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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:5175
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Caldrà executar l'obra comunicada prèviament d'acord amb les condicions
específiques que es contenen en el model normalitzat CT02.
sentences:
- Quin és el propòsit de la instal·lació d'un circ sense animals a la via pública?
- Quin és el destinatari de les dades bloquejades?
- Quin és el format de presentació de la comunicació prèvia?
- source_sentence: Armes utilitzables en activitats lúdico-esportives d’airsoft i
paintball...
sentences:
- Quin és el paper de l'AFA en la venda de llibres?
- Quin és el benefici de tenir dades personals correctes?
- Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria?
- source_sentence: En les activitats sotmeses al règim d’autorització ambiental o
llicència municipal d’activitat (Annex I o Annex II de la Llei 20/2009) cal demanar
aquest certificat previ a la presentació de la sol·licitud d’autorització ambiental
o llicència municipal.
sentences:
- Quin és el benefici de tenir el certificat de compatibilitat urbanística en les
activitats sotmeses a llicència municipal d’activitat?
- Com puc controlar la recepció de propaganda electoral per correu?
- Quin és el benefici de la cessió d'un compostador domèstic per a l'entorn?
- source_sentence: La persona interessada posa en coneixement de l’Administració,
les actuacions urbanístiques que pretén dur a terme consistents en l'apuntalament
o reforç provisional d'estructures existents fins a la intervenció definitiva.
sentences:
- Qui pot participar en el Consell d'Adolescents?
- Quin és el resultat de la presentació de la comunicació prèvia?
- Quin és el paper de la persona interessada en relació amb la presentació de la
comunicació prèvia?
- source_sentence: La persona consumidora presenti la reclamació davant de l'entitat
acreditada en un termini superior a un any des de la data en què va presentar
la reclamació a l'empresa.
sentences:
- Quin és el tràmit per inscriure'm al Padró d'Habitants sense tenir constància
de la meva anterior residència?
- Quin és el resultat de la modificació substancial de la llicència d'obres en relació
a les autoritzacions administratives?
- Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?
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: 0.057391304347826085
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.15304347826086956
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.23478260869565218
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.41739130434782606
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.057391304347826085
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.051014492753623186
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04695652173913043
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04173913043478261
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.057391304347826085
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15304347826086956
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.23478260869565218
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.41739130434782606
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20551130934080394
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14188060731539
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16516795239083046
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.05565217391304348
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.16
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.40695652173913044
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05565217391304348
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.048
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04069565217391305
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05565217391304348
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40695652173913044
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20158774447839253
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.13959282263630102
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16377775492511307
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.06956521739130435
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.16695652173913045
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24869565217391304
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4260869565217391
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06956521739130435
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05565217391304348
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04973913043478261
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.042608695652173914
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06956521739130435
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16695652173913045
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24869565217391304
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4260869565217391
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21580306349457917
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1526128364389235
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1754746652296583
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.05565217391304348
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.16695652173913045
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.25217391304347825
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.42434782608695654
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05565217391304348
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05565217391304348
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05043478260869566
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.042434782608695654
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05565217391304348
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16695652173913045
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.25217391304347825
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.42434782608695654
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2100045076980214
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14526432022084196
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1684764968624273
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.06086956521739131
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.1617391304347826
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2608695652173913
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4434782608695652
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06086956521739131
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05391304347826087
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05217391304347826
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04434782608695652
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06086956521739131
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1617391304347826
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2608695652173913
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4434782608695652
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21805066438366894
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.15018150448585244
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17220421856187046
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.06086956521739131
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.15478260869565216
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24521739130434783
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.42782608695652175
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06086956521739131
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05159420289855072
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04904347826086957
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.042782608695652175
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06086956521739131
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15478260869565216
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24521739130434783
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.42782608695652175
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21079002748958972
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14568875086266406
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16756200348857653
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/sqv-v4-10ep")
# Run inference
sentences = [
"La persona consumidora presenti la reclamació davant de l'entitat acreditada en un termini superior a un any des de la data en què va presentar la reclamació a l'empresa.",
"Quin és el paper de l'entitat acreditada en la tramitació d'una reclamació?",
"Quin és el resultat de la modificació substancial de la llicència d'obres en relació a les autoritzacions administratives?",
]
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 | 0.0574 |
| cosine_accuracy@3 | 0.153 |
| cosine_accuracy@5 | 0.2348 |
| cosine_accuracy@10 | 0.4174 |
| cosine_precision@1 | 0.0574 |
| cosine_precision@3 | 0.051 |
| cosine_precision@5 | 0.047 |
| cosine_precision@10 | 0.0417 |
| cosine_recall@1 | 0.0574 |
| cosine_recall@3 | 0.153 |
| cosine_recall@5 | 0.2348 |
| cosine_recall@10 | 0.4174 |
| cosine_ndcg@10 | 0.2055 |
| cosine_mrr@10 | 0.1419 |
| **cosine_map@100** | **0.1652** |
#### 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 | 0.0557 |
| cosine_accuracy@3 | 0.16 |
| cosine_accuracy@5 | 0.24 |
| cosine_accuracy@10 | 0.407 |
| cosine_precision@1 | 0.0557 |
| cosine_precision@3 | 0.0533 |
| cosine_precision@5 | 0.048 |
| cosine_precision@10 | 0.0407 |
| cosine_recall@1 | 0.0557 |
| cosine_recall@3 | 0.16 |
| cosine_recall@5 | 0.24 |
| cosine_recall@10 | 0.407 |
| cosine_ndcg@10 | 0.2016 |
| cosine_mrr@10 | 0.1396 |
| **cosine_map@100** | **0.1638** |
#### 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 | 0.0696 |
| cosine_accuracy@3 | 0.167 |
| cosine_accuracy@5 | 0.2487 |
| cosine_accuracy@10 | 0.4261 |
| cosine_precision@1 | 0.0696 |
| cosine_precision@3 | 0.0557 |
| cosine_precision@5 | 0.0497 |
| cosine_precision@10 | 0.0426 |
| cosine_recall@1 | 0.0696 |
| cosine_recall@3 | 0.167 |
| cosine_recall@5 | 0.2487 |
| cosine_recall@10 | 0.4261 |
| cosine_ndcg@10 | 0.2158 |
| cosine_mrr@10 | 0.1526 |
| **cosine_map@100** | **0.1755** |
#### 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 | 0.0557 |
| cosine_accuracy@3 | 0.167 |
| cosine_accuracy@5 | 0.2522 |
| cosine_accuracy@10 | 0.4243 |
| cosine_precision@1 | 0.0557 |
| cosine_precision@3 | 0.0557 |
| cosine_precision@5 | 0.0504 |
| cosine_precision@10 | 0.0424 |
| cosine_recall@1 | 0.0557 |
| cosine_recall@3 | 0.167 |
| cosine_recall@5 | 0.2522 |
| cosine_recall@10 | 0.4243 |
| cosine_ndcg@10 | 0.21 |
| cosine_mrr@10 | 0.1453 |
| **cosine_map@100** | **0.1685** |
#### 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 | 0.0609 |
| cosine_accuracy@3 | 0.1617 |
| cosine_accuracy@5 | 0.2609 |
| cosine_accuracy@10 | 0.4435 |
| cosine_precision@1 | 0.0609 |
| cosine_precision@3 | 0.0539 |
| cosine_precision@5 | 0.0522 |
| cosine_precision@10 | 0.0443 |
| cosine_recall@1 | 0.0609 |
| cosine_recall@3 | 0.1617 |
| cosine_recall@5 | 0.2609 |
| cosine_recall@10 | 0.4435 |
| cosine_ndcg@10 | 0.2181 |
| cosine_mrr@10 | 0.1502 |
| **cosine_map@100** | **0.1722** |
#### 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 | 0.0609 |
| cosine_accuracy@3 | 0.1548 |
| cosine_accuracy@5 | 0.2452 |
| cosine_accuracy@10 | 0.4278 |
| cosine_precision@1 | 0.0609 |
| cosine_precision@3 | 0.0516 |
| cosine_precision@5 | 0.049 |
| cosine_precision@10 | 0.0428 |
| cosine_recall@1 | 0.0609 |
| cosine_recall@3 | 0.1548 |
| cosine_recall@5 | 0.2452 |
| cosine_recall@10 | 0.4278 |
| cosine_ndcg@10 | 0.2108 |
| cosine_mrr@10 | 0.1457 |
| **cosine_map@100** | **0.1676** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 5,175 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: 43.23 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.25 tokens</li><li>max: 46 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
| <code>Aquest tràmit us permet consultar informació de les anotacions d'entrada i sortida que hi consten al registre de l'Ajuntament de Sant Quirze del Vallès.</code> | <code>Quin és el format de les dades de sortida del tràmit?</code> |
| <code>Tràmit a través del qual la persona interessada posa en coneixement de l’Ajuntament la voluntat de: ... Renunciar a una llicència prèviament atorgada.</code> | <code>Quin és el resultat de la renúncia a una llicència urbanística prèviament atorgada?</code> |
| <code>D’acord amb el plànol d'ubicació de parades: Mercat de diumenges a Les Fonts</code> | <code>Quin és el plànol d'ubicació de parades del mercat de diumenges a Les Fonts?</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`: 10
- `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`: 10
- `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 |
|:---------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4938 | 10 | 4.1082 | - | - | - | - | - | - |
| 0.9877 | 20 | 3.2445 | 0.1490 | 0.1440 | 0.1466 | 0.1546 | 0.1249 | 0.1521 |
| 1.4815 | 30 | 1.9296 | - | - | - | - | - | - |
| 1.9753 | 40 | 1.7067 | 0.1607 | 0.1548 | 0.1567 | 0.1648 | 0.1448 | 0.1593 |
| 2.4691 | 50 | 0.9578 | - | - | - | - | - | - |
| 2.9630 | 60 | 1.003 | 0.1640 | 0.1699 | 0.1660 | 0.1695 | 0.1568 | 0.1592 |
| 3.4568 | 70 | 0.6298 | - | - | - | - | - | - |
| 3.9506 | 80 | 0.7035 | - | - | - | - | - | - |
| 4.0 | 81 | - | 0.1707 | 0.1657 | 0.1769 | 0.1690 | 0.1610 | 0.1719 |
| 4.4444 | 90 | 0.4606 | - | - | - | - | - | - |
| 4.9383 | 100 | 0.5131 | - | - | - | - | - | - |
| 4.9877 | 101 | - | 0.1645 | 0.1686 | 0.1669 | 0.1620 | 0.1580 | 0.1722 |
| 5.4321 | 110 | 0.3748 | - | - | - | - | - | - |
| 5.9259 | 120 | 0.4799 | - | - | - | - | - | - |
| 5.9753 | 121 | - | 0.1670 | 0.1670 | 0.1725 | 0.1711 | 0.1628 | 0.1715 |
| 6.4198 | 130 | 0.3237 | - | - | - | - | - | - |
| 6.9136 | 140 | 0.4132 | - | - | - | - | - | - |
| **6.963** | **141** | **-** | **0.1746** | **0.1757** | **0.1697** | **0.1746** | **0.1655** | **0.1746** |
| 7.4074 | 150 | 0.3169 | - | - | - | - | - | - |
| 7.9012 | 160 | 0.3438 | - | - | - | - | - | - |
| 8.0 | 162 | - | 0.1692 | 0.1698 | 0.1718 | 0.1735 | 0.1707 | 0.1656 |
| 8.3951 | 170 | 0.2987 | - | - | - | - | - | - |
| 8.8889 | 180 | 0.3193 | - | - | - | - | - | - |
| 8.9877 | 182 | - | 0.1703 | 0.1703 | 0.1695 | 0.1710 | 0.1619 | 0.1666 |
| 9.3827 | 190 | 0.2883 | - | - | - | - | - | - |
| 9.8765 | 200 | 0.3098 | 0.1652 | 0.1722 | 0.1685 | 0.1755 | 0.1676 | 0.1638 |
* 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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