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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:4091
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Aquest tràmit permet formalitzar la matrícula a les llars d’infants
municipals, si l'infant ha estat admès al període de preinscripcions.
sentences:
- Quin és el tràmit que es realitza abans de la matrícula?
- Quin és el propòsit de l'Ajuntament en aquest tràmit?
- Què es pot fer amb les exclusions indegudes al Cens Electoral?
- source_sentence: També cal que facis aquest tràmit per revocar o modificar les dades
de correu electrònic i/o telèfon mòbil facilitades per portar a terme les notificacions.
sentences:
- Què passa si vull canviar la meva adreça de correu electrònic?
- Quin és el resultat de no comunicar la finalització de les obres en el termini
establert?
- Quin és el procés de selecció de personal de l'Ajuntament de Viladecavalls?
- source_sentence: Aquest tràmit et permet comunicar a l'ajuntament de Viladecavalls,
l'actuació en representació fer efectuar un tràmit, d'acord a l'article 5 de la
Llei 39/2015,d'1 d'octubre, del Procediment Administratiu Comú de les Administracions
Públiques.
sentences:
- Quin és el registre que es relaciona amb les dades que es modifiquen?
- Quan es pot consultar la llista definitiva d'admessos?
- Quin és el paper de fer efectuar un tràmit en representació a tercers?
- source_sentence: La taxa per la prestació del Servei de Gestió dels Residus Municipals.
sentences:
- Quins són els motius per inscriure's al Servei Local d'Ocupació?
- Quin és el document que es necessita per a la sol·licitud de volants col·lectius
o de convivència?
- Quin és el paper de la taxa d'escombraries en aquest procés?
- source_sentence: S'ha de comunicar la realització de focs d’esbarjo i qualsevol
mena de crema de vegetació agrària en microexplotacions o petites explotacions
agràries...
sentences:
- Què cal fer si no has rebut el document per pagar IVTM o IBI?
- Quin és el tipus de explotacions agràries que estan subjectes a la comunicació
de focs d'esbarjo o cremes de vegetació agrària en microexplotacions?
- Quin és el paper de les bases de la convocatòria en la sol·licitud de subvenció?
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: 0.12408759124087591
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22627737226277372
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3357664233576642
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5328467153284672
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12408759124087591
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0754257907542579
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06715328467153285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05328467153284672
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12408759124087591
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22627737226277372
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3357664233576642
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5328467153284672
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28998901896488977
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21748928281774996
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24037395859471752
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.1386861313868613
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26277372262773724
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3357664233576642
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5693430656934306
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1386861313868613
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08759124087591241
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06715328467153284
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05693430656934306
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1386861313868613
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26277372262773724
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3357664233576642
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5693430656934306
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31363827421519996
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.23752751708956085
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2568041111732728
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.1386861313868613
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.27007299270072993
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3795620437956204
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5693430656934306
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1386861313868613
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0900243309002433
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07591240875912408
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05693430656934306
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1386861313868613
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.27007299270072993
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3795620437956204
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5693430656934306
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.317041085199572
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24058046576294745
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2615607719139071
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.12408759124087591
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2773722627737226
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32116788321167883
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5182481751824818
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12408759124087591
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09245742092457421
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06423357664233577
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.051824817518248176
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12408759124087591
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2773722627737226
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32116788321167883
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5182481751824818
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.29042019634687105
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2218456725755996
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24399596123266679
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.10948905109489052
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.25547445255474455
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.40145985401459855
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5401459854014599
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10948905109489052
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08515815085158149
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08029197080291971
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05401459854014598
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10948905109489052
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.25547445255474455
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.40145985401459855
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5401459854014599
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2983398214582463
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22380952380952376
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2454078859030295
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.10948905109489052
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.20437956204379562
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3284671532846715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5547445255474452
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10948905109489052
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06812652068126519
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06569343065693431
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05547445255474452
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10948905109489052
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.20437956204379562
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3284671532846715
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5547445255474452
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28965339873789575
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21023635731664925
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22988556376565739
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-VL-001-5ep")
# Run inference
sentences = [
"S'ha de comunicar la realització de focs d’esbarjo i qualsevol mena de crema de vegetació agrària en microexplotacions o petites explotacions agràries...",
"Quin és el tipus de explotacions agràries que estan subjectes a la comunicació de focs d'esbarjo o cremes de vegetació agrària en microexplotacions?",
'Quin és el paper de les bases de la convocatòria en la sol·licitud de subvenció?',
]
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 | 0.1241 |
| cosine_accuracy@3 | 0.2263 |
| cosine_accuracy@5 | 0.3358 |
| cosine_accuracy@10 | 0.5328 |
| cosine_precision@1 | 0.1241 |
| cosine_precision@3 | 0.0754 |
| cosine_precision@5 | 0.0672 |
| cosine_precision@10 | 0.0533 |
| cosine_recall@1 | 0.1241 |
| cosine_recall@3 | 0.2263 |
| cosine_recall@5 | 0.3358 |
| cosine_recall@10 | 0.5328 |
| cosine_ndcg@10 | 0.29 |
| cosine_mrr@10 | 0.2175 |
| **cosine_map@100** | **0.2404** |
#### 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 | 0.1387 |
| cosine_accuracy@3 | 0.2628 |
| cosine_accuracy@5 | 0.3358 |
| cosine_accuracy@10 | 0.5693 |
| cosine_precision@1 | 0.1387 |
| cosine_precision@3 | 0.0876 |
| cosine_precision@5 | 0.0672 |
| cosine_precision@10 | 0.0569 |
| cosine_recall@1 | 0.1387 |
| cosine_recall@3 | 0.2628 |
| cosine_recall@5 | 0.3358 |
| cosine_recall@10 | 0.5693 |
| cosine_ndcg@10 | 0.3136 |
| cosine_mrr@10 | 0.2375 |
| **cosine_map@100** | **0.2568** |
#### 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 | 0.1387 |
| cosine_accuracy@3 | 0.2701 |
| cosine_accuracy@5 | 0.3796 |
| cosine_accuracy@10 | 0.5693 |
| cosine_precision@1 | 0.1387 |
| cosine_precision@3 | 0.09 |
| cosine_precision@5 | 0.0759 |
| cosine_precision@10 | 0.0569 |
| cosine_recall@1 | 0.1387 |
| cosine_recall@3 | 0.2701 |
| cosine_recall@5 | 0.3796 |
| cosine_recall@10 | 0.5693 |
| cosine_ndcg@10 | 0.317 |
| cosine_mrr@10 | 0.2406 |
| **cosine_map@100** | **0.2616** |
#### 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 | 0.1241 |
| cosine_accuracy@3 | 0.2774 |
| cosine_accuracy@5 | 0.3212 |
| cosine_accuracy@10 | 0.5182 |
| cosine_precision@1 | 0.1241 |
| cosine_precision@3 | 0.0925 |
| cosine_precision@5 | 0.0642 |
| cosine_precision@10 | 0.0518 |
| cosine_recall@1 | 0.1241 |
| cosine_recall@3 | 0.2774 |
| cosine_recall@5 | 0.3212 |
| cosine_recall@10 | 0.5182 |
| cosine_ndcg@10 | 0.2904 |
| cosine_mrr@10 | 0.2218 |
| **cosine_map@100** | **0.244** |
#### 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 | 0.1095 |
| cosine_accuracy@3 | 0.2555 |
| cosine_accuracy@5 | 0.4015 |
| cosine_accuracy@10 | 0.5401 |
| cosine_precision@1 | 0.1095 |
| cosine_precision@3 | 0.0852 |
| cosine_precision@5 | 0.0803 |
| cosine_precision@10 | 0.054 |
| cosine_recall@1 | 0.1095 |
| cosine_recall@3 | 0.2555 |
| cosine_recall@5 | 0.4015 |
| cosine_recall@10 | 0.5401 |
| cosine_ndcg@10 | 0.2983 |
| cosine_mrr@10 | 0.2238 |
| **cosine_map@100** | **0.2454** |
#### 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 | 0.1095 |
| cosine_accuracy@3 | 0.2044 |
| cosine_accuracy@5 | 0.3285 |
| cosine_accuracy@10 | 0.5547 |
| cosine_precision@1 | 0.1095 |
| cosine_precision@3 | 0.0681 |
| cosine_precision@5 | 0.0657 |
| cosine_precision@10 | 0.0555 |
| cosine_recall@1 | 0.1095 |
| cosine_recall@3 | 0.2044 |
| cosine_recall@5 | 0.3285 |
| cosine_recall@10 | 0.5547 |
| cosine_ndcg@10 | 0.2897 |
| cosine_mrr@10 | 0.2102 |
| **cosine_map@100** | **0.2299** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 4,091 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: 39.34 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.77 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
| positive | anchor |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
| <code>Posteriorment a l’obtenció de l’informe favorable, caldrà realitzar l’acte de comprovació en matèria d’incendis i procedir a efectuar la comunicació prèvia corresponent.</code> | <code>Quin és el resultat esperat després d'obtenir l'informe previ en matèria d'incendis?</code> |
| <code>El certificat tècnic és un requisit per a l'exercici d'una activitat econòmica innòcua.</code> | <code>Quin és el paper del certificat tècnic en la Declaració responsable d'obertura?</code> |
| <code>El document necessari per realitzar l'autoliquidació de taxa per llicència de primera ocupació és la llicència de primera ocupació de l'immoble.</code> | <code>Quin és el document necessari per realitzar l'autoliquidació de taxa per llicència de primera ocupació?</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_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.625 | 10 | 4.3533 | - | - | - | - | - | - |
| 1.0 | 16 | - | 0.2076 | 0.2123 | 0.2055 | 0.1996 | 0.2188 | 0.1861 |
| 1.2461 | 20 | 2.4149 | - | - | - | - | - | - |
| 1.8711 | 30 | 1.1968 | - | - | - | - | - | - |
| 1.9961 | 32 | - | 0.2056 | 0.2318 | 0.2363 | 0.1932 | 0.2330 | 0.2255 |
| 2.4922 | 40 | 0.7983 | - | - | - | - | - | - |
| **2.9922** | **48** | **-** | **0.2322** | **0.2512** | **0.2514** | **0.2385** | **0.2437** | **0.2489** |
| 3.1133 | 50 | 0.4869 | - | - | - | - | - | - |
| 3.7383 | 60 | 0.3793 | - | - | - | - | - | - |
| 3.9883 | 64 | - | 0.2414 | 0.2364 | 0.2365 | 0.2244 | 0.2167 | 0.2190 |
| 4.3594 | 70 | 0.3421 | - | - | - | - | - | - |
| 4.9844 | 80 | 0.2925 | 0.2404 | 0.2568 | 0.2616 | 0.2440 | 0.2454 | 0.2299 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.1.0.dev0
- Datasets: 3.1.0
- 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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