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---
base_model: BAAI/bge-m3
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9717
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Per accedir a un habitatge amb protecció oficial al municipi de
Sitges s'ha d'estar inscrit en el Registre municipal de sol·licitants.
sentences:
- Quin és el motiu perquè la renovació de la inscripció en el Registre municipal
de sol·licitants d'habitatge amb protecció oficial de Sitges és necessària?
- Quin és el sector que es veu afectat per la disminució d'ingressos?
- Quin és el propòsit de la descripció de l'activitat?
- source_sentence: Aquest tràmit permet presentar ofertes i/o pressupostos sol·licitats
per l'Ajuntament de Sitges en procediments de contractes menors.
sentences:
- Quin és el requisit per a sol·licitar l'ajut econòmic a l'Ajuntament de Sitges?
- Què passa amb la llicència de gual quan es vol reduir les característiques físiques?
- Quin és el propòsit del tràmit de presentació d'ofertes?
- source_sentence: Estudis universitaris fins al grau de llicenciatura
sentences:
- Quin és el propòsit de la subvenció per a les persones autònomes?
- Quin és el requisit per als establiments oberts al públic destinats a espectacles
públics i activitats recreatives musicals?
- Quins estudis universitaris es poden fer amb aquesta ajuda?
- source_sentence: Les entitats especialitzades i acreditades com a proveïdores de
la Xarxa de Serveis Socials d'Atenció Pública interesades en la la gestió delegada
dels serveis públics de l'Ajuntament de Sitges així determinats, poden presentar-se
a les respectives convocatòries per a l'adjudicació.
sentences:
- On comencen i acaben les activitats de l'Estiu Jove?
- Quin és el benefici per a l'Ajuntament de Sitges de la gestió delegada?
- Quin és el paper de l’organització en la valoració d'una proposta?
- source_sentence: Publicada la llista d'infants admesos i exclosos a les estades
esportives, s'obre un termini perquè les persones admeses puguin demanar qualsevol
canvi a la sol·licitud inicial.
sentences:
- Quin és el contingut del volant històric de convivència?
- Quin és el període en què es pot demanar un canvi a la sol·licitud inicial?
- Quin és el paper de les escoles de Sitges en les activitats de foment de l'esport
escolar
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: 0.10126582278481013
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.18565400843881857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24472573839662448
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.34177215189873417
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10126582278481013
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.061884669479606184
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0489451476793249
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03417721518987342
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10126582278481013
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.18565400843881857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24472573839662448
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.34177215189873417
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20497940546236365
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1631588641082312
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18190274772574827
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.0970464135021097
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.18143459915611815
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2616033755274262
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.34177215189873417
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0970464135021097
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06047819971870604
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.052320675105485236
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.034177215189873406
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0970464135021097
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.18143459915611815
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2616033755274262
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.34177215189873417
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20447797235629017
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16207219878105952
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18114215201809386
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.08438818565400844
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.17721518987341772
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.23628691983122363
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.34177215189873417
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08438818565400844
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05907172995780591
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04725738396624472
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03417721518987342
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08438818565400844
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.17721518987341772
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.23628691983122363
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.34177215189873417
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19477348596574798
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.15014232134485297
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16826302734813764
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.0759493670886076
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.16877637130801687
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.23628691983122363
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.34177215189873417
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0759493670886076
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05625879043600562
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04725738396624473
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.034177215189873406
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0759493670886076
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16877637130801687
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.23628691983122363
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.34177215189873417
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18887676996048183
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14248208425423614
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15960797563687307
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.08016877637130802
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.16455696202531644
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2320675105485232
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.32489451476793246
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08016877637130802
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05485232067510549
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.046413502109704644
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.032489451476793246
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08016877637130802
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16455696202531644
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2320675105485232
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.32489451476793246
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18920967116655296
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14736454356707523
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1622413863660417
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.046413502109704644
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.1518987341772152
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21940928270042195
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.270042194092827
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.046413502109704644
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.050632911392405056
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04388185654008439
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0270042194092827
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.046413502109704644
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1518987341772152
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21940928270042195
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.270042194092827
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15109586098353134
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.11371308016877635
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12600329900444687
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/sitges-v2-5ep")
# Run inference
sentences = [
"Publicada la llista d'infants admesos i exclosos a les estades esportives, s'obre un termini perquè les persones admeses puguin demanar qualsevol canvi a la sol·licitud inicial.",
'Quin és el període en què es pot demanar un canvi a la sol·licitud inicial?',
'Quin és el contingut del volant històric de convivència?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## 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 | 0.1013 |
| cosine_accuracy@3 | 0.1857 |
| cosine_accuracy@5 | 0.2447 |
| cosine_accuracy@10 | 0.3418 |
| cosine_precision@1 | 0.1013 |
| cosine_precision@3 | 0.0619 |
| cosine_precision@5 | 0.0489 |
| cosine_precision@10 | 0.0342 |
| cosine_recall@1 | 0.1013 |
| cosine_recall@3 | 0.1857 |
| cosine_recall@5 | 0.2447 |
| cosine_recall@10 | 0.3418 |
| cosine_ndcg@10 | 0.205 |
| cosine_mrr@10 | 0.1632 |
| **cosine_map@100** | **0.1819** |
#### 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 | 0.097 |
| cosine_accuracy@3 | 0.1814 |
| cosine_accuracy@5 | 0.2616 |
| cosine_accuracy@10 | 0.3418 |
| cosine_precision@1 | 0.097 |
| cosine_precision@3 | 0.0605 |
| cosine_precision@5 | 0.0523 |
| cosine_precision@10 | 0.0342 |
| cosine_recall@1 | 0.097 |
| cosine_recall@3 | 0.1814 |
| cosine_recall@5 | 0.2616 |
| cosine_recall@10 | 0.3418 |
| cosine_ndcg@10 | 0.2045 |
| cosine_mrr@10 | 0.1621 |
| **cosine_map@100** | **0.1811** |
#### 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 | 0.0844 |
| cosine_accuracy@3 | 0.1772 |
| cosine_accuracy@5 | 0.2363 |
| cosine_accuracy@10 | 0.3418 |
| cosine_precision@1 | 0.0844 |
| cosine_precision@3 | 0.0591 |
| cosine_precision@5 | 0.0473 |
| cosine_precision@10 | 0.0342 |
| cosine_recall@1 | 0.0844 |
| cosine_recall@3 | 0.1772 |
| cosine_recall@5 | 0.2363 |
| cosine_recall@10 | 0.3418 |
| cosine_ndcg@10 | 0.1948 |
| cosine_mrr@10 | 0.1501 |
| **cosine_map@100** | **0.1683** |
#### 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 | 0.0759 |
| cosine_accuracy@3 | 0.1688 |
| cosine_accuracy@5 | 0.2363 |
| cosine_accuracy@10 | 0.3418 |
| cosine_precision@1 | 0.0759 |
| cosine_precision@3 | 0.0563 |
| cosine_precision@5 | 0.0473 |
| cosine_precision@10 | 0.0342 |
| cosine_recall@1 | 0.0759 |
| cosine_recall@3 | 0.1688 |
| cosine_recall@5 | 0.2363 |
| cosine_recall@10 | 0.3418 |
| cosine_ndcg@10 | 0.1889 |
| cosine_mrr@10 | 0.1425 |
| **cosine_map@100** | **0.1596** |
#### 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 | 0.0802 |
| cosine_accuracy@3 | 0.1646 |
| cosine_accuracy@5 | 0.2321 |
| cosine_accuracy@10 | 0.3249 |
| cosine_precision@1 | 0.0802 |
| cosine_precision@3 | 0.0549 |
| cosine_precision@5 | 0.0464 |
| cosine_precision@10 | 0.0325 |
| cosine_recall@1 | 0.0802 |
| cosine_recall@3 | 0.1646 |
| cosine_recall@5 | 0.2321 |
| cosine_recall@10 | 0.3249 |
| cosine_ndcg@10 | 0.1892 |
| cosine_mrr@10 | 0.1474 |
| **cosine_map@100** | **0.1622** |
#### 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 | 0.0464 |
| cosine_accuracy@3 | 0.1519 |
| cosine_accuracy@5 | 0.2194 |
| cosine_accuracy@10 | 0.27 |
| cosine_precision@1 | 0.0464 |
| cosine_precision@3 | 0.0506 |
| cosine_precision@5 | 0.0439 |
| cosine_precision@10 | 0.027 |
| cosine_recall@1 | 0.0464 |
| cosine_recall@3 | 0.1519 |
| cosine_recall@5 | 0.2194 |
| cosine_recall@10 | 0.27 |
| cosine_ndcg@10 | 0.1511 |
| cosine_mrr@10 | 0.1137 |
| **cosine_map@100** | **0.126** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 9,717 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: 8 tokens</li><li>mean: 49.79 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.83 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el requisit per a obtenir les subvencions per a projectes i activitats esportives?</code> |
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges al llarg de l'exercici per la qual es sol·licita la subvenció, i reuneixin les condicions assenyalades a les bases.</code> | <code>Quin és el requisit per a obtenir les subvencions per a projectes i activitats esportives?</code> |
| <code>No es proporciona informació sobre el requisit principal per obtenir el certificat.</code> | <code>Quin és el requisit principal per obtenir el certificat?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_1024_cosine_map@100 | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2632 | 10 | 3.2527 | - | - | - | - | - | - |
| 0.5263 | 20 | 1.9679 | - | - | - | - | - | - |
| 0.7895 | 30 | 1.8319 | - | - | - | - | - | - |
| **1.0** | **38** | **-** | **0.1819** | **0.1622** | **0.1596** | **0.1683** | **0.126** | **0.1811** |
| 1.0526 | 40 | 1.3358 | - | - | - | - | - | - |
| 1.3158 | 50 | 1.1166 | - | - | - | - | - | - |
| 1.5789 | 60 | 0.8715 | - | - | - | - | - | - |
| 1.8421 | 70 | 0.8801 | - | - | - | - | - | - |
| 2.0 | 76 | - | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 |
| 2.1053 | 80 | 0.6515 | - | - | - | - | - | - |
| 2.3684 | 90 | 0.536 | - | - | - | - | - | - |
| 2.6316 | 100 | 0.4682 | - | - | - | - | - | - |
| 2.8947 | 110 | 0.4686 | - | - | - | - | - | - |
| 3.0 | 114 | - | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 |
| 3.1579 | 120 | 0.3161 | - | - | - | - | - | - |
| 3.4211 | 130 | 0.3554 | - | - | - | - | - | - |
| 3.6842 | 140 | 0.2886 | - | - | - | - | - | - |
| 3.9474 | 150 | 0.2616 | - | - | - | - | - | - |
| 4.0 | 152 | - | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 |
| 4.2105 | 160 | 0.1902 | - | - | - | - | - | - |
| 4.4737 | 170 | 0.1894 | - | - | - | - | - | - |
| 4.7368 | 180 | 0.1858 | - | - | - | - | - | - |
| 5.0 | 190 | 0.1939 | 0.1819 | 0.1622 | 0.1596 | 0.1683 | 0.1260 | 0.1811 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 3.0.1
- 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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