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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:5520
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Queda exclosa de la prohibició, dintre de les àrees recreatives
i d'acampada i en parcel·les de les urbanitzacions, la utilització dels fogons
de gas i de barbacoes d'obra amb mataguspires.
sentences:
- Què està prohibit fer en àrees d'acampada?
- Quin és el benefici de la reserva d'un equipament municipal?
- Quin és el benefici de la targeta d'aparcament individual per a l'autonomia personal?
- source_sentence: Aquest tràmit permet participar en processos oberts de selecció
i provisió de personal de l'Ajuntament, i fer el pagament de la taxa per drets
d'examen establerta en la convocatòria.
sentences:
- Quin és el requisit per participar en un procés de selecció de personal de l'Ajuntament?
- On es pot trobar la relació de requeriments de documentació per a l'ajut de menjador
escolar?
- Quin és el tipus d'activitats que es poden practicar amb les armes de 4a categoria?
- source_sentence: Sol·licitar la cessió temporal d’un compostador domèstic.
sentences:
- Quin és el requisit per a la tala d'arbres aïllats en sòl urbà?
- Quin és el paper de la persona interessada en aquest tràmit?
- Quin és el paper del compostador domèstic en la reducció de les emissions de gasos
d'efecte hivernacle?
- source_sentence: Matriculació a l'Escola Bressol Municipal El Patufet.
sentences:
- Quin és el termini màxim per a deutes de 1.500,01 fins a 6.000,00 euros en el
criteri excepcional?
- Quin és el lloc on es realitza el tràmit de matrícula?
- Quin és el lloc on es realitza el taller 'Informàtica nivell bàsic'?
- source_sentence: Aquest tipus de transmissió entre cedent i cessionari només podrà
ser de caràcter gratuït i no condicionada.
sentences:
- Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?
- Quin és el propòsit de la comunicació prèvia en relació amb la intervenció definitiva?
- Quin és el propòsit de la Deixalleria municipal?
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: 0.04782608695652174
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.20869565217391303
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.30869565217391304
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5565217391304348
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.04782608695652174
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06956521739130433
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.061739130434782616
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.055652173913043466
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04782608695652174
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.20869565217391303
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30869565217391304
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5565217391304348
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25888429095047366
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16955314009661854
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18763324173665294
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.06086956521739131
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21304347826086956
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.30434782608695654
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5565217391304348
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06086956521739131
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07101449275362319
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06086956521739131
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.055652173913043466
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06086956521739131
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21304347826086956
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30434782608695654
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5565217391304348
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2637812435357463
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.17599723947550047
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.19341889075062485
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.0782608695652174
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21739130434782608
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.34347826086956523
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5695652173913044
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0782608695652174
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07246376811594202
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06869565217391305
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05695652173913043
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0782608695652174
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21739130434782608
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.34347826086956523
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5695652173913044
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28117776588045035
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1947342995169084
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21224466664057137
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.05217391304347826
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.20869565217391303
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3173913043478261
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5130434782608696
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05217391304347826
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06956521739130433
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06347826086956522
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05130434782608694
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05217391304347826
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.20869565217391303
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3173913043478261
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5130434782608696
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.24833360148474737
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16793305728088342
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1892957688791951
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.05652173913043478
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22608695652173913
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32608695652173914
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5434782608695652
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05652173913043478
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0753623188405797
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06521739130434782
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05434782608695651
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05652173913043478
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22608695652173913
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32608695652173914
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5434782608695652
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2660596038952714
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.18197895100069028
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20038255187663148
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.05652173913043478
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21739130434782608
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3173913043478261
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5434782608695652
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05652173913043478
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07246376811594202
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06347826086956522
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.054347826086956506
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05652173913043478
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21739130434782608
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3173913043478261
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5434782608695652
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2641081743881476
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.17965838509316792
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.19707496290303578
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-v5-10ep")
# Run inference
sentences = [
'Aquest tipus de transmissió entre cedent i cessionari només podrà ser de caràcter gratuït i no condicionada.',
'Quin és el caràcter de la transmissió de drets funeraris entre cedent i cessionari?',
'Quin és el propòsit de la Deixalleria municipal?',
]
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 | 0.0478 |
| cosine_accuracy@3 | 0.2087 |
| cosine_accuracy@5 | 0.3087 |
| cosine_accuracy@10 | 0.5565 |
| cosine_precision@1 | 0.0478 |
| cosine_precision@3 | 0.0696 |
| cosine_precision@5 | 0.0617 |
| cosine_precision@10 | 0.0557 |
| cosine_recall@1 | 0.0478 |
| cosine_recall@3 | 0.2087 |
| cosine_recall@5 | 0.3087 |
| cosine_recall@10 | 0.5565 |
| cosine_ndcg@10 | 0.2589 |
| cosine_mrr@10 | 0.1696 |
| **cosine_map@100** | **0.1876** |
#### 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 | 0.0609 |
| cosine_accuracy@3 | 0.213 |
| cosine_accuracy@5 | 0.3043 |
| cosine_accuracy@10 | 0.5565 |
| cosine_precision@1 | 0.0609 |
| cosine_precision@3 | 0.071 |
| cosine_precision@5 | 0.0609 |
| cosine_precision@10 | 0.0557 |
| cosine_recall@1 | 0.0609 |
| cosine_recall@3 | 0.213 |
| cosine_recall@5 | 0.3043 |
| cosine_recall@10 | 0.5565 |
| cosine_ndcg@10 | 0.2638 |
| cosine_mrr@10 | 0.176 |
| **cosine_map@100** | **0.1934** |
#### 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 | 0.0783 |
| cosine_accuracy@3 | 0.2174 |
| cosine_accuracy@5 | 0.3435 |
| cosine_accuracy@10 | 0.5696 |
| cosine_precision@1 | 0.0783 |
| cosine_precision@3 | 0.0725 |
| cosine_precision@5 | 0.0687 |
| cosine_precision@10 | 0.057 |
| cosine_recall@1 | 0.0783 |
| cosine_recall@3 | 0.2174 |
| cosine_recall@5 | 0.3435 |
| cosine_recall@10 | 0.5696 |
| cosine_ndcg@10 | 0.2812 |
| cosine_mrr@10 | 0.1947 |
| **cosine_map@100** | **0.2122** |
#### 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 | 0.0522 |
| cosine_accuracy@3 | 0.2087 |
| cosine_accuracy@5 | 0.3174 |
| cosine_accuracy@10 | 0.513 |
| cosine_precision@1 | 0.0522 |
| cosine_precision@3 | 0.0696 |
| cosine_precision@5 | 0.0635 |
| cosine_precision@10 | 0.0513 |
| cosine_recall@1 | 0.0522 |
| cosine_recall@3 | 0.2087 |
| cosine_recall@5 | 0.3174 |
| cosine_recall@10 | 0.513 |
| cosine_ndcg@10 | 0.2483 |
| cosine_mrr@10 | 0.1679 |
| **cosine_map@100** | **0.1893** |
#### 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 | 0.0565 |
| cosine_accuracy@3 | 0.2261 |
| cosine_accuracy@5 | 0.3261 |
| cosine_accuracy@10 | 0.5435 |
| cosine_precision@1 | 0.0565 |
| cosine_precision@3 | 0.0754 |
| cosine_precision@5 | 0.0652 |
| cosine_precision@10 | 0.0543 |
| cosine_recall@1 | 0.0565 |
| cosine_recall@3 | 0.2261 |
| cosine_recall@5 | 0.3261 |
| cosine_recall@10 | 0.5435 |
| cosine_ndcg@10 | 0.2661 |
| cosine_mrr@10 | 0.182 |
| **cosine_map@100** | **0.2004** |
#### 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 | 0.0565 |
| cosine_accuracy@3 | 0.2174 |
| cosine_accuracy@5 | 0.3174 |
| cosine_accuracy@10 | 0.5435 |
| cosine_precision@1 | 0.0565 |
| cosine_precision@3 | 0.0725 |
| cosine_precision@5 | 0.0635 |
| cosine_precision@10 | 0.0543 |
| cosine_recall@1 | 0.0565 |
| cosine_recall@3 | 0.2174 |
| cosine_recall@5 | 0.3174 |
| cosine_recall@10 | 0.5435 |
| cosine_ndcg@10 | 0.2641 |
| cosine_mrr@10 | 0.1797 |
| **cosine_map@100** | **0.1971** |
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## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 5,520 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: 43.78 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.5 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>L’Ajuntament vol crear un banc de recursos on recollir tots els oferiments de la població i que servirà per atendre les necessitats de les famílies refugiades acollides al poble.</code> | <code>Quin és el paper de l’Ajuntament en la integració de les persones refugiades acollides?</code> |
| <code>Aquest tipus d'actuació requereix la intervenció d'una persona tècnica competent que subscrigui el projecte o la documentació tècnica corresponent i que assumeixi la direcció facultativa de l'execució de les obres.</code> | <code>Quin és el requisit per a la intervenció d'una persona tècnica competent en les obres d'intervenció parcial interior en edificis amb elements catalogats?</code> |
| <code>Aquest títol, adreçat a persones empadronades a Sant Quirze del Vallès, es concedirà segons el nivell d’ingressos, la condició d’edat o de discapacitat, en base als criteris específics que recull l’ordenança reguladora del sistema de tarifació social del transport públic municipal en autobús a Sant Quirze del Vallès.</code> | <code>Quin és el benefici de la TBUS GRATUÏTA per a les persones majors?</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 |
|:----------:|:-------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4638 | 10 | 4.0375 | - | - | - | - | - | - |
| 0.9275 | 20 | 3.2095 | - | - | - | - | - | - |
| 0.9739 | 21 | - | 0.1772 | 0.1818 | 0.1967 | 0.1911 | 0.1417 | 0.1750 |
| 1.3913 | 30 | 2.1843 | - | - | - | - | - | - |
| 1.8551 | 40 | 1.6095 | - | - | - | - | - | - |
| 1.9942 | 43 | - | 0.1889 | 0.1676 | 0.1961 | 0.1969 | 0.1834 | 0.1899 |
| 2.3188 | 50 | 1.2099 | - | - | - | - | - | - |
| 2.7826 | 60 | 0.909 | - | - | - | - | - | - |
| 2.9681 | 64 | - | 0.1998 | 0.1977 | 0.2164 | 0.2030 | 0.1972 | 0.2156 |
| 3.2464 | 70 | 0.7534 | - | - | - | - | - | - |
| 3.7101 | 80 | 0.6339 | - | - | - | - | - | - |
| 3.9884 | 86 | - | 0.2049 | 0.2024 | 0.1989 | 0.1935 | 0.2046 | 0.1949 |
| 4.1739 | 90 | 0.5423 | - | - | - | - | - | - |
| 4.6377 | 100 | 0.5135 | - | - | - | - | - | - |
| 4.9623 | 107 | - | 0.1967 | 0.2199 | 0.1892 | 0.2113 | 0.1957 | 0.2037 |
| 5.1014 | 110 | 0.4563 | - | - | - | - | - | - |
| 5.5652 | 120 | 0.3837 | - | - | - | - | - | - |
| 5.9826 | 129 | - | 0.2026 | 0.1898 | 0.1903 | 0.2035 | 0.2034 | 0.2187 |
| 6.0290 | 130 | 0.3991 | - | - | - | - | - | - |
| 6.4928 | 140 | 0.3996 | - | - | - | - | - | - |
| 6.9565 | 150 | 0.3225 | 0.2053 | 0.1866 | 0.2046 | 0.2083 | 0.1822 | 0.2086 |
| 7.4203 | 160 | 0.3407 | - | - | - | - | - | - |
| 7.8841 | 170 | 0.2982 | - | - | - | - | - | - |
| **7.9768** | **172** | **-** | **0.2092** | **0.2197** | **0.2005** | **0.2178** | **0.2063** | **0.2042** |
| 8.3478 | 180 | 0.3169 | - | - | - | - | - | - |
| 8.8116 | 190 | 0.2799 | - | - | - | - | - | - |
| 8.9971 | 194 | - | 0.2053 | 0.2215 | 0.1929 | 0.2191 | 0.2106 | 0.2170 |
| 9.2754 | 200 | 0.312 | - | - | - | - | - | - |
| 9.7391 | 210 | 0.2684 | 0.1876 | 0.2004 | 0.1893 | 0.2122 | 0.1971 | 0.1934 |
* 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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