This is a sentence-transformers model finetuned from PlanTL-GOB-ES/roberta-base-bne. It maps sentences & paragraphs to a 768-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: PlanTL-GOB-ES/roberta-base-bne
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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/sitges10242608-4ep-rerankv3-sp")
# Run inference
sentences = [
    'Els establiments locals tenen un paper clau en el projecte de la targeta de fidelització, ja que són els que ofereixen descomptes i ofertes especials als consumidors que utilitzen la targeta.',
    'Quin és el paper dels establiments locals en el projecte de la targeta de fidelització?',
    "Quins són els tractaments que beneficien la salut de l'empleat municipal que s'inclouen en l'ajuda?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0517
cosine_accuracy@3	0.125
cosine_accuracy@5	0.1875
cosine_accuracy@10	0.3858
cosine_precision@1	0.0517
cosine_precision@3	0.0417
cosine_precision@5	0.0375
cosine_precision@10	0.0386
cosine_recall@1	0.0517
cosine_recall@3	0.125
cosine_recall@5	0.1875
cosine_recall@10	0.3858
cosine_ndcg@10	0.1821
cosine_mrr@10	0.122
cosine_map@100	0.1462

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0453
cosine_accuracy@3	0.1142
cosine_accuracy@5	0.181
cosine_accuracy@10	0.3815
cosine_precision@1	0.0453
cosine_precision@3	0.0381
cosine_precision@5	0.0362
cosine_precision@10	0.0381
cosine_recall@1	0.0453
cosine_recall@3	0.1142
cosine_recall@5	0.181
cosine_recall@10	0.3815
cosine_ndcg@10	0.1753
cosine_mrr@10	0.1144
cosine_map@100	0.139

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0474
cosine_accuracy@3	0.1228
cosine_accuracy@5	0.2004
cosine_accuracy@10	0.3987
cosine_precision@1	0.0474
cosine_precision@3	0.0409
cosine_precision@5	0.0401
cosine_precision@10	0.0399
cosine_recall@1	0.0474
cosine_recall@3	0.1228
cosine_recall@5	0.2004
cosine_recall@10	0.3987
cosine_ndcg@10	0.1851
cosine_mrr@10	0.1217
cosine_map@100	0.1457

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0453
cosine_accuracy@3	0.1164
cosine_accuracy@5	0.1681
cosine_accuracy@10	0.3815
cosine_precision@1	0.0453
cosine_precision@3	0.0388
cosine_precision@5	0.0336
cosine_precision@10	0.0381
cosine_recall@1	0.0453
cosine_recall@3	0.1164
cosine_recall@5	0.1681
cosine_recall@10	0.3815
cosine_ndcg@10	0.1753
cosine_mrr@10	0.1146
cosine_map@100	0.1393

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0388
cosine_accuracy@3	0.097
cosine_accuracy@5	0.153
cosine_accuracy@10	0.347
cosine_precision@1	0.0388
cosine_precision@3	0.0323
cosine_precision@5	0.0306
cosine_precision@10	0.0347
cosine_recall@1	0.0388
cosine_recall@3	0.097
cosine_recall@5	0.153
cosine_recall@10	0.347
cosine_ndcg@10	0.1569
cosine_mrr@10	0.1011
cosine_map@100	0.1268

Training Details

Training Dataset

Unnamed Dataset

Size: 4,173 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 10 tokens
mean: 60.84 tokens
max: 206 tokens
min: 10 tokens
mean: 25.34 tokens
max: 53 tokens

Samples:

positive	anchor
`L'objectiu principal de la persona coordinadora de colònia felina és garantir el benestar dels animals de la colònia.`	`Quin és l'objectiu principal de la persona coordinadora de colònia felina?`
`Es tracta d'una sala amb capacitat per a 125 persones, equipada amb un petit escenari, sistema de sonorització, pantalla per a projeccions, camerins i serveis higiènics (WC).`	`Quin és el nombre de persones que pot acollir la sala d'actes del Casal Municipal de la Gent Gran de Sitges?`
`Aquest ajut pretén fomentar l’associacionisme empresarial local, per tal de disposar d’agrupacions, gremis o associacions representatives de l’activitat empresarial del municipi.`	`Quin és el paper de les empreses en aquest ajut?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.2
bf16: True
tf32: False
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 5e-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: 4
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: False
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

Training Logs

Epoch	Step	Training Loss	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
0.6130	10	10.8464	-	-	-	-	-
0.9808	16	-	0.1060	0.1088	0.1067	0.0984	0.1074
1.2261	20	3.5261	-	-	-	-	-
1.8391	30	1.4363	-	-	-	-	-
1.9617	32	-	0.1406	0.1468	0.1356	0.1395	0.1373
2.4521	40	0.5627	-	-	-	-	-
2.9425	48	-	0.1377	0.1418	0.1427	0.1322	0.1437
3.0651	50	0.2727	-	-	-	-	-
3.6782	60	0.1297	-	-	-	-	-
3.9234	64	-	0.1393	0.1457	0.139	0.1268	0.1462

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.4.0+cu121
Accelerate: 0.34.0.dev0
Datasets: 2.21.0
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}
}

	positive	anchor
type	string	string
details	min: 10 tokens mean: 60.84 tokens max: 206 tokens	min: 10 tokens mean: 25.34 tokens max: 53 tokens