fabricioalmeida/BumbaLM-Embedding-4B-v0.1

BumbaLM-Embedding-4B-v0.1 (Jurídico Brasileiro)

Este modelo é uma versão fine-tuned do Qwen/Qwen3-Embedding-4B, especializado no domínio jurídico brasileiro. Ele foi desenvolvido como parte de uma pesquisa de doutorado focada no Enriquecimento de Embeddings Neurais para a Linguagem Jurídica Brasileira.

O BumbaLM-Embedding mapeia sentenças e parágrafos (como peças jurídicas, jurisprudência e consultas processuais) para um espaço vetorial denso de 2560 dimensões, sendo ideal para tarefas de:

• —Recuperação de Informação Legal (Legal IR)
• —Busca Semântica em processos jurídicos(TJMA e outros tribunais)
• —Aplicações de RAG (Retrieval-Augmented Generation) para Direito

Model Description

• —Model Type: Sentence Transformer
• —Base model: Qwen3-Embedding-4B -->
• —Maximum Sequence Length: 1024 tokens
• —Output Dimensionality: 2560 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset: Legal Documents
• —Language: Portuguese
• —License: Unknown

Métricas de Avaliação

O modelo foi avaliado em um conjunto de teste reservado ("bumbatesteval") composto por pares de consultas e trechos jurídicos reais.

Nota: O modelo base foi refinado utilizando a função de perda TripletLoss, focando na distinção entre passagens juridicamente relevantes e "negativos difíceis" (trechos com termos similares mas semanticamente incorretos para a consulta).

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': 1024, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, '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': True, '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("fabricioalmeida/BumbaLM-Embedding-4B-v0.1")
# Run inference
frases = [
    "O réu apresentou habeas corpus preventivo.",
    "A jurisprudência do STJ é pacífica nesse sentido.",
    "Receita de bolo de cenoura com chocolate."
]

embeddings = model.encode(frases)
print(embeddings.shape)
# [3, 2560]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.2075, 0.2365],
#        [0.2075, 1.0000, 0.1745],
#        [0.2365, 0.1745, 1.0000]])

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Evaluation

Metrics

Information Retrieval

• —Dataset: bumba_test_eval
• —Evaluated with <code>InformationRetrievalEvaluator</code>

Training Dataset

Legal Dataset

• —Size: 19,500 training samples
• —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>sentence_2</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | sentence_2 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 31 tokens</li><li>mean: 47.57 tokens</li><li>max: 92 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 242.04 tokens</li><li>max: 463 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 224.16 tokens</li><li>max: 457 tokens</li></ul> |

• —Loss: <code>TripletLoss</code> with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }

Training Details

Training Hyperparameters

• —eval_strategy: steps
• —num_train_epochs: 1
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —prediction_loss_only: True
• —per_device_train_batch_size: 8
• —per_device_eval_batch_size: 8
• —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
• —num_train_epochs: 1
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.0
• —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
• —bf16: False
• —fp16: False
• —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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —project: huggingface
• —trackio_space_id: trackio
• —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
• —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: no
• —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: True
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: round_robin
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• —Python: 3.12.12
• —Sentence Transformers: 5.1.2
• —Transformers: 4.57.2
• —PyTorch: 2.9.0+cu126
• —Accelerate: 1.12.0
• —Datasets: 4.4.1
• —Tokenizers: 0.22.1

Citation

BibTeX

@misc{bumbalm2025,
    title={BumbaLM-Embeddings: Enriquecimento de Embeddings Neurais para a Linguagem Jurídica Brasileira},
    author={Almeida, Fabrício},
    year={2025},
    description={Modelo de embedding fine-tuned para o domínio jurídico brasileiro (TJMA).}
}

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