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DIACDE

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metadata

license: cc-by-nc-4.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - loss:CosineSimilarityLoss
base_model: neuralmind/bert-large-portuguese-cased
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_dot
  - spearman_dot
  - pearson_euclidean
  - spearman_euclidean
  - pearson_manhattan
  - spearman_manhattan
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on neuralmind/bert-large-portuguese-cased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev dot
          type: sts-dev-dot
        metrics:
          - type: pearson_dot
            value: 0.18794469022476173
            name: Pearson Dot
          - type: spearman_dot
            value: 0.17330642951334718
            name: Spearman Dot
          - type: pearson_dot
            value: 0.18794469022476173
            name: Pearson Dot
          - type: spearman_dot
            value: 0.17330642951334718
            name: Spearman Dot
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev euclidian
          type: sts-dev-euclidian
        metrics:
          - type: pearson_euclidean
            value: 0.6901184462009367
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6918422814802583
            name: Spearman Euclidean
          - type: pearson_euclidean
            value: 0.6901184462009367
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6918422814802583
            name: Spearman Euclidean
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev manhattan
          type: sts-dev-manhattan
        metrics:
          - type: pearson_manhattan
            value: 0.6894045916517573
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6915146514062644
            name: Spearman Manhattan
          - type: pearson_manhattan
            value: 0.6894045916517573
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6915146514062644
            name: Spearman Manhattan
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev cosine
          type: sts-dev-cosine
        metrics:
          - type: pearson_cosine
            value: 0.6697919427315735
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6927776866317362
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.6697919427315735
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6927776866317362
            name: Spearman Cosine

SentenceTransformer based on neuralmind/bert-large-portuguese-cased

This is a sentence-transformers model finetuned from neuralmind/bert-large-portuguese-cased. 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: neuralmind/bert-large-portuguese-cased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
            "o autor possuía..., ",
            "a parte autora é servidor pública...",
            "a parte autora é..."
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.8019],
#         [1.0000, 1.0000, 0.8019],
#         [0.8019, 0.8019, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev-dot
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_dot	0.1879
spearman_dot	0.1733

Semantic Similarity

Dataset: sts-dev-euclidian
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_euclidean	0.6901
spearman_euclidean	0.6918

Semantic Similarity

Dataset: sts-dev-manhattan
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_manhattan	0.6894
spearman_manhattan	0.6915

Semantic Similarity

Dataset: sts-dev-cosine
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6698
spearman_cosine	0.6928

Semantic Similarity

Dataset: sts-dev-dot
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_dot	0.1879
spearman_dot	0.1733

Semantic Similarity

Dataset: sts-dev-euclidian
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_euclidean	0.6901
spearman_euclidean	0.6918

Semantic Similarity

Dataset: sts-dev-manhattan
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_manhattan	0.6894
spearman_manhattan	0.6915

Semantic Similarity

Dataset: sts-dev-cosine
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6698
spearman_cosine	0.6928

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 1e-05
num_train_epochs: 4
warmup_ratio: 0.1
fp16: True
resume_from_checkpoint: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-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: 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}
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: True
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Framework Versions

Python: 3.10.12
Sentence Transformers: 5.0.0
Transformers: 4.53.3
PyTorch: 2.7.1+cu126
Accelerate: 1.9.0
Datasets: 3.6.0
Tokenizers: 0.21.2

Authors

Diretoria de Inteligência Artificial, Ciência de Dados e Estatística do Tribunal de Justiça do Estado de Goiás (TJGO).

Citation

BibTeX

Sentence Transformers

@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",
}

BERTIMBAU

@inproceedings{souza2020bertimbau,
  author    = {F{\'a}bio Souza and
               Rodrigo Nogueira and
               Roberto Lotufo},
  title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
  booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
  year      = {2020}
}