This is a sentence-transformers model finetuned from neuralmind/bert-base-portuguese-cased. 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: neuralmind/bert-base-portuguese-cased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
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: BertModel 
  (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("wilsonmarciliojr/bertimbau-embed-hard-neg")
# Run inference
sentences = [
    'O 1.º troféu disputado em Portugal foi ganho pelo Sporting e o Sporting é líder do campeonato com o FC Porto .',
    'O primeiro troféu que se disputou em Portugal foi ganho pelo Sporting.',
    'Alexandre Pato recebeu em posição legal, fez o gol, mas o impedimento foi marcado.',
]
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

Semantic Similarity

Datasets: sts-dev and sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	sts-dev	sts-test
pearson_cosine	0.797	0.756
spearman_cosine	0.7938	0.7401

Training Details

Training Dataset

Unnamed Dataset

Size: 26,156 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 13 tokens
mean: 24.92 tokens
max: 43 tokens
min: 10 tokens
mean: 18.61 tokens
max: 33 tokens
min: 9 tokens
mean: 18.79 tokens
max: 39 tokens

Samples:

anchor	positive	negative
`Quatro jovens foram assassinados na madrugada de hoje (19) em Carapicuíba, município da região metropolitana de São Paulo.`	`Quatro jovens foram assassinados em Carapicuíba.`	`O enterro ocorreu no Cemitério Municipal de Carapicuíba.`
`Quatro jovens foram assassinados na madrugada de hoje (19) em Carapicuíba, município da região metropolitana de São Paulo.`	`Quatro jovens foram assassinados em Carapicuíba.`	`Esta madrugada (14) foi coroada a nova Miss EUA.`
`Quatro jovens foram assassinados na madrugada de hoje (19) em Carapicuíba, município da região metropolitana de São Paulo.`	`Quatro jovens foram assassinados em Carapicuíba.`	`Há alguns de focos de incêndio na Região Metropolitana de Manaus.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 5,520 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 15 tokens
mean: 25.03 tokens
max: 47 tokens
min: 9 tokens
mean: 20.03 tokens
max: 40 tokens
min: 9 tokens
mean: 19.4 tokens
max: 40 tokens

Samples:

anchor	positive	negative
`Um novo rumor direto da Coréia do Sul nos dá uma ideia do material que será usado no próximo Galaxy S7, que será anunciado oficialmente em janeiro de 2016.`	`O novo Galaxy S7 deverá ser anunciado oficialmente em janeiro de 2016.`	`Comparado com o Galaxy S6 da Samsung, a diferença na bateria é muito grande.`
`Um novo rumor direto da Coréia do Sul nos dá uma ideia do material que será usado no próximo Galaxy S7, que será anunciado oficialmente em janeiro de 2016.`	`O novo Galaxy S7 deverá ser anunciado oficialmente em janeiro de 2016.`	`Teremos um smartphone criado pela grande empresa de refrigerante Pepsi.`
`Um novo rumor direto da Coréia do Sul nos dá uma ideia do material que será usado no próximo Galaxy S7, que será anunciado oficialmente em janeiro de 2016.`	`O novo Galaxy S7 deverá ser anunciado oficialmente em janeiro de 2016.`	`Recorde-se que a irmã de Kim Kardashian e o companheiro se separaram no passado mês de julho, depois de nove anos juntos.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 220
per_device_eval_batch_size: 220
num_train_epochs: 5
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 220
per_device_eval_batch_size: 220
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_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: 5
max_steps: -1
lr_scheduler_type: linear
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: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
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: False
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}
tp_size: 0
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
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
-1	-1	-	-	0.6424	-
0.0840	10	-	0.1726	0.6642	-
0.1681	20	-	0.0523	0.7141	-
0.2521	30	-	0.0242	0.7580	-
0.3361	40	-	0.0160	0.7759	-
0.4202	50	-	0.0100	0.7848	-
0.5042	60	-	0.0069	0.7893	-
0.5882	70	-	0.0059	0.7904	-
0.6723	80	-	0.0059	0.7907	-
0.7563	90	-	0.0053	0.7908	-
0.8403	100	0.1681	0.0049	0.7921	-
0.9244	110	-	0.0049	0.7925	-
1.0084	120	-	0.0049	0.7929	-
1.0924	130	-	0.0050	0.7925	-
1.1765	140	-	0.0053	0.7922	-
1.2605	150	-	0.0052	0.7919	-
1.3445	160	-	0.0048	0.7922	-
1.4286	170	-	0.0046	0.7923	-
1.5126	180	-	0.0045	0.7928	-
1.5966	190	-	0.0045	0.7932	-
1.6807	200	0.0013	0.0047	0.7933	-
1.7647	210	-	0.0047	0.7929	-
1.8487	220	-	0.0047	0.7928	-
1.9328	230	-	0.0047	0.7928	-
2.0168	240	-	0.0046	0.7926	-
2.1008	250	-	0.0047	0.7927	-
2.1849	260	-	0.0047	0.7927	-
2.2689	270	-	0.0047	0.7929	-
2.3529	280	-	0.0045	0.7933	-
2.4370	290	-	0.0045	0.7934	-
2.5210	300	0.0007	0.0045	0.7932	-
2.6050	310	-	0.0045	0.7933	-
2.6891	320	-	0.0046	0.7932	-
2.7731	330	-	0.0046	0.7932	-
2.8571	340	-	0.0046	0.7933	-
2.9412	350	-	0.0047	0.7934	-
3.0252	360	-	0.0047	0.7934	-
3.1092	370	-	0.0046	0.7935	-
3.1933	380	-	0.0046	0.7936	-
3.2773	390	-	0.0047	0.7937	-
3.3613	400	0.0005	0.0046	0.7937	-
3.4454	410	-	0.0046	0.7937	-
3.5294	420	-	0.0046	0.7937	-
3.6134	430	-	0.0046	0.7937	-
3.6975	440	-	0.0046	0.7938	-
3.7815	450	-	0.0046	0.7938	-
3.8655	460	-	0.0047	0.7939	-
3.9496	470	-	0.0046	0.7940	-
4.0336	480	-	0.0046	0.7940	-
4.1176	490	-	0.0046	0.7940	-
4.2017	500	0.0005	0.0046	0.7940	-
4.2857	510	-	0.0046	0.7939	-
4.3697	520	-	0.0046	0.7938	-
4.4538	530	-	0.0046	0.7938	-
4.5378	540	-	0.0046	0.7938	-
4.6218	550	-	0.0046	0.7939	-
4.7059	560	-	0.0046	0.7939	-
4.7899	570	-	0.0046	0.7938	-
4.8739	580	-	0.0046	0.7938	-
4.9580	590	-	0.0046	0.7938	-
-1	-1	-	-	-	0.7401

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}

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}
}

	anchor	positive	negative
type	string	string	string
details	min: 13 tokens mean: 24.92 tokens max: 43 tokens	min: 10 tokens mean: 18.61 tokens max: 33 tokens	min: 9 tokens mean: 18.79 tokens max: 39 tokens