stjiris_tjgo_diacde_sts / README.md

DIACDE

Update README.md

d2c2738 verified 5 months ago

preview code

raw

history blame contribute delete

16.4 kB

metadata

license: cc-by-nc-4.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - loss:CosineSimilarityLoss
base_model: stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0
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
      stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev dot
          type: sts-dev-dot
        metrics:
          - type: pearson_dot
            value: 0.6125529066567547
            name: Pearson Dot
          - type: spearman_dot
            value: 0.607020920491597
            name: Spearman Dot
          - type: pearson_dot
            value: 0.6151741779356057
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6095317749105116
            name: Spearman Dot
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev euclidian
          type: sts-dev-euclidian
        metrics:
          - type: pearson_euclidean
            value: 0.7076748166600304
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7205880822002616
            name: Spearman Euclidean
          - type: pearson_euclidean
            value: 0.7099832358841494
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7216899339827408
            name: Spearman Euclidean
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev manhattan
          type: sts-dev-manhattan
        metrics:
          - type: pearson_manhattan
            value: 0.706930206993536
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7197955970878462
            name: Spearman Manhattan
          - type: pearson_manhattan
            value: 0.7092200936299493
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7209197353975371
            name: Spearman Manhattan
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev cosine
          type: sts-dev-cosine
        metrics:
          - type: pearson_cosine
            value: 0.7244468292859898
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7251349738474332
            name: Spearman Cosine
          - type: pearson_cosine
            value: 0.7253818539410067
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7209886641359866
            name: Spearman Cosine

SentenceTransformer based on stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0

This is a sentence-transformers model finetuned from stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0. 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: stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1.0
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Language: Portuguese

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	0.6126
spearman_dot	0.607

Semantic Similarity

Dataset: sts-dev-euclidian
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_euclidean	0.7077
spearman_euclidean	0.7206

Semantic Similarity

Dataset: sts-dev-manhattan
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_manhattan	0.7069
spearman_manhattan	0.7198

Semantic Similarity

Dataset: sts-dev-cosine
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7244
spearman_cosine	0.7251

Semantic Similarity

Dataset: sts-dev-dot
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_dot	0.6152
spearman_dot	0.6095

Semantic Similarity

Dataset: sts-dev-euclidian
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_euclidean	0.71
spearman_euclidean	0.7217

Semantic Similarity

Dataset: sts-dev-manhattan
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_manhattan	0.7092
spearman_manhattan	0.7209

Semantic Similarity

Dataset: sts-dev-cosine
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7254
spearman_cosine	0.721

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 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: 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",
}

STJ IRIS

@InProceedings{MeloSemantic,
  author="Melo, Rui
  and Santos, Pedro A.
  and Dias, Jo{\~a}o",
  editor="Moniz, Nuno
  and Vale, Zita
  and Cascalho, Jos{\'e}
  and Silva, Catarina
  and Sebasti{\~a}o, Raquel",
  title="A Semantic Search System for the Supremo Tribunal de Justi{\c{c}}a",
  booktitle="Progress in Artificial Intelligence",
  year="2023",
  publisher="Springer Nature Switzerland",
  address="Cham",
  pages="142--154",
  abstract="Many information retrieval systems use lexical approaches to retrieve information. Such approaches have multiple limitations, and these constraints are exacerbated when tied to specific domains, such as the legal one. Large language models, such as BERT, deeply understand a language and may overcome the limitations of older methodologies, such as BM25. This work investigated and developed a prototype of a Semantic Search System to assist the Supremo Tribunal de Justi{\c{c}}a (Portuguese Supreme Court of Justice) in its decision-making process. We built a Semantic Search System that uses specially trained BERT models (Legal-BERTimbau variants) and a Hybrid Search System that incorporates both lexical and semantic techniques by combining the capabilities of BM25 and the potential of Legal-BERTimbau. In this context, we obtained a {\$}{\$}335{\backslash}{\%}{\$}{\$}335{\%}increase on the discovery metric when compared to BM25 for the first query result. This work also provides information on the most relevant techniques for training a Large Language Model adapted to Portuguese jurisprudence and introduces a new technique of Metadata Knowledge Distillation.",
  isbn="978-3-031-49011-8"
}

@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}
}

@inproceedings{fonseca2016assin,
  title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
  author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
  booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
  pages={13--15},
  year={2016}
}

@inproceedings{real2020assin,
  title={The assin 2 shared task: a quick overview},
  author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
  booktitle={International Conference on Computational Processing of the Portuguese Language},
  pages={406--412},
  year={2020},
  organization={Springer}
}
@InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
}