---
base_model: BAAI/bge-m3
datasets: []
language:
- ca
library_name: sentence-transformers
license: apache-2.0
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:3755
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: En el cas que la persona beneficiària mantingui les condicions
    d’elegibilitat es podrà concedir la pròrroga de la prestació sempre que la persona
    interessada ho sol·liciti i ho permetin les dotacions pressupostàries de cada
    exercici.
  sentences:
  - Quin és el benefici de l'ajut a la consolidació d'empreses?
  - Quin és el requisit per a la persona beneficiària?
  - Quin és el benefici del Registre municipal d'entitats per a l'Ajuntament?
- source_sentence: Aquest tràmit permet la presentació de les sol·licituds per a l’atorgament
    de llicències d’aprofitament especial sense transformació del domini públic marítim
    terrestre consistent en la instal·lació i explotació d'escola per oferir activitats
    nàutiques, amb zona d’avarada, durant la temporada.
  sentences:
  - Quin és el propòsit de la llicència d'aprofitament especial sense transformació
    del domini públic marítim terrestre?
  - Quin és el termini per a presentar les sol·licituds de subvencions per a projectes
    i activitats a entitats de l'àmbit de drets civils?
  - Quin és el lloc on es realitzen les activitats amb aquest permís?
- source_sentence: en cas de compliment dels requisits establerts (persones residents,
    titulars de plaça d'aparcament, autotaxis, establiments hotelers)
  sentences:
  - Quin és el paper de l'administració en la justificació del projecte/activitat
    subvencionada?
  - Quin és el benefici de ser un autotaxi?
  - Quin és el benefici per als establiments de la instal·lació de terrasses o vetlladors?
- source_sentence: La convocatòria és el document que estableix les condicions i els
    requisits per a poder sol·licitar les subvencions pel suport educatiu a les escoles
    públiques de Sitges.
  sentences:
  - Quin és el paper de la convocatòria en les subvencions pel suport educatiu a les
    escoles públiques de Sitges?
  - Quin és el benefici de la consulta prèvia de classificació d'activitat per a l'Ajuntament
    de Sitges?
  - Quin és el tipus d'ocupació de la via pública que es pot realitzar amb aquest
    permís?
- source_sentence: Cal revisar la informació i els terminis de la convocatòria específica
    de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.
  sentences:
  - Quin és el document que es necessita per acreditar l'any de construcció i l'adequació
    a la legalitat urbanística d'un immoble?
  - Quin és el paper de l'Ajuntament en la gestió de les activitats per temporades?
  - On es pot trobar la informació sobre els terminis de presentació d'al·legacions
    en un procés de selecció de personal de l'Ajuntament de Sitges?
model-index:
- name: BGE SITGES  CAT
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 1024
      type: dim_1024
    metrics:
    - type: cosine_accuracy@1
      value: 0.13875598086124402
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22248803827751196
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30861244019138756
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13875598086124402
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07416267942583732
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06172248803827752
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049999999999999996
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13875598086124402
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22248803827751196
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30861244019138756
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.28246378665685234
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21777644869750143
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24297774164515282
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.13157894736842105
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.22248803827751196
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3157894736842105
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4904306220095694
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13157894736842105
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07416267942583732
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06315789473684211
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04904306220095694
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13157894736842105
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.22248803827751196
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3157894736842105
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4904306220095694
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.27585932698577753
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21171489329384077
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23780085464747025
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.13875598086124402
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21770334928229665
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3062200956937799
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.48564593301435405
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.13875598086124402
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07256778309409888
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06124401913875598
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0485645933014354
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.13875598086124402
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21770334928229665
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3062200956937799
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.48564593301435405
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.276564299219231
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.21426198070934924
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24076362333582052
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.12440191387559808
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21770334928229665
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3133971291866029
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4688995215311005
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12440191387559808
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07256778309409888
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06267942583732058
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04688995215311005
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12440191387559808
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21770334928229665
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3133971291866029
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4688995215311005
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2671493494247117
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20640996430470124
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23431223249888664
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.12200956937799043
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21291866028708134
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3014354066985646
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.49282296650717705
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.12200956937799043
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07097288676236044
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06028708133971292
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.049282296650717705
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12200956937799043
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21291866028708134
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3014354066985646
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.49282296650717705
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.27152939051256636
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.20549764562922473
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.23082152106975815
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.11961722488038277
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.19856459330143542
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.2822966507177033
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4688995215311005
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.11961722488038277
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.06618819776714513
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.056459330143540674
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.046889952153110044
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.11961722488038277
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.19856459330143542
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2822966507177033
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4688995215311005
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2582882544405147
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19569188121819714
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22122525098210105
      name: Cosine Map@100
---

# BGE SITGES  CAT

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:** ca
- **License:** apache-2.0

### 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/SITGES-BAAI2")
# Run inference
sentences = [
    "Cal revisar la informació i els terminis de la convocatòria específica de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.",
    "On es pot trobar la informació sobre els terminis de presentació d'al·legacions en un procés de selecció de personal de l'Ajuntament de Sitges?",
    "Quin és el document que es necessita per acreditar l'any de construcció i l'adequació a la legalitat urbanística d'un immoble?",
]
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   | 0.1388    |
| cosine_accuracy@3   | 0.2225    |
| cosine_accuracy@5   | 0.3086    |
| cosine_accuracy@10  | 0.5       |
| cosine_precision@1  | 0.1388    |
| cosine_precision@3  | 0.0742    |
| cosine_precision@5  | 0.0617    |
| cosine_precision@10 | 0.05      |
| cosine_recall@1     | 0.1388    |
| cosine_recall@3     | 0.2225    |
| cosine_recall@5     | 0.3086    |
| cosine_recall@10    | 0.5       |
| cosine_ndcg@10      | 0.2825    |
| cosine_mrr@10       | 0.2178    |
| **cosine_map@100**  | **0.243** |

#### 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   | 0.1316     |
| cosine_accuracy@3   | 0.2225     |
| cosine_accuracy@5   | 0.3158     |
| cosine_accuracy@10  | 0.4904     |
| cosine_precision@1  | 0.1316     |
| cosine_precision@3  | 0.0742     |
| cosine_precision@5  | 0.0632     |
| cosine_precision@10 | 0.049      |
| cosine_recall@1     | 0.1316     |
| cosine_recall@3     | 0.2225     |
| cosine_recall@5     | 0.3158     |
| cosine_recall@10    | 0.4904     |
| cosine_ndcg@10      | 0.2759     |
| cosine_mrr@10       | 0.2117     |
| **cosine_map@100**  | **0.2378** |

#### 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   | 0.1388     |
| cosine_accuracy@3   | 0.2177     |
| cosine_accuracy@5   | 0.3062     |
| cosine_accuracy@10  | 0.4856     |
| cosine_precision@1  | 0.1388     |
| cosine_precision@3  | 0.0726     |
| cosine_precision@5  | 0.0612     |
| cosine_precision@10 | 0.0486     |
| cosine_recall@1     | 0.1388     |
| cosine_recall@3     | 0.2177     |
| cosine_recall@5     | 0.3062     |
| cosine_recall@10    | 0.4856     |
| cosine_ndcg@10      | 0.2766     |
| cosine_mrr@10       | 0.2143     |
| **cosine_map@100**  | **0.2408** |

#### 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   | 0.1244     |
| cosine_accuracy@3   | 0.2177     |
| cosine_accuracy@5   | 0.3134     |
| cosine_accuracy@10  | 0.4689     |
| cosine_precision@1  | 0.1244     |
| cosine_precision@3  | 0.0726     |
| cosine_precision@5  | 0.0627     |
| cosine_precision@10 | 0.0469     |
| cosine_recall@1     | 0.1244     |
| cosine_recall@3     | 0.2177     |
| cosine_recall@5     | 0.3134     |
| cosine_recall@10    | 0.4689     |
| cosine_ndcg@10      | 0.2671     |
| cosine_mrr@10       | 0.2064     |
| **cosine_map@100**  | **0.2343** |

#### 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   | 0.122      |
| cosine_accuracy@3   | 0.2129     |
| cosine_accuracy@5   | 0.3014     |
| cosine_accuracy@10  | 0.4928     |
| cosine_precision@1  | 0.122      |
| cosine_precision@3  | 0.071      |
| cosine_precision@5  | 0.0603     |
| cosine_precision@10 | 0.0493     |
| cosine_recall@1     | 0.122      |
| cosine_recall@3     | 0.2129     |
| cosine_recall@5     | 0.3014     |
| cosine_recall@10    | 0.4928     |
| cosine_ndcg@10      | 0.2715     |
| cosine_mrr@10       | 0.2055     |
| **cosine_map@100**  | **0.2308** |

#### 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   | 0.1196     |
| cosine_accuracy@3   | 0.1986     |
| cosine_accuracy@5   | 0.2823     |
| cosine_accuracy@10  | 0.4689     |
| cosine_precision@1  | 0.1196     |
| cosine_precision@3  | 0.0662     |
| cosine_precision@5  | 0.0565     |
| cosine_precision@10 | 0.0469     |
| cosine_recall@1     | 0.1196     |
| cosine_recall@3     | 0.1986     |
| cosine_recall@5     | 0.2823     |
| cosine_recall@10    | 0.4689     |
| cosine_ndcg@10      | 0.2583     |
| cosine_mrr@10       | 0.1957     |
| **cosine_map@100**  | **0.2212** |

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## Bias, Risks and Limitations

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### Recommendations

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

### Training 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`: 6
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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
- `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`: 6
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step   | Training Loss | 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 |
|:----------:|:------:|:-------------:|:---------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3404     | 5      | 3.3256        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.6809     | 10     | 2.2115        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 0.9532     | 14     | -             | 1.2963    | 0.2260                  | 0.2148                 | 0.2144                 | 0.2258                 | 0.2069                | 0.2252                 |
| 1.0213     | 15     | 1.7921        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.3617     | 20     | 1.2295        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.7021     | 25     | 0.9048        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 1.9745     | 29     | -             | 0.8667    | 0.2311                  | 0.2267                 | 0.2292                 | 0.2279                 | 0.2121                | 0.2278                 |
| 2.0426     | 30     | 0.7256        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.3830     | 35     | 0.5252        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.7234     | 40     | 0.4648        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 2.9957     | 44     | -             | 0.6920    | 0.2311                  | 0.2243                 | 0.2332                 | 0.2319                 | 0.2211                | 0.2354                 |
| 3.0638     | 45     | 0.3518        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.4043     | 50     | 0.321         | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.7447     | 55     | 0.2923        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 3.9489     | 58     | -             | 0.6514    | 0.2343                  | 0.2210                 | 0.2293                 | 0.2338                 | 0.2242                | 0.2331                 |
| 4.0851     | 60     | 0.2522        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.4255     | 65     | 0.2445        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.7660     | 70     | 0.2358        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 4.9702     | 73     | -             | 0.6481    | 0.2348                  | 0.2239                 | 0.2252                 | 0.2332                 | 0.2167                | 0.2298                 |
| 5.1064     | 75     | 0.2301        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| 5.4468     | 80     | 0.2262        | -         | -                       | -                      | -                      | -                      | -                     | -                      |
| **5.7191** | **84** | **-**         | **0.646** | **0.243**               | **0.2308**             | **0.2343**             | **0.2408**             | **0.2212**            | **0.2378**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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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