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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:6692
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: La inscripció en aquest registre caduca en el termini d'un any,
llevat que sigui renovada abans del transcurs d'aquest termini mitjançant la presentació
d'una declaració responsable sobre el compliment dels requisits exigits.
sentences:
- Quin és el requisit per a la sol·licitud del volant d'empadronament?
- Què passa si no es renova la inscripció en el Registre municipal de sol·licitants?
- Quin és el segon objectiu que han de tenir els projectes/activitats per a rebre
aquesta subvenció?
- source_sentence: 'AVÍS: Places exhaurides de l''activitat de psicomotricitat fins
nou avís. Les persones interessades poden contactar amb el Departament d''Esports,
el qual obrirà un llistat d''espera, si escau.'
sentences:
- Què passa si les places de Psicomotricitat estan exhaurides?
- Quin és el paper del tractament en la declaració?
- Quin és el període de temps que es requereix per a la venda d'articles d'artesania?
- source_sentence: El registre de noves patents en relació a les noves línies d’actuació
és una despesa subvencionable per a la reactivació i adaptació del negoci post
COVID19.
sentences:
- Quins són els tipus de despeses que es poden finançar amb les subvencions?
- Quin és el paper de les organitzacions membres del Consell de Cooperació en els
projectes de cooperació internacional?
- Quin és el propòsit del registre de noves patents en relació a les noves línies
d’actuació?
- source_sentence: 'Justificació de les subvencions atorgades per l''Ajuntament de
Sitges per les activitats culturals incloses dins els següents tipus: Activitats
de difusió cultural. Iniciatives de recuperació i difusió del patrimoni cultural,
tradicional i popular. Activitats de formació no reglada i de recerca. Activitats
d''animació socio-cultural.'
sentences:
- Quins són els residus que es recullen en el servei municipal complementari?
- Quin és el paper de l'expedient d'ajut a la contractació laboral de persones en
la contractació laboral?
- Quin és el paper de les activitats d'animació socio-cultural?
- source_sentence: La comunicació és un element important en la cura dels gats, ja
que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats
competents i amb els altres implicats en la cura dels animals.
sentences:
- Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments
oberts al públic i les activitats recreatives?
- Quin és el paper de la comunicació en la cura dels gats?
- Quin és el benefici de la llicència de gual per a la persona titular?
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: 0.1589958158995816
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.303347280334728
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3723849372384937
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5188284518828452
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1589958158995816
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.101115760111576
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07447698744769873
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05188284518828451
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1589958158995816
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.303347280334728
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3723849372384937
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5188284518828452
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31740141154907076
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2560196254233912
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27634436521904066
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.15690376569037656
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.29707112970711297
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3807531380753138
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5083682008368201
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.15690376569037656
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09902370990237098
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07615062761506276
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.050836820083682004
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15690376569037656
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.29707112970711297
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3807531380753138
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5083682008368201
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3138709871801379
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.25412432755528996
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27566053318396105
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.17364016736401675
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.3138075313807531
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.39539748953974896
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5376569037656904
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.17364016736401675
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.10460251046025104
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07907949790794978
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05376569037656903
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.17364016736401675
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3138075313807531
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.39539748953974896
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5376569037656904
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.33244445391299926
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2700023245002324
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.29010151423672403
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.1506276150627615
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2907949790794979
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.401673640167364
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5355648535564853
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1506276150627615
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09693165969316596
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0803347280334728
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05355648535564853
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1506276150627615
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2907949790794979
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.401673640167364
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5355648535564853
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3189819772344188
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.25269392973367877
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2728848917988661
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.16736401673640167
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.3200836820083682
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.41631799163179917
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5481171548117155
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16736401673640167
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.10669456066945607
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08326359832635982
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05481171548117154
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16736401673640167
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3200836820083682
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.41631799163179917
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5481171548117155
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3353691502747181
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.26997077771136346
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2891803614784421
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.15481171548117154
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28451882845188287
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3514644351464435
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5209205020920502
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.15481171548117154
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09483960948396093
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07029288702928871
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.052092050209205015
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15481171548117154
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28451882845188287
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3514644351464435
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5209205020920502
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3116868900381799
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2481885501759978
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2685744617473963
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/ST-tramits-SITGES-007-5ep")
# Run inference
sentences = [
'La comunicació és un element important en la cura dels gats, ja que implica la capacitat per a comunicar-se de manera efectiva amb les autoritats competents i amb els altres implicats en la cura dels animals.',
'Quin és el paper de la comunicació en la cura dels gats?',
'Qui són considerats titulars o nous exercents en el cas dels espectacles, establiments oberts al públic i les activitats recreatives?',
]
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 | 0.159 |
| cosine_accuracy@3 | 0.3033 |
| cosine_accuracy@5 | 0.3724 |
| cosine_accuracy@10 | 0.5188 |
| cosine_precision@1 | 0.159 |
| cosine_precision@3 | 0.1011 |
| cosine_precision@5 | 0.0745 |
| cosine_precision@10 | 0.0519 |
| cosine_recall@1 | 0.159 |
| cosine_recall@3 | 0.3033 |
| cosine_recall@5 | 0.3724 |
| cosine_recall@10 | 0.5188 |
| cosine_ndcg@10 | 0.3174 |
| cosine_mrr@10 | 0.256 |
| **cosine_map@100** | **0.2763** |
#### 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 | 0.1569 |
| cosine_accuracy@3 | 0.2971 |
| cosine_accuracy@5 | 0.3808 |
| cosine_accuracy@10 | 0.5084 |
| cosine_precision@1 | 0.1569 |
| cosine_precision@3 | 0.099 |
| cosine_precision@5 | 0.0762 |
| cosine_precision@10 | 0.0508 |
| cosine_recall@1 | 0.1569 |
| cosine_recall@3 | 0.2971 |
| cosine_recall@5 | 0.3808 |
| cosine_recall@10 | 0.5084 |
| cosine_ndcg@10 | 0.3139 |
| cosine_mrr@10 | 0.2541 |
| **cosine_map@100** | **0.2757** |
#### 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 | 0.1736 |
| cosine_accuracy@3 | 0.3138 |
| cosine_accuracy@5 | 0.3954 |
| cosine_accuracy@10 | 0.5377 |
| cosine_precision@1 | 0.1736 |
| cosine_precision@3 | 0.1046 |
| cosine_precision@5 | 0.0791 |
| cosine_precision@10 | 0.0538 |
| cosine_recall@1 | 0.1736 |
| cosine_recall@3 | 0.3138 |
| cosine_recall@5 | 0.3954 |
| cosine_recall@10 | 0.5377 |
| cosine_ndcg@10 | 0.3324 |
| cosine_mrr@10 | 0.27 |
| **cosine_map@100** | **0.2901** |
#### 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 | 0.1506 |
| cosine_accuracy@3 | 0.2908 |
| cosine_accuracy@5 | 0.4017 |
| cosine_accuracy@10 | 0.5356 |
| cosine_precision@1 | 0.1506 |
| cosine_precision@3 | 0.0969 |
| cosine_precision@5 | 0.0803 |
| cosine_precision@10 | 0.0536 |
| cosine_recall@1 | 0.1506 |
| cosine_recall@3 | 0.2908 |
| cosine_recall@5 | 0.4017 |
| cosine_recall@10 | 0.5356 |
| cosine_ndcg@10 | 0.319 |
| cosine_mrr@10 | 0.2527 |
| **cosine_map@100** | **0.2729** |
#### 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 | 0.1674 |
| cosine_accuracy@3 | 0.3201 |
| cosine_accuracy@5 | 0.4163 |
| cosine_accuracy@10 | 0.5481 |
| cosine_precision@1 | 0.1674 |
| cosine_precision@3 | 0.1067 |
| cosine_precision@5 | 0.0833 |
| cosine_precision@10 | 0.0548 |
| cosine_recall@1 | 0.1674 |
| cosine_recall@3 | 0.3201 |
| cosine_recall@5 | 0.4163 |
| cosine_recall@10 | 0.5481 |
| cosine_ndcg@10 | 0.3354 |
| cosine_mrr@10 | 0.27 |
| **cosine_map@100** | **0.2892** |
#### 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 | 0.1548 |
| cosine_accuracy@3 | 0.2845 |
| cosine_accuracy@5 | 0.3515 |
| cosine_accuracy@10 | 0.5209 |
| cosine_precision@1 | 0.1548 |
| cosine_precision@3 | 0.0948 |
| cosine_precision@5 | 0.0703 |
| cosine_precision@10 | 0.0521 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.2845 |
| cosine_recall@5 | 0.3515 |
| cosine_recall@10 | 0.5209 |
| cosine_ndcg@10 | 0.3117 |
| cosine_mrr@10 | 0.2482 |
| **cosine_map@100** | **0.2686** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,692 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 44.83 tokens</li><li>max: 185 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.89 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|
| <code>Els residus comercials o industrials assimilables als municipals que hauran d'acreditar si disposen d'un gestor autoritzat per a la gestió dels residus.</code> | <code>Quins són els residus que es recullen en el servei municipal complementari?</code> |
| <code>L'Ajuntament de Sitges ofereix ajuts econòmics a famílies amb recursos insuficients per accedir a la realització d'activitats de lleure...</code> | <code>Quin és el paper de l'Ajuntament de Sitges en la promoció de l'educació no formal i de lleure?</code> |
| <code>Permet comunicar les intervencions necessàries per executar una instal·lació/remodelació d’autoconsum amb energia solar fotovoltaica amb una potència instal·lada inferior a 100 kWp en sòl urbà consolidat.</code> | <code>Quin és el propòsit de la remodelació d'una instal·lació d'autoconsum?</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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3819 | 10 | 3.3449 | - | - | - | - | - | - |
| 0.7637 | 20 | 2.0557 | - | - | - | - | - | - |
| 0.9928 | 26 | - | 0.2440 | 0.2408 | 0.2590 | 0.2439 | 0.2379 | 0.2512 |
| 1.1456 | 30 | 1.4634 | - | - | - | - | - | - |
| 1.5274 | 40 | 0.8163 | - | - | - | - | - | - |
| 1.9093 | 50 | 0.6103 | - | - | - | - | - | - |
| 1.9857 | 52 | - | 0.2621 | 0.2683 | 0.2483 | 0.2629 | 0.2404 | 0.2472 |
| 2.2912 | 60 | 0.4854 | - | - | - | - | - | - |
| 2.6730 | 70 | 0.2796 | - | - | - | - | - | - |
| 2.9785 | 78 | - | 0.2701 | 0.2697 | 0.2761 | 0.2845 | 0.2673 | 0.2709 |
| 3.0549 | 80 | 0.2458 | - | - | - | - | - | - |
| 3.4368 | 90 | 0.2616 | - | - | - | - | - | - |
| 3.8186 | 100 | 0.174 | - | - | - | - | - | - |
| 3.9714 | 104 | - | 0.2729 | 0.2863 | 0.2858 | 0.2853 | 0.2656 | 0.2752 |
| 4.2005 | 110 | 0.1841 | - | - | - | - | - | - |
| 4.5823 | 120 | 0.1668 | - | - | - | - | - | - |
| **4.9642** | **130** | **0.1484** | **0.2763** | **0.2892** | **0.2729** | **0.2901** | **0.2686** | **0.2757** |
* 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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