta infracció urbanística?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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.*
-->

## Evaluation

### Metrics

#### 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   | 0.0388     |
| cosine_accuracy@3   | 0.0884     |
| cosine_accuracy@5   | 0.1228     |
| cosine_accuracy@10  | 0.1875     |
| cosine_precision@1  | 0.0388     |
| cosine_precision@3  | 0.0295     |
| cosine_precision@5  | 0.0246     |
| cosine_precision@10 | 0.0187     |
| cosine_recall@1     | 0.0388     |
| cosine_recall@3     | 0.0884     |
| cosine_recall@5     | 0.1228     |
| cosine_recall@10    | 0.1875     |
| cosine_ndcg@10      | 0.1024     |
| cosine_mrr@10       | 0.0766     |
| **cosine_map@100**  | **0.0906** |

#### 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   | 0.0388     |
| cosine_accuracy@3   | 0.0884     |
| cosine_accuracy@5   | 0.1228     |
| cosine_accuracy@10  | 0.1875     |
| cosine_precision@1  | 0.0388     |
| cosine_precision@3  | 0.0295     |
| cosine_precision@5  | 0.0246     |
| cosine_precision@10 | 0.0187     |
| cosine_recall@1     | 0.0388     |
| cosine_recall@3     | 0.0884     |
| cosine_recall@5     | 0.1228     |
| cosine_recall@10    | 0.1875     |
| cosine_ndcg@10      | 0.1024     |
| cosine_mrr@10       | 0.0766     |
| **cosine_map@100**  | **0.0906** |

#### 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   | 0.0388     |
| cosine_accuracy@3   | 0.0841     |
| cosine_accuracy@5   | 0.1293     |
| cosine_accuracy@10  | 0.1853     |
| cosine_precision@1  | 0.0388     |
| cosine_precision@3  | 0.028      |
| cosine_precision@5  | 0.0259     |
| cosine_precision@10 | 0.0185     |
| cosine_recall@1     | 0.0388     |
| cosine_recall@3     | 0.0841     |
| cosine_recall@5     | 0.1293     |
| cosine_recall@10    | 0.1853     |
| cosine_ndcg@10      | 0.1021     |
| cosine_mrr@10       | 0.0767     |
| **cosine_map@100**  | **0.0899** |

#### 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   | 0.0345     |
| cosine_accuracy@3   | 0.0948     |
| cosine_accuracy@5   | 0.1272     |
| cosine_accuracy@10  | 0.1853     |
| cosine_precision@1  | 0.0345     |
| cosine_precision@3  | 0.0316     |
| cosine_precision@5  | 0.0254     |
| cosine_precision@10 | 0.0185     |
| cosine_recall@1     | 0.0345     |
| cosine_recall@3     | 0.0948     |
| cosine_recall@5     | 0.1272     |
| cosine_recall@10    | 0.1853     |
| cosine_ndcg@10      | 0.101      |
| cosine_mrr@10       | 0.0753     |
| **cosine_map@100**  | **0.0899** |

#### 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   | 0.0345     |
| cosine_accuracy@3   | 0.0841     |
| cosine_accuracy@5   | 0.1034     |
| cosine_accuracy@10  | 0.1703     |
| cosine_precision@1  | 0.0345     |
| cosine_precision@3  | 0.028      |
| cosine_precision@5  | 0.0207     |
| cosine_precision@10 | 0.017      |
| cosine_recall@1     | 0.0345     |
| cosine_recall@3     | 0.0841     |
| cosine_recall@5     | 0.1034     |
| cosine_recall@10    | 0.1703     |
| cosine_ndcg@10      | 0.0933     |
| cosine_mrr@10       | 0.07       |
| **cosine_map@100**  | **0.0837** |

<!--
## 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

#### Unnamed Dataset


* Size: 4,173 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: 10 tokens</li><li>mean: 67.49 tokens</li><li>max: 214 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 28.0 tokens</li><li>max: 61 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                             | anchor                                                                                         |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
  | <code>Havent-se d'acreditar la matriculació i inscripció en el respectiu centre públic o concertat, així com el cost de les llars d'infants, de l'educació especialitzada per les discapacitats físiques, psíquiques i sensorials en centres públics, concertats o privats.</code>                                                                                                                                                   | <code>Quin és el requisit per acreditar la llar d'infants?</code>                              |
  | <code>El volant històric de convivència és el document que informa de la residencia en el municipi de Sitges, així com altres fets relatius a l'empadronament d'una persona, i detalla tots els domicilis, la data inicial i final en els que ha estat empadronada en cadascun d'ells, i les persones amb les què constava inscrites, segons les dades que consten al Padró Municipal d'Habitants fins a la data d'expedició.</code> | <code>Quin és el propòsit del volant històric de convivència?</code>                           |
  | <code>Instal·lació de tanques sense obra.</code>                                                                                                                                                                                                                                                                                                                                                                                     | <code>Quins són els exemples d'instal·lacions que es poden comunicar amb aquest tràmit?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          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
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: False
- `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
- `learning_rate`: 5e-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`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | 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 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6130     | 10     | 11.3695       | -                      | -                      | -                      | -                     | -                      |
| 0.9808     | 16     | -             | 0.0214                 | 0.0243                 | 0.0234                 | 0.0199                | 0.0234                 |
| 1.2261     | 20     | 10.653        | -                      | -                      | -                      | -                     | -                      |
| 1.8391     | 30     | 9.0745        | -                      | -                      | -                      | -                     | -                      |
| 1.9617     | 32     | -             | 0.0495                 | 0.0517                 | 0.0589                 | 0.0481                | 0.0589                 |
| 2.4521     | 40     | 7.3468        | -                      | -                      | -                      | -                     | -                      |
| 2.9425     | 48     | -             | 0.0764                 | 0.0734                 | 0.0811                 | 0.0709                | 0.0811                 |
| 3.0651     | 50     | 5.887         | -                      | -                      | -                      | -                     | -                      |
| 3.6782     | 60     | 5.3568        | -                      | -                      | -                      | -                     | -                      |
| **3.9847** | **65** | **-**         | **0.0922**             | **0.0857**             | **0.0896**             | **0.0808**            | **0.0896**             |
| 4.2912     | 70     | 4.8338        | -                      | -                      | -                      | -                     | -                      |
| 4.9042     | 80     | 4.9251        | 0.0899                 | 0.0899                 | 0.0906                 | 0.0837                | 0.0906                 |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- 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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