Multilingual E5 large Legal Spanish
This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the small-spanish-legal-dataset dataset. 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: intfloat/multilingual-e5-large
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: es
- License: apache-2.0
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
(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})
(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("wilfredomartel/multilingual-e5-large-es-legal-v2")
# Run inference
queries = [
"passage: La Sala de Admisi\u00f3n de la Corte Constitucional inadmiti\u00f3 la acci\u00f3n extraordinaria de protecci\u00f3n interpuesta por el Ec. Guillermo Antonio Quezada Ter\u00e1n, representante legal de TRIPLEORO CEM., debido a una serie de deficiencias sustanciales y formales identificadas en su petici\u00f3n. En primer lugar, la Sala determin\u00f3 que el accionante no logr\u00f3 justificar la relevancia constitucional del conflicto planteado. El fundamento de su acci\u00f3n se centr\u00f3 en evidenciar lo que consideraba improcedente y equivocado de la sentencia de la Segunda Sala de lo Laboral de la Corte Nacional de Justicia, argumentando que se atentaba contra los intereses econ\u00f3micos de su representada y del cant\u00f3n Machala al imponer el pago de sumas que, a su juicio, no correspond\u00edan legal ni justamente. Este tipo de argumentaci\u00f3n, centrada en la legalidad y los intereses econ\u00f3micos, no es el objeto principal de la acci\u00f3n extraordinaria de protecci\u00f3n, la cual est\u00e1 dise\u00f1ada para salvaguardar derechos constitucionales y el debido proceso, no para reexaminar la correcta aplicaci\u00f3n de leyes ordinarias o la valoraci\u00f3n de pruebas desde una perspectiva econ\u00f3mica. Adicionalmente, la Sala constat\u00f3 una falta de precisi\u00f3n en la identificaci\u00f3n del acto materia de impugnaci\u00f3n. Si bien la demanda inicialmente se dirig\u00eda contra la sentencia de 30 de noviembre de 2009, emitida dentro del recurso de casaci\u00f3n No. 128-2009, en la secci\u00f3n de pretensiones se solicitaba la nulidad de todo lo actuado desde la sentencia dictada por el Juez Primero Ocasional del Trabajo de El Oro, dentro del juicio laboral No. 16-2006. Esta imprecisi\u00f3n genera incertidumbre sobre el alcance de la impugnaci\u00f3n y el acto espec\u00edfico que supuestamente vulner\u00f3 derechos constitucionales, dificultando el an\u00e1lisis de procedibilidad.",
]
documents = [
"query: What specific procedural and substantive deficiencies led the Constitutional Court's Sala de Admisión to inadmit the extraordinary protection action filed by Ec. Guillermo Antonio Quezada Terán, representing TRIPLEORO CEM., against the National Court of Justice's labor ruling?",
'query: ¿Cuál fue el motivo principal por el cual la Sala de Admisión de la Corte Constitucional inadmitió a trámite la acción extraordinaria de protección N° 0377-19-EP, presentada por el Ministerio de Educación?',
'query: ¿Cuál fue la razón principal por la que la Sala de Admisión de la Corte Constitucional inadmitió a trámite la acción extraordinaria de protección No. 0230-10-EP presentada por Juan Edmundo Castillo Salas?',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.7951, 0.2287, 0.1933]])
Evaluation
Metrics
Information Retrieval
Information Retrieval
Information Retrieval
Training Details
Training Dataset
small-spanish-legal-dataset
- Dataset: small-spanish-legal-dataset at f7d3e93
- Size: 7,872 training samples
- Columns:
passage and query
- Approximate statistics based on the first 1000 samples:
- Samples:
- Loss:
MatryoshkaLoss with these parameters:{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
Evaluation Dataset
small-spanish-legal-dataset
- Dataset: small-spanish-legal-dataset at f7d3e93
- Size: 3,374 evaluation samples
- Columns:
passage and query
- Approximate statistics based on the first 1000 samples:
- Samples:
- Loss:
MatryoshkaLoss with these parameters:{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
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: 4
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
torch_empty_cache_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: 4
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: 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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}
Training Logs
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.1.0
- Transformers: 4.55.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.10.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
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}
}