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---
base_model: BAAI/bge-m3
datasets: []
language:
- ca
library_name: sentence-transformers
license: apache-2.0
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:3755
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: En el cas que la persona beneficiària mantingui les condicions
d’elegibilitat es podrà concedir la pròrroga de la prestació sempre que la persona
interessada ho sol·liciti i ho permetin les dotacions pressupostàries de cada
exercici.
sentences:
- Quin és el benefici de l'ajut a la consolidació d'empreses?
- Quin és el requisit per a la persona beneficiària?
- Quin és el benefici del Registre municipal d'entitats per a l'Ajuntament?
- source_sentence: Aquest tràmit permet la presentació de les sol·licituds per a l’atorgament
de llicències d’aprofitament especial sense transformació del domini públic marítim
terrestre consistent en la instal·lació i explotació d'escola per oferir activitats
nàutiques, amb zona d’avarada, durant la temporada.
sentences:
- Quin és el propòsit de la llicència d'aprofitament especial sense transformació
del domini públic marítim terrestre?
- Quin és el termini per a presentar les sol·licituds de subvencions per a projectes
i activitats a entitats de l'àmbit de drets civils?
- Quin és el lloc on es realitzen les activitats amb aquest permís?
- source_sentence: en cas de compliment dels requisits establerts (persones residents,
titulars de plaça d'aparcament, autotaxis, establiments hotelers)
sentences:
- Quin és el paper de l'administració en la justificació del projecte/activitat
subvencionada?
- Quin és el benefici de ser un autotaxi?
- Quin és el benefici per als establiments de la instal·lació de terrasses o vetlladors?
- source_sentence: La convocatòria és el document que estableix les condicions i els
requisits per a poder sol·licitar les subvencions pel suport educatiu a les escoles
públiques de Sitges.
sentences:
- Quin és el paper de la convocatòria en les subvencions pel suport educatiu a les
escoles públiques de Sitges?
- Quin és el benefici de la consulta prèvia de classificació d'activitat per a l'Ajuntament
de Sitges?
- Quin és el tipus d'ocupació de la via pública que es pot realitzar amb aquest
permís?
- source_sentence: Cal revisar la informació i els terminis de la convocatòria específica
de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.
sentences:
- Quin és el document que es necessita per acreditar l'any de construcció i l'adequació
a la legalitat urbanística d'un immoble?
- Quin és el paper de l'Ajuntament en la gestió de les activitats per temporades?
- On es pot trobar la informació sobre els terminis de presentació d'al·legacions
en un procés de selecció de personal de l'Ajuntament de Sitges?
model-index:
- name: BGE SITGES CAT
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.12679425837320574
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21291866028708134
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.30861244019138756
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.49521531100478466
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12679425837320574
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07097288676236044
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06172248803827751
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.049521531100478466
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12679425837320574
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21291866028708134
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30861244019138756
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49521531100478466
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.27514703200596163
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20944786207944124
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23684652150885108
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.11961722488038277
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.20574162679425836
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.31100478468899523
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.49760765550239233
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11961722488038277
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.06858054226475278
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06220095693779904
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04976076555023923
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11961722488038277
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.20574162679425836
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.31100478468899523
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49760765550239233
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2725409285822112
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2052479684058634
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23218215402287107
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.12440191387559808
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.215311004784689
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.33014354066985646
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5047846889952153
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.12440191387559808
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07177033492822966
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.0660287081339713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.050478468899521525
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12440191387559808
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.215311004784689
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.33014354066985646
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5047846889952153
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2802134368260993
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21296422875370263
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23912050845024263
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.11961722488038277
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.23205741626794257
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32057416267942584
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.47607655502392343
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11961722488038277
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07735247208931419
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06411483253588517
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04760765550239234
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11961722488038277
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23205741626794257
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32057416267942584
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.47607655502392343
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2689946292721634
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20637104123946248
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23511603125214608
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.11961722488038277
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21770334928229665
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3253588516746411
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11961722488038277
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07256778309409888
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06507177033492824
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.049999999999999996
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11961722488038277
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21770334928229665
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3253588516746411
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2754707963170229
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20811498443077409
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23411435647414974
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.1291866028708134
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21291866028708134
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32057416267942584
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.48086124401913877
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1291866028708134
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07097288676236044
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06411483253588518
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04808612440191388
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1291866028708134
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21291866028708134
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32057416267942584
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.48086124401913877
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2704775725936489
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20746753246753263
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23395020532132502
name: Cosine Map@100
---
# BGE SITGES CAT
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** ca
- **License:** apache-2.0
### 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/SITGES-BAAI3")
# Run inference
sentences = [
"Cal revisar la informació i els terminis de la convocatòria específica de cada procés que trobareu a la Seu electrònica de l'Ajuntament de Sitges.",
"On es pot trobar la informació sobre els terminis de presentació d'al·legacions en un procés de selecció de personal de l'Ajuntament de Sitges?",
"Quin és el document que es necessita per acreditar l'any de construcció i l'adequació a la legalitat urbanística d'un immoble?",
]
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 | 0.1268 |
| cosine_accuracy@3 | 0.2129 |
| cosine_accuracy@5 | 0.3086 |
| cosine_accuracy@10 | 0.4952 |
| cosine_precision@1 | 0.1268 |
| cosine_precision@3 | 0.071 |
| cosine_precision@5 | 0.0617 |
| cosine_precision@10 | 0.0495 |
| cosine_recall@1 | 0.1268 |
| cosine_recall@3 | 0.2129 |
| cosine_recall@5 | 0.3086 |
| cosine_recall@10 | 0.4952 |
| cosine_ndcg@10 | 0.2751 |
| cosine_mrr@10 | 0.2094 |
| **cosine_map@100** | **0.2368** |
#### 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 | 0.1196 |
| cosine_accuracy@3 | 0.2057 |
| cosine_accuracy@5 | 0.311 |
| cosine_accuracy@10 | 0.4976 |
| cosine_precision@1 | 0.1196 |
| cosine_precision@3 | 0.0686 |
| cosine_precision@5 | 0.0622 |
| cosine_precision@10 | 0.0498 |
| cosine_recall@1 | 0.1196 |
| cosine_recall@3 | 0.2057 |
| cosine_recall@5 | 0.311 |
| cosine_recall@10 | 0.4976 |
| cosine_ndcg@10 | 0.2725 |
| cosine_mrr@10 | 0.2052 |
| **cosine_map@100** | **0.2322** |
#### 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 | 0.1244 |
| cosine_accuracy@3 | 0.2153 |
| cosine_accuracy@5 | 0.3301 |
| cosine_accuracy@10 | 0.5048 |
| cosine_precision@1 | 0.1244 |
| cosine_precision@3 | 0.0718 |
| cosine_precision@5 | 0.066 |
| cosine_precision@10 | 0.0505 |
| cosine_recall@1 | 0.1244 |
| cosine_recall@3 | 0.2153 |
| cosine_recall@5 | 0.3301 |
| cosine_recall@10 | 0.5048 |
| cosine_ndcg@10 | 0.2802 |
| cosine_mrr@10 | 0.213 |
| **cosine_map@100** | **0.2391** |
#### 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 | 0.1196 |
| cosine_accuracy@3 | 0.2321 |
| cosine_accuracy@5 | 0.3206 |
| cosine_accuracy@10 | 0.4761 |
| cosine_precision@1 | 0.1196 |
| cosine_precision@3 | 0.0774 |
| cosine_precision@5 | 0.0641 |
| cosine_precision@10 | 0.0476 |
| cosine_recall@1 | 0.1196 |
| cosine_recall@3 | 0.2321 |
| cosine_recall@5 | 0.3206 |
| cosine_recall@10 | 0.4761 |
| cosine_ndcg@10 | 0.269 |
| cosine_mrr@10 | 0.2064 |
| **cosine_map@100** | **0.2351** |
#### 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 | 0.1196 |
| cosine_accuracy@3 | 0.2177 |
| cosine_accuracy@5 | 0.3254 |
| cosine_accuracy@10 | 0.5 |
| cosine_precision@1 | 0.1196 |
| cosine_precision@3 | 0.0726 |
| cosine_precision@5 | 0.0651 |
| cosine_precision@10 | 0.05 |
| cosine_recall@1 | 0.1196 |
| cosine_recall@3 | 0.2177 |
| cosine_recall@5 | 0.3254 |
| cosine_recall@10 | 0.5 |
| cosine_ndcg@10 | 0.2755 |
| cosine_mrr@10 | 0.2081 |
| **cosine_map@100** | **0.2341** |
#### 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 | 0.1292 |
| cosine_accuracy@3 | 0.2129 |
| cosine_accuracy@5 | 0.3206 |
| cosine_accuracy@10 | 0.4809 |
| cosine_precision@1 | 0.1292 |
| cosine_precision@3 | 0.071 |
| cosine_precision@5 | 0.0641 |
| cosine_precision@10 | 0.0481 |
| cosine_recall@1 | 0.1292 |
| cosine_recall@3 | 0.2129 |
| cosine_recall@5 | 0.3206 |
| cosine_recall@10 | 0.4809 |
| cosine_ndcg@10 | 0.2705 |
| cosine_mrr@10 | 0.2075 |
| **cosine_map@100** | **0.234** |
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## Training Details
### 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`: 6
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | 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 |
|:----------:|:------:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3404 | 5 | 3.3256 | - | - | - | - | - | - | - |
| 0.6809 | 10 | 2.2115 | - | - | - | - | - | - | - |
| 0.9532 | 14 | - | 1.2963 | 0.2260 | 0.2148 | 0.2144 | 0.2258 | 0.2069 | 0.2252 |
| 1.0213 | 15 | 1.7921 | - | - | - | - | - | - | - |
| 1.3617 | 20 | 1.2295 | - | - | - | - | - | - | - |
| 1.7021 | 25 | 0.9048 | - | - | - | - | - | - | - |
| 1.9745 | 29 | - | 0.8667 | 0.2311 | 0.2267 | 0.2292 | 0.2279 | 0.2121 | 0.2278 |
| 2.0426 | 30 | 0.7256 | - | - | - | - | - | - | - |
| 2.3830 | 35 | 0.5252 | - | - | - | - | - | - | - |
| 2.7234 | 40 | 0.4648 | - | - | - | - | - | - | - |
| **2.9957** | **44** | **-** | **0.692** | **0.2311** | **0.2243** | **0.2332** | **0.2319** | **0.2211** | **0.2354** |
| 3.0638 | 45 | 0.3518 | - | - | - | - | - | - | - |
| 3.4043 | 50 | 0.321 | - | - | - | - | - | - | - |
| 3.7447 | 55 | 0.2923 | - | - | - | - | - | - | - |
| 3.9489 | 58 | - | 0.6514 | 0.2343 | 0.2210 | 0.2293 | 0.2338 | 0.2242 | 0.2331 |
| 4.0851 | 60 | 0.2522 | - | - | - | - | - | - | - |
| 4.4255 | 65 | 0.2445 | - | - | - | - | - | - | - |
| 4.7660 | 70 | 0.2358 | - | - | - | - | - | - | - |
| 4.9702 | 73 | - | 0.6481 | 0.2348 | 0.2239 | 0.2252 | 0.2332 | 0.2167 | 0.2298 |
| 5.1064 | 75 | 0.2301 | - | - | - | - | - | - | - |
| 5.4468 | 80 | 0.2262 | - | - | - | - | - | - | - |
| 5.7191 | 84 | - | 0.6460 | 0.2430 | 0.2308 | 0.2343 | 0.2408 | 0.2212 | 0.2378 |
| 0.3404 | 5 | 0.1585 | - | - | - | - | - | - | - |
| 0.6809 | 10 | 0.1465 | - | - | - | - | - | - | - |
| 0.9532 | 14 | - | 0.6325 | 0.2407 | 0.2255 | 0.2328 | 0.2333 | 0.2266 | 0.2429 |
| 1.0213 | 15 | 0.1411 | - | - | - | - | - | - | - |
| 1.3617 | 20 | 0.079 | - | - | - | - | - | - | - |
| 1.7021 | 25 | 0.1159 | - | - | - | - | - | - | - |
| 1.9745 | 29 | - | 0.6772 | 0.2361 | 0.2287 | 0.2252 | 0.2325 | 0.2228 | 0.2387 |
| 2.0426 | 30 | 0.0838 | - | - | - | - | - | - | - |
| 2.3830 | 35 | 0.0647 | - | - | - | - | - | - | - |
| 2.7234 | 40 | 0.0752 | - | - | - | - | - | - | - |
| **2.9957** | **44** | **-** | **0.6668** | **0.2304** | **0.2354** | **0.2304** | **0.2344** | **0.2155** | **0.2321** |
| 3.0638 | 45 | 0.0706 | - | - | - | - | - | - | - |
| 3.4043 | 50 | 0.0478 | - | - | - | - | - | - | - |
| 3.7447 | 55 | 0.0768 | - | - | - | - | - | - | - |
| 3.9489 | 58 | - | 0.6040 | 0.2318 | 0.2293 | 0.2292 | 0.2305 | 0.2165 | 0.2264 |
| 4.0851 | 60 | 0.0793 | - | - | - | - | - | - | - |
| 4.4255 | 65 | 0.0559 | - | - | - | - | - | - | - |
| 4.7660 | 70 | 0.0654 | - | - | - | - | - | - | - |
| 4.9702 | 73 | - | 0.6105 | 0.2328 | 0.2328 | 0.2313 | 0.2364 | 0.2279 | 0.2320 |
| 5.1064 | 75 | 0.0734 | - | - | - | - | - | - | - |
| 5.4468 | 80 | 0.0616 | - | - | - | - | - | - | - |
| 5.7191 | 84 | - | 0.6107 | 0.2368 | 0.2341 | 0.2351 | 0.2391 | 0.2340 | 0.2322 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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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