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---
base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9593
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Aquestes parades estaran ocupades per empreses del sector, entitats
socials i culturals i centres escolars amb seu a Sitges, o empreses del sector
amb activitat a Sitges, que prèviament han fet la sol·licitud, se'ls ha autoritzat
i, si escau, han abonat la taxa corresponent.
sentences:
- Quin és el paper de les petites empreses i persones autònomes en aquests ajuts?
- Quin és el tràmit que permet sol·licitar una nova placa de gual?
- Quin és el requisit per a l'ocupació de les parades de la Fira de Sant Jordi?
- source_sentence: L'Ajuntament de Sitges atorga subvencions pels projectes educatius
que realitzen les escoles de Sitges que tinguin com a finalitat augmentar la qualitat
educativa dels infants d'infantil i primària al llarg de l’exercici pel qual es
sol·licita la subvenció.
sentences:
- Quin és el paper de la targeta 'smart Sitges' en la gestió de residus?
- Quin és el requisit per rebre ajuts econòmics per la meva empresa en dificultats
econòmiques?
- Quin és el resultat esperat de les subvencions per a les escoles?
- source_sentence: ocupades per empreses del sector i entitats culturals, amb activitat
editorial acreditada
sentences:
- Quin és el percentatge de bonificació per als carrers i locals afectats indirectament?
- Quin és el propòsit de presentar documents en un procés de selecció de personal
de l'Ajuntament de Sitges?
- Quin és el lloc on es troben les empreses del sector que participen en la Fira
de la Vila del Llibre de Sitges?
- source_sentence: Aquest tràmit permet a les persones interessades la presentació
d'al·legacions i/o la interposició de recursos contra actes administratius dictats
per l'Ajuntament de Sitges.
sentences:
- Quin és el tràmit per presentar una al·legació contra una decisió de l'Ajuntament
de Sitges?
- Quin és el benefici de la llicència per a obres a la via pública
- Com puc promoure l'esport a la ciutat?
- source_sentence: 'Per valorar l’interès de la proposta es tindrà en compte: Tipus
d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme
des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana.'
sentences:
- Quin és el benefici de la realització d'exposicions al Centre Cultural Miramar?
- Quin és el paper de les accions de promoció en les subvencions per a projectes
i activitats de l'àmbit turístic?
- Quins són els productes que es venen al Mercat setmanal dels dijous?
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: 0.05909943714821764
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.1275797373358349
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.17354596622889307
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.2861163227016886
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05909943714821764
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04252657911194496
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03470919324577861
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.028611632270168854
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05909943714821764
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1275797373358349
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17354596622889307
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2861163227016886
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1537318058278305
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.11394435510289168
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1397865116884934
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.05909943714821764
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.12570356472795496
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.1801125703564728
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.2945590994371482
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05909943714821764
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04190118824265165
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.036022514071294566
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02945590994371482
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05909943714821764
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12570356472795496
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1801125703564728
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2945590994371482
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15635010592942117
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1149472140325799
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.14049204491324296
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.05909943714821764
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.12570356472795496
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.17073170731707318
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.29831144465290804
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05909943714821764
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04190118824265165
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03414634146341463
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.029831144465290803
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05909943714821764
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12570356472795496
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17073170731707318
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.29831144465290804
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1571277123670345
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1149557759313857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1397328880376811
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.051594746716697934
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.12101313320825516
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.16791744840525327
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.28893058161350843
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.051594746716697934
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.040337711069418386
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03358348968105066
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.028893058161350845
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.051594746716697934
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12101313320825516
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16791744840525327
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.28893058161350843
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14978486884903933
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1081955984395009
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.13375931969408872
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.051594746716697934
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.11726078799249531
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.17166979362101314
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.28893058161350843
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.051594746716697934
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.039086929330831764
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.034333958724202626
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.028893058161350845
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.051594746716697934
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11726078799249531
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17166979362101314
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.28893058161350843
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14877654954358344
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1068536138658091
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.13283061923015374
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.05065666041275797
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.1125703564727955
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.16416510318949343
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.28236397748592873
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05065666041275797
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0375234521575985
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03283302063789869
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02823639774859287
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05065666041275797
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1125703564727955
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16416510318949343
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.28236397748592873
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14493487779487546
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10395931981297837
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1306497575595095
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/sitgrsBAAIbge-m3-300824")
# Run inference
sentences = [
'Per valorar l’interès de la proposta es tindrà en compte: Tipus d’activitat Antecedents Dates de celebració Accions de promoció dutes a terme des de l’organització Nivell de molèstia previst i interferència en la vida quotidiana.',
"Quin és el paper de les accions de promoció en les subvencions per a projectes i activitats de l'àmbit turístic?",
"Quin és el benefici de la realització d'exposicions al Centre Cultural Miramar?",
]
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 | 0.0591 |
| cosine_accuracy@3 | 0.1276 |
| cosine_accuracy@5 | 0.1735 |
| cosine_accuracy@10 | 0.2861 |
| cosine_precision@1 | 0.0591 |
| cosine_precision@3 | 0.0425 |
| cosine_precision@5 | 0.0347 |
| cosine_precision@10 | 0.0286 |
| cosine_recall@1 | 0.0591 |
| cosine_recall@3 | 0.1276 |
| cosine_recall@5 | 0.1735 |
| cosine_recall@10 | 0.2861 |
| cosine_ndcg@10 | 0.1537 |
| cosine_mrr@10 | 0.1139 |
| **cosine_map@100** | **0.1398** |
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0591 |
| cosine_accuracy@3 | 0.1257 |
| cosine_accuracy@5 | 0.1801 |
| cosine_accuracy@10 | 0.2946 |
| cosine_precision@1 | 0.0591 |
| cosine_precision@3 | 0.0419 |
| cosine_precision@5 | 0.036 |
| cosine_precision@10 | 0.0295 |
| cosine_recall@1 | 0.0591 |
| cosine_recall@3 | 0.1257 |
| cosine_recall@5 | 0.1801 |
| cosine_recall@10 | 0.2946 |
| cosine_ndcg@10 | 0.1564 |
| cosine_mrr@10 | 0.1149 |
| **cosine_map@100** | **0.1405** |
#### 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 | 0.0591 |
| cosine_accuracy@3 | 0.1257 |
| cosine_accuracy@5 | 0.1707 |
| cosine_accuracy@10 | 0.2983 |
| cosine_precision@1 | 0.0591 |
| cosine_precision@3 | 0.0419 |
| cosine_precision@5 | 0.0341 |
| cosine_precision@10 | 0.0298 |
| cosine_recall@1 | 0.0591 |
| cosine_recall@3 | 0.1257 |
| cosine_recall@5 | 0.1707 |
| cosine_recall@10 | 0.2983 |
| cosine_ndcg@10 | 0.1571 |
| cosine_mrr@10 | 0.115 |
| **cosine_map@100** | **0.1397** |
#### 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 | 0.0516 |
| cosine_accuracy@3 | 0.121 |
| cosine_accuracy@5 | 0.1679 |
| cosine_accuracy@10 | 0.2889 |
| cosine_precision@1 | 0.0516 |
| cosine_precision@3 | 0.0403 |
| cosine_precision@5 | 0.0336 |
| cosine_precision@10 | 0.0289 |
| cosine_recall@1 | 0.0516 |
| cosine_recall@3 | 0.121 |
| cosine_recall@5 | 0.1679 |
| cosine_recall@10 | 0.2889 |
| cosine_ndcg@10 | 0.1498 |
| cosine_mrr@10 | 0.1082 |
| **cosine_map@100** | **0.1338** |
#### 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 | 0.0516 |
| cosine_accuracy@3 | 0.1173 |
| cosine_accuracy@5 | 0.1717 |
| cosine_accuracy@10 | 0.2889 |
| cosine_precision@1 | 0.0516 |
| cosine_precision@3 | 0.0391 |
| cosine_precision@5 | 0.0343 |
| cosine_precision@10 | 0.0289 |
| cosine_recall@1 | 0.0516 |
| cosine_recall@3 | 0.1173 |
| cosine_recall@5 | 0.1717 |
| cosine_recall@10 | 0.2889 |
| cosine_ndcg@10 | 0.1488 |
| cosine_mrr@10 | 0.1069 |
| **cosine_map@100** | **0.1328** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0507 |
| cosine_accuracy@3 | 0.1126 |
| cosine_accuracy@5 | 0.1642 |
| cosine_accuracy@10 | 0.2824 |
| cosine_precision@1 | 0.0507 |
| cosine_precision@3 | 0.0375 |
| cosine_precision@5 | 0.0328 |
| cosine_precision@10 | 0.0282 |
| cosine_recall@1 | 0.0507 |
| cosine_recall@3 | 0.1126 |
| cosine_recall@5 | 0.1642 |
| cosine_recall@10 | 0.2824 |
| cosine_ndcg@10 | 0.1449 |
| cosine_mrr@10 | 0.104 |
| **cosine_map@100** | **0.1306** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 9,593 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 49.28 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.16 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
| <code>Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament l’inici o modificació substancial d’una activitat econòmica, i hi adjunta el certificat tècnic acreditatiu del compliment dels requisits necessaris que estableix la normativa vigent per a l‘exercici de l’activitat.</code> | <code>Quin és el resultat esperat després de presentar el certificat tècnic en el tràmit de comunicació d'inici d'activitat?</code> |
| <code>L'Ajuntament de Sitges ofereix a aquelles famílies que acompleixin els requisits establerts, ajuts per al pagament de la quota del servei i de la quota del menjador dels infants matriculats a les Llars d'Infants Municipals ( 0-3 anys).</code> | <code>Quins són els requisits per a beneficiar-se dels ajuts de l'Ajuntament de Sitges?</code> |
| <code>Les entitats o associacions culturals han de presentar la sol·licitud de subvenció dins del termini establert per l'Ajuntament de Sitges.</code> | <code>Quin és el termini per a presentar una sol·licitud de subvenció per a un projecte cultural?</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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.2667 | 10 | 3.5318 | - | - | - | - | - | - |
| 0.5333 | 20 | 2.3744 | - | - | - | - | - | - |
| 0.8 | 30 | 1.6587 | - | - | - | - | - | - |
| 0.9867 | 37 | - | 0.1350 | 0.1317 | 0.1349 | 0.1341 | 0.1207 | 0.1322 |
| 1.0667 | 40 | 1.1513 | - | - | - | - | - | - |
| 1.3333 | 50 | 1.0055 | - | - | - | - | - | - |
| 1.6 | 60 | 0.7369 | - | - | - | - | - | - |
| 1.8667 | 70 | 0.4855 | - | - | - | - | - | - |
| 2.0 | 75 | - | 0.1366 | 0.1370 | 0.1376 | 0.1345 | 0.1290 | 0.1355 |
| 2.1333 | 80 | 0.4362 | - | - | - | - | - | - |
| 2.4 | 90 | 0.3943 | - | - | - | - | - | - |
| 2.6667 | 100 | 0.3495 | - | - | - | - | - | - |
| 2.9333 | 110 | 0.2138 | - | - | - | - | - | - |
| **2.9867** | **112** | **-** | **0.1364** | **0.1342** | **0.1374** | **0.1361** | **0.1256** | **0.1367** |
| 3.2 | 120 | 0.2176 | - | - | - | - | - | - |
| 3.4667 | 130 | 0.2513 | - | - | - | - | - | - |
| 3.7333 | 140 | 0.2163 | - | - | - | - | - | - |
| 4.0 | 150 | 0.15 | 0.1401 | 0.1308 | 0.1332 | 0.1396 | 0.1279 | 0.1396 |
| 4.2667 | 160 | 0.1613 | - | - | - | - | - | - |
| 4.5333 | 170 | 0.1955 | - | - | - | - | - | - |
| 4.8 | 180 | 0.1514 | - | - | - | - | - | - |
| 4.9333 | 185 | - | 0.1398 | 0.1328 | 0.1338 | 0.1397 | 0.1306 | 0.1405 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.0.dev0
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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