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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:5750
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: El seu objecte és que -prèviament a la seva execució material-
l'Ajuntament comprovi l'adequació de l'actuació a la normativa i planejament,
així com a les ordenances municipals.
sentences:
- Quin és el paper de la normativa en la llicència de tala de masses arbòries?
- Com puc actualitzar les meves dades de naixement al Padró?
- Quin és el paper de la persona tècnica competent en la llicència per a la primera
utilització i ocupació parcial de l'edifici?
- source_sentence: El seu objecte és que -prèviament a la seva execució material-
l'Ajuntament comprovi l'adequació de l’actuació a la normativa i planejament,
així com a les ordenances municipals sobre l’ús del sòl i edificació.
sentences:
- Quin és el propòsit del tràmit CA05?
- Quin és el propòsit del tràmit de llicència d'instal·lació de producció d'energia
elèctrica?
- Quin és el paper de l'Ajuntament de Sant Quirze del Vallès en la notificació electrònica
de procediments?
- source_sentence: 'PROFESSIONALS: Assistència jurídica, traducció/interpretació,
psicologia, o qualsevol professió o habilitat que vulgueu posar a disposició del
banc de recursos.'
sentences:
- Quin és el propòsit del tràmit de comunicació prèvia d'obertura d'activitat de
baix risc?
- Quin és el tipus d’autorització que es necessita per a talls de carrers?
- Quin és el paper dels professionals en el banc de recursos?
- source_sentence: No està especificat
sentences:
- Quin és el percentatge de bonificació per a una família nombrosa amb 3 membres
i una renda màxima anual bruta de 25.815,45 euros?
- Quin és el propòsit del tràmit de baixa del Padró d'Habitants per defunció?
- Quin és el procediment per a cancel·lar les concessions de drets funeraris de
nínxols?
- source_sentence: 'Import En cas de renovació per caducitat, pèrdua, sostracció o
deteriorament: 12,00 € (en metàl·lic i preferiblement import exacte).'
sentences:
- Quin és el procediment per a la renovació del DNI en cas de sostracció?
- Quin és el paper del motiu legítim en l'oposició de dades personals en cas de
motiu legítim i situació personal concreta?
- Vull fer una activitat a l'espai públic, quin és el tràmit que debo seguir?
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: 0.0406885758998435
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.11737089201877934
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.18153364632237873
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3302034428794992
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0406885758998435
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.03912363067292644
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03630672926447575
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03302034428794992
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0406885758998435
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11737089201877934
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18153364632237873
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3302034428794992
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15804646538595332
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10652433117221861
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12794271910761573
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.03912363067292645
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.107981220657277
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.18153364632237873
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3286384976525822
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.03912363067292645
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.03599374021909233
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03630672926447575
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03286384976525822
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03912363067292645
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.107981220657277
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18153364632237873
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3286384976525822
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15506867908727437
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10328203790645119
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12470788174358402
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.0406885758998435
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.10172143974960876
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.16588419405320814
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3223787167449139
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0406885758998435
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.033907146583202916
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03317683881064163
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03223787167449139
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0406885758998435
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10172143974960876
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16588419405320814
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3223787167449139
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15172399342641055
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1010190774275283
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12301092660478197
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.04225352112676056
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.10954616588419405
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.18466353677621283
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3270735524256651
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.04225352112676056
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.03651538862806468
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03693270735524257
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03270735524256651
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04225352112676056
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10954616588419405
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18466353677621283
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3270735524256651
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15644008525556197
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10541458628313109
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1273528705075161
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.0406885758998435
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.11267605633802817
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.17996870109546165
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3145539906103286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0406885758998435
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.03755868544600939
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03599374021909233
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03145539906103287
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0406885758998435
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11267605633802817
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17996870109546165
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3145539906103286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.15177339619789426
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10291936806021326
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12605282457123526
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.0406885758998435
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.09859154929577464
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.1596244131455399
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.29107981220657275
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0406885758998435
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.03286384976525822
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03192488262910798
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02910798122065728
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0406885758998435
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.09859154929577464
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1596244131455399
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.29107981220657275
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14046451788883374
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.09552562287304085
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.11941800675417487
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/sqv-5ep")
# Run inference
sentences = [
'Import En cas de renovació per caducitat, pèrdua, sostracció o deteriorament: 12,00 € (en metàl·lic i preferiblement import exacte).',
'Quin és el procediment per a la renovació del DNI en cas de sostracció?',
"Quin és el paper del motiu legítim en l'oposició de dades personals en cas de motiu legítim i situació personal concreta?",
]
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 | 0.0407 |
| cosine_accuracy@3 | 0.1174 |
| cosine_accuracy@5 | 0.1815 |
| cosine_accuracy@10 | 0.3302 |
| cosine_precision@1 | 0.0407 |
| cosine_precision@3 | 0.0391 |
| cosine_precision@5 | 0.0363 |
| cosine_precision@10 | 0.033 |
| cosine_recall@1 | 0.0407 |
| cosine_recall@3 | 0.1174 |
| cosine_recall@5 | 0.1815 |
| cosine_recall@10 | 0.3302 |
| cosine_ndcg@10 | 0.158 |
| cosine_mrr@10 | 0.1065 |
| **cosine_map@100** | **0.1279** |
#### 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 | 0.0391 |
| cosine_accuracy@3 | 0.108 |
| cosine_accuracy@5 | 0.1815 |
| cosine_accuracy@10 | 0.3286 |
| cosine_precision@1 | 0.0391 |
| cosine_precision@3 | 0.036 |
| cosine_precision@5 | 0.0363 |
| cosine_precision@10 | 0.0329 |
| cosine_recall@1 | 0.0391 |
| cosine_recall@3 | 0.108 |
| cosine_recall@5 | 0.1815 |
| cosine_recall@10 | 0.3286 |
| cosine_ndcg@10 | 0.1551 |
| cosine_mrr@10 | 0.1033 |
| **cosine_map@100** | **0.1247** |
#### 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 | 0.0407 |
| cosine_accuracy@3 | 0.1017 |
| cosine_accuracy@5 | 0.1659 |
| cosine_accuracy@10 | 0.3224 |
| cosine_precision@1 | 0.0407 |
| cosine_precision@3 | 0.0339 |
| cosine_precision@5 | 0.0332 |
| cosine_precision@10 | 0.0322 |
| cosine_recall@1 | 0.0407 |
| cosine_recall@3 | 0.1017 |
| cosine_recall@5 | 0.1659 |
| cosine_recall@10 | 0.3224 |
| cosine_ndcg@10 | 0.1517 |
| cosine_mrr@10 | 0.101 |
| **cosine_map@100** | **0.123** |
#### 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 | 0.0423 |
| cosine_accuracy@3 | 0.1095 |
| cosine_accuracy@5 | 0.1847 |
| cosine_accuracy@10 | 0.3271 |
| cosine_precision@1 | 0.0423 |
| cosine_precision@3 | 0.0365 |
| cosine_precision@5 | 0.0369 |
| cosine_precision@10 | 0.0327 |
| cosine_recall@1 | 0.0423 |
| cosine_recall@3 | 0.1095 |
| cosine_recall@5 | 0.1847 |
| cosine_recall@10 | 0.3271 |
| cosine_ndcg@10 | 0.1564 |
| cosine_mrr@10 | 0.1054 |
| **cosine_map@100** | **0.1274** |
#### 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 | 0.0407 |
| cosine_accuracy@3 | 0.1127 |
| cosine_accuracy@5 | 0.18 |
| cosine_accuracy@10 | 0.3146 |
| cosine_precision@1 | 0.0407 |
| cosine_precision@3 | 0.0376 |
| cosine_precision@5 | 0.036 |
| cosine_precision@10 | 0.0315 |
| cosine_recall@1 | 0.0407 |
| cosine_recall@3 | 0.1127 |
| cosine_recall@5 | 0.18 |
| cosine_recall@10 | 0.3146 |
| cosine_ndcg@10 | 0.1518 |
| cosine_mrr@10 | 0.1029 |
| **cosine_map@100** | **0.1261** |
#### 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 | 0.0407 |
| cosine_accuracy@3 | 0.0986 |
| cosine_accuracy@5 | 0.1596 |
| cosine_accuracy@10 | 0.2911 |
| cosine_precision@1 | 0.0407 |
| cosine_precision@3 | 0.0329 |
| cosine_precision@5 | 0.0319 |
| cosine_precision@10 | 0.0291 |
| cosine_recall@1 | 0.0407 |
| cosine_recall@3 | 0.0986 |
| cosine_recall@5 | 0.1596 |
| cosine_recall@10 | 0.2911 |
| cosine_ndcg@10 | 0.1405 |
| cosine_mrr@10 | 0.0955 |
| **cosine_map@100** | **0.1194** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 5,750 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: 4 tokens</li><li>mean: 43.32 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.77 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| <code>Aquest tràmit permet donar d'alta ofertes de treball que es gestionaran pel Servei a l'Ocupació.</code> | <code>Com puc saber si el meu perfil és compatible amb les ofertes de treball?</code> |
| <code>El titular de l’activitat ha de declarar sota la seva responsabilitat, que compleix els requisits establerts per la normativa vigent per a l’exercici de l’activitat, que disposa d’un certificat tècnic justificatiu i que es compromet a mantenir-ne el compliment durant el seu exercici.</code> | <code>Quin és el paper del titular de l'activitat en la Declaració responsable?</code> |
| <code>Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.</code> | <code>Quin és el paper del cedent en la transmissió de drets funeraris?</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
- `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
- `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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4444 | 10 | 4.5093 | - | - | - | - | - | - |
| 0.8889 | 20 | 2.7989 | - | - | - | - | - | - |
| 0.9778 | 22 | - | 0.1072 | 0.1182 | 0.1122 | 0.1083 | 0.1044 | 0.1082 |
| 1.3333 | 30 | 1.8343 | - | - | - | - | - | - |
| 1.7778 | 40 | 1.5248 | - | - | - | - | - | - |
| 2.0 | 45 | - | 0.1182 | 0.1203 | 0.1163 | 0.1188 | 0.1209 | 0.1229 |
| 2.2222 | 50 | 0.9624 | - | - | - | - | - | - |
| 2.6667 | 60 | 1.1161 | - | - | - | - | - | - |
| **2.9778** | **67** | **-** | **0.1235** | **0.1324** | **0.1302** | **0.1252** | **0.1213** | **0.1239** |
| 3.1111 | 70 | 0.7405 | - | - | - | - | - | - |
| 3.5556 | 80 | 0.8621 | - | - | - | - | - | - |
| 4.0 | 90 | 0.6071 | 0.1249 | 0.1282 | 0.1310 | 0.1280 | 0.1181 | 0.1278 |
| 4.4444 | 100 | 0.7091 | - | - | - | - | - | - |
| 4.8889 | 110 | 0.606 | 0.1279 | 0.1261 | 0.1274 | 0.1230 | 0.1194 | 0.1247 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.35.0.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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