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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:6468
- 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 sobre l’ús del sòl i edificació.
sentences:
- Quin és el paper de les ordenances municipals en la llicència d'extracció d'àrids
i explotació de pedreres?
- Quin és el percentatge de bonificació que es pot obtenir?
- Quin és el propòsit del tràmit d'adjudicació d'habitatges socials i d'emergència?
- source_sentence: La renda és un element important en la tramitació d'un ajornament
o fraccionament, ja que es té en compte per determinar si el sol·licitant compleix
els requisits per a sol·licitar el criteri excepcional.
sentences:
- Quin és el paper de la renda en la tramitació d'un ajornament o fraccionament?
- Quin és l'objectiu del tràmit C03?
- Quin és el paper de les ordenances municipals en la llicència de parcel·lació?
- source_sentence: L’article 14 de la llei 39/2015 estableix l’obligatorietat de l’ús
de mitjans electrònics, informàtics o telemàtics per desenvolupar totes les fases
del procediment de contractació.
sentences:
- Quin és el paper de les ordenances municipals sobre l’ús del sòl i edificació
en el tràmit de modificació substancial de la llicència d'obres?
- Quin és el requisit per a la intervenció d'una persona tècnica?
- Quin és el propòsit de l’article 14 de la llei 39/2015?
- 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 paper del planejament en el tràmit de llicència d'obres per l'obertura,
la pavimentació i la modificació de camins rurals?
- Quin és el requisit per presentar una sol·licitud?
- Quin és el resultat de la falta de presentació de la documentació tècnica corresponent?
- source_sentence: L’Ajuntament de Sant Quirze del Vallès reconeix un dret preferent
al titular del dret funerari sobre la corresponent sepultura o al successor o
causahavent de l’anterior titular d’aquest dret, que permet adquirir de nou el
dret funerari referit, sobre la mateixa sepultura, un cop el dret atorgat ha exhaurit
el termini de vigència
sentences:
- Quin és el requisit per a les instal·lacions solars per mantenir la bonificació?
- Quin és el paper del cens electoral en les eleccions?
- Quan es pot adquirir de nou el dret funerari?
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: 0.10173160173160173
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.27705627705627706
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36796536796536794
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.48268398268398266
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10173160173160173
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09235209235209235
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0735930735930736
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04826839826839826
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10173160173160173
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.27705627705627706
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36796536796536794
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.48268398268398266
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.27573421573267004
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21126485947914525
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22874042563037256
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.11904761904761904
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.29004329004329005
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3658008658008658
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.49567099567099565
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11904761904761904
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09668109668109669
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07316017316017315
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.049567099567099565
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11904761904761904
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.29004329004329005
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3658008658008658
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49567099567099565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2892077987787756
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22525767882910738
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24276232307204765
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.10822510822510822
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2662337662337662
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36363636363636365
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5064935064935064
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10822510822510822
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08874458874458875
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07272727272727272
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.050649350649350645
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10822510822510822
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2662337662337662
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36363636363636365
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5064935064935064
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28386807922368074
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21557239057239053
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23234161860560523
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.11471861471861472
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.24025974025974026
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3398268398268398
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4805194805194805
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11471861471861472
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08008658008658008
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06796536796536796
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04805194805194805
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11471861471861472
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24025974025974026
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3398268398268398
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4805194805194805
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2749619650624931
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21201642273070856
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23043548788604293
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.11255411255411256
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26406926406926406
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.329004329004329
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.487012987012987
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11255411255411256
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08802308802308802
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0658008658008658
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.048701298701298704
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11255411255411256
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.26406926406926406
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.329004329004329
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.487012987012987
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.27907708560411776
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21522795987081703
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23398722217128723
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.1038961038961039
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2619047619047619
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3354978354978355
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.474025974025974
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1038961038961039
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0873015873015873
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0670995670995671
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0474025974025974
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1038961038961039
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2619047619047619
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3354978354978355
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.474025974025974
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2700415740619265
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20714285714285718
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22556246902969454
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-SQV-007-5ep")
# Run inference
sentences = [
'L’Ajuntament de Sant Quirze del Vallès reconeix un dret preferent al titular del dret funerari sobre la corresponent sepultura o al successor o causahavent de l’anterior titular d’aquest dret, que permet adquirir de nou el dret funerari referit, sobre la mateixa sepultura, un cop el dret atorgat ha exhaurit el termini de vigència',
'Quan es pot adquirir de nou el dret funerari?',
'Quin és el paper del cens electoral en les eleccions?',
]
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 | 0.1017 |
| cosine_accuracy@3 | 0.2771 |
| cosine_accuracy@5 | 0.368 |
| cosine_accuracy@10 | 0.4827 |
| cosine_precision@1 | 0.1017 |
| cosine_precision@3 | 0.0924 |
| cosine_precision@5 | 0.0736 |
| cosine_precision@10 | 0.0483 |
| cosine_recall@1 | 0.1017 |
| cosine_recall@3 | 0.2771 |
| cosine_recall@5 | 0.368 |
| cosine_recall@10 | 0.4827 |
| cosine_ndcg@10 | 0.2757 |
| cosine_mrr@10 | 0.2113 |
| **cosine_map@100** | **0.2287** |
#### 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 | 0.119 |
| cosine_accuracy@3 | 0.29 |
| cosine_accuracy@5 | 0.3658 |
| cosine_accuracy@10 | 0.4957 |
| cosine_precision@1 | 0.119 |
| cosine_precision@3 | 0.0967 |
| cosine_precision@5 | 0.0732 |
| cosine_precision@10 | 0.0496 |
| cosine_recall@1 | 0.119 |
| cosine_recall@3 | 0.29 |
| cosine_recall@5 | 0.3658 |
| cosine_recall@10 | 0.4957 |
| cosine_ndcg@10 | 0.2892 |
| cosine_mrr@10 | 0.2253 |
| **cosine_map@100** | **0.2428** |
#### 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 | 0.1082 |
| cosine_accuracy@3 | 0.2662 |
| cosine_accuracy@5 | 0.3636 |
| cosine_accuracy@10 | 0.5065 |
| cosine_precision@1 | 0.1082 |
| cosine_precision@3 | 0.0887 |
| cosine_precision@5 | 0.0727 |
| cosine_precision@10 | 0.0506 |
| cosine_recall@1 | 0.1082 |
| cosine_recall@3 | 0.2662 |
| cosine_recall@5 | 0.3636 |
| cosine_recall@10 | 0.5065 |
| cosine_ndcg@10 | 0.2839 |
| cosine_mrr@10 | 0.2156 |
| **cosine_map@100** | **0.2323** |
#### 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 | 0.1147 |
| cosine_accuracy@3 | 0.2403 |
| cosine_accuracy@5 | 0.3398 |
| cosine_accuracy@10 | 0.4805 |
| cosine_precision@1 | 0.1147 |
| cosine_precision@3 | 0.0801 |
| cosine_precision@5 | 0.068 |
| cosine_precision@10 | 0.0481 |
| cosine_recall@1 | 0.1147 |
| cosine_recall@3 | 0.2403 |
| cosine_recall@5 | 0.3398 |
| cosine_recall@10 | 0.4805 |
| cosine_ndcg@10 | 0.275 |
| cosine_mrr@10 | 0.212 |
| **cosine_map@100** | **0.2304** |
#### 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 | 0.1126 |
| cosine_accuracy@3 | 0.2641 |
| cosine_accuracy@5 | 0.329 |
| cosine_accuracy@10 | 0.487 |
| cosine_precision@1 | 0.1126 |
| cosine_precision@3 | 0.088 |
| cosine_precision@5 | 0.0658 |
| cosine_precision@10 | 0.0487 |
| cosine_recall@1 | 0.1126 |
| cosine_recall@3 | 0.2641 |
| cosine_recall@5 | 0.329 |
| cosine_recall@10 | 0.487 |
| cosine_ndcg@10 | 0.2791 |
| cosine_mrr@10 | 0.2152 |
| **cosine_map@100** | **0.234** |
#### 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 | 0.1039 |
| cosine_accuracy@3 | 0.2619 |
| cosine_accuracy@5 | 0.3355 |
| cosine_accuracy@10 | 0.474 |
| cosine_precision@1 | 0.1039 |
| cosine_precision@3 | 0.0873 |
| cosine_precision@5 | 0.0671 |
| cosine_precision@10 | 0.0474 |
| cosine_recall@1 | 0.1039 |
| cosine_recall@3 | 0.2619 |
| cosine_recall@5 | 0.3355 |
| cosine_recall@10 | 0.474 |
| cosine_ndcg@10 | 0.27 |
| cosine_mrr@10 | 0.2071 |
| **cosine_map@100** | **0.2256** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,468 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: 39.4 tokens</li><li>max: 168 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.48 tokens</li><li>max: 44 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------|
| <code>Aquest tràmit permet la inscripció al padró dels canvis de domicili dins de Sant Quirze del Vallès...</code> | <code>Quin és el benefici de la inscripció al Padró d'Habitants?</code> |
| <code>Els recursos que es poden oferir al banc de recursos són: MATERIALS, PROFESSIONALS i SOCIALS.</code> | <code>Quins tipus de recursos es poden oferir al banc de recursos?</code> |
| <code>El termini per a la presentació de sol·licituds serà del 8 al 21 de maig de 2024, ambdós inclosos.</code> | <code>Quin és el termini per a la presentació de sol·licituds per a la preinscripció a l'Escola Bressol Municipal El Patufet?</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 |
|:---------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3951 | 10 | 4.4042 | - | - | - | - | - | - |
| 0.7901 | 20 | 2.9471 | - | - | - | - | - | - |
| 0.9877 | 25 | - | 0.2293 | 0.2045 | 0.2099 | 0.2138 | 0.1717 | 0.2242 |
| 1.1852 | 30 | 2.2351 | - | - | - | - | - | - |
| 1.5802 | 40 | 1.5289 | - | - | - | - | - | - |
| 1.9753 | 50 | 1.2045 | 0.2332 | 0.2182 | 0.2277 | 0.2221 | 0.2051 | 0.2248 |
| 2.3704 | 60 | 0.9435 | - | - | - | - | - | - |
| 2.7654 | 70 | 0.7958 | - | - | - | - | - | - |
| **2.963** | **75** | **-** | **0.2379** | **0.2352** | **0.2276** | **0.2204** | **0.2138** | **0.2235** |
| 3.1605 | 80 | 0.6703 | - | - | - | - | - | - |
| 3.5556 | 90 | 0.6162 | - | - | - | - | - | - |
| 3.9506 | 100 | 0.6079 | - | - | - | - | - | - |
| 3.9901 | 101 | - | 0.2251 | 0.2307 | 0.2201 | 0.2343 | 0.2210 | 0.2348 |
| 4.3457 | 110 | 0.5085 | - | - | - | - | - | - |
| 4.7407 | 120 | 0.5248 | - | - | - | - | - | - |
| 4.9383 | 125 | - | 0.2287 | 0.2340 | 0.2304 | 0.2323 | 0.2256 | 0.2428 |
* 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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