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dariolopez
/
bge-m3-es-legal-tmp-4

Sentence Similarity
sentence-transformers
Safetensors
Spanish
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2947
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community

BGE large Legal Spanish

This is a sentence-transformers model finetuned from BAAI/bge-m3. 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: BAAI/bge-m3
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 1024 tokens
  • Similarity Function: Cosine Similarity
  • Language: es
  • License: apache-2.0

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, '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': False, 'include_prompt': True})
  (2): Normalize()
)

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("dariolopez/bge-m3-es-legal-tmp-4")
# Run inference
sentences = [
    'Artículo 6. Definiciones. 1. Discriminación directa e indirecta. b) La discriminación indirecta se produce cuando una disposición, criterio o práctica aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja particular con respecto a otras por razón de las causas previstas en el apartado 1 del artículo 2.',
    '¿Qué se considera discriminación indirecta?',
    '¿Qué tipo de información se considera veraz?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.5427
cosine_accuracy@3 0.7988
cosine_accuracy@5 0.8384
cosine_accuracy@10 0.8872
cosine_precision@1 0.5427
cosine_precision@3 0.2663
cosine_precision@5 0.1677
cosine_precision@10 0.0887
cosine_recall@1 0.5427
cosine_recall@3 0.7988
cosine_recall@5 0.8384
cosine_recall@10 0.8872
cosine_ndcg@10 0.7233
cosine_mrr@10 0.6696
cosine_map@100 0.6746

Information Retrieval

Metric Value
cosine_accuracy@1 0.5396
cosine_accuracy@3 0.8049
cosine_accuracy@5 0.8445
cosine_accuracy@10 0.8902
cosine_precision@1 0.5396
cosine_precision@3 0.2683
cosine_precision@5 0.1689
cosine_precision@10 0.089
cosine_recall@1 0.5396
cosine_recall@3 0.8049
cosine_recall@5 0.8445
cosine_recall@10 0.8902
cosine_ndcg@10 0.7246
cosine_mrr@10 0.6702
cosine_map@100 0.6749

Information Retrieval

Metric Value
cosine_accuracy@1 0.5488
cosine_accuracy@3 0.8018
cosine_accuracy@5 0.8354
cosine_accuracy@10 0.8933
cosine_precision@1 0.5488
cosine_precision@3 0.2673
cosine_precision@5 0.1671
cosine_precision@10 0.0893
cosine_recall@1 0.5488
cosine_recall@3 0.8018
cosine_recall@5 0.8354
cosine_recall@10 0.8933
cosine_ndcg@10 0.7304
cosine_mrr@10 0.6771
cosine_map@100 0.6811

Information Retrieval

Metric Value
cosine_accuracy@1 0.5457
cosine_accuracy@3 0.7774
cosine_accuracy@5 0.8293
cosine_accuracy@10 0.872
cosine_precision@1 0.5457
cosine_precision@3 0.2591
cosine_precision@5 0.1659
cosine_precision@10 0.0872
cosine_recall@1 0.5457
cosine_recall@3 0.7774
cosine_recall@5 0.8293
cosine_recall@10 0.872
cosine_ndcg@10 0.7183
cosine_mrr@10 0.6678
cosine_map@100 0.6733

Information Retrieval

Metric Value
cosine_accuracy@1 0.5335
cosine_accuracy@3 0.7622
cosine_accuracy@5 0.814
cosine_accuracy@10 0.8659
cosine_precision@1 0.5335
cosine_precision@3 0.2541
cosine_precision@5 0.1628
cosine_precision@10 0.0866
cosine_recall@1 0.5335
cosine_recall@3 0.7622
cosine_recall@5 0.814
cosine_recall@10 0.8659
cosine_ndcg@10 0.708
cosine_mrr@10 0.6563
cosine_map@100 0.6617

Information Retrieval

Metric Value
cosine_accuracy@1 0.5122
cosine_accuracy@3 0.7317
cosine_accuracy@5 0.7896
cosine_accuracy@10 0.8659
cosine_precision@1 0.5122
cosine_precision@3 0.2439
cosine_precision@5 0.1579
cosine_precision@10 0.0866
cosine_recall@1 0.5122
cosine_recall@3 0.7317
cosine_recall@5 0.7896
cosine_recall@10 0.8659
cosine_ndcg@10 0.6908
cosine_mrr@10 0.6347
cosine_map@100 0.6394

Training Details

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • gradient_accumulation_steps: 16
  • learning_rate: 2e-05
  • num_train_epochs: 16
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • bf16: True
  • tf32: True
  • 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: 2e-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: 16
  • max_steps: -1
  • lr_scheduler_type: cosine
  • 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: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: True
  • 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 loss dim_1024_cosine_map@100 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
0.4324 5 1.6932 - - - - - - -
0.8649 10 1.1787 - - - - - - -
0.9514 11 - 0.6685 0.6708 0.6300 0.6676 0.6716 0.5560 0.6781
1.2973 15 1.0084 - - - - - - -
1.7297 20 0.5743 - - - - - - -
1.9892 23 - 0.4458 0.6734 0.6533 0.6773 0.6770 0.6174 0.6657
2.1622 25 0.4435 - - - - - - -
2.5946 30 0.2396 - - - - - - -
2.9405 34 - 0.4239 0.6749 0.6591 0.6725 0.6752 0.6188 0.6784
3.0270 35 0.1568 - - - - - - -
3.4595 40 0.1085 - - - - - - -
3.8919 45 0.0582 - - - - - - -
3.9784 46 - 0.3934 0.6820 0.6594 0.6862 0.6856 0.6293 0.6777
4.3243 50 0.0543 - - - - - - -
4.7568 55 0.0349 - - - - - - -
4.9297 57 - 0.3690 0.6747 0.6582 0.6760 0.6852 0.6375 0.6774
5.1892 60 0.03 - - - - - - -
5.6216 65 0.0228 - - - - - - -
5.9676 69 - 0.362 0.6752 0.6643 0.6784 0.6809 0.6312 0.6799
6.0541 70 0.0183 - - - - - - -
6.4865 75 0.0159 - - - - - - -
6.9189 80 0.0113 0.3608 0.6780 0.6582 0.6769 0.6785 0.6366 0.6769
7.3514 85 0.0107 - - - - - - -
7.7838 90 0.0098 - - - - - - -
7.9568 92 - 0.3307 0.6804 0.6511 0.6774 0.6823 0.6355 0.6747
8.2162 95 0.0084 - - - - - - -
8.6486 100 0.0067 - - - - - - -
8.9946 104 - 0.3387 0.6778 0.6518 0.6751 0.6787 0.6313 0.6693
9.0811 105 0.0074 - - - - - - -
9.5135 110 0.0064 - - - - - - -
9.9459 115 0.0052 0.3222 0.6776 0.6571 0.6745 0.6810 0.6397 0.6722
10.3784 120 0.0058 - - - - - - -
10.8108 125 0.0058 - - - - - - -
10.9838 127 - 0.3325 0.6760 0.6595 0.6714 0.6807 0.6399 0.6729
11.2432 130 0.0052 - - - - - - -
11.6757 135 0.0046 - - - - - - -
11.9351 138 - 0.3366 0.6770 0.6598 0.6730 0.6813 0.6360 0.6733
12.1081 140 0.0053 - - - - - - -
12.5405 145 0.0046 - - - - - - -
12.9730 150 0.0045 0.3263 0.6759 0.6599 0.6743 0.6816 0.6394 0.6759
13.4054 155 0.0044 - - - - - - -
13.8378 160 0.0043 - - - - - - -
13.9243 161 - 0.3231 0.6747 0.6593 0.6729 0.6804 0.6407 0.6746
14.2703 165 0.005 - - - - - - -
14.7027 170 0.004 - - - - - - -
14.9622 173 - 0.3238 0.6743 0.6597 0.6720 0.6828 0.6395 0.6759
15.1351 175 0.005 - - - - - - -
15.2216 176 - 0.3244 0.6746 0.6617 0.6733 0.6811 0.6394 0.6749
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.42.3
  • PyTorch: 2.2.0+cu121
  • Accelerate: 0.32.1
  • Datasets: 2.20.0
  • Tokenizers: 0.19.1

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",
}

MatryoshkaLoss 

@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 

@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}
}
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