This is a sentence-transformers model finetuned from JFernandoGRE/bert-ner-colombian-elitenames. It maps sentences & paragraphs to a 768-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: JFernandoGRE/bert-ner-colombian-elitenames
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 768, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("JFernandoGRE/gtelarge-colombian-elitenames-righttail")
# Run inference
sentences = [
    'STEVEN ANDRES SANCHEZ HERNANDEZ',
    'BRYAN MANUEL SANCHEZ HERNANDEZ',
    'JHONN JAIRO GOMEZ RAMIREZ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5568, 0.3681],
#         [0.5568, 1.0000, 0.3804],
#         [0.3681, 0.3804, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 10,629 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 4 tokens
mean: 8.5 tokens
max: 15 tokens
min: 4 tokens
mean: 8.85 tokens
max: 15 tokens
0: ~83.10%
1: ~16.90%
Samples:
sentence1 sentence2 label
JUAN CARLOS CORTES GONZALEZ JEAN CARLOS CORTES GONZALEZ 0
SERGIO ANTONIO PACHECO AHUMAITRE SERGIO ANTONIO PACHECO AUMAITRE 1
JOSÉ MAURICIO ZULUAGA TOBÓN JOSE HERNANDO ZULUAGA MARIN 0
Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 2,658 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 4 tokens
mean: 8.55 tokens
max: 13 tokens
min: 5 tokens
mean: 8.88 tokens
max: 15 tokens
0: ~81.50%
1: ~18.50%

Samples:

sentence1	sentence2	label
`YEIMIN DARIO PEREZ MOLINA`	`YESSIKA YULIETH PEREZ MOLINA`	`0`
`CARLOS HUMBERTO MARTINEZ ALZATE`	`CARLOS HUMBERTO MARTINEZ PATIÑO`	`0`
`CRISTHIAN DAVID BERMUDEZ TORRES`	`CRISTHIAN DAVID FORERO TORRES`	`0`

Loss: OnlineContrastiveLoss

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: 5
warmup_ratio: 0.182
fp16: 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: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.182
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: 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}
parallelism_config: 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
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: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.1504	100	0.3195	0.6335
0.3008	200	0.3014	0.5719
0.4511	300	0.2717	0.4428
0.6015	400	0.2156	0.2780
0.7519	500	0.1779	0.2302
0.9023	600	0.1264	0.1554
1.0526	700	0.1178	0.1374
1.2030	800	0.1093	0.0901
1.3534	900	0.1025	0.1134
1.5038	1000	0.0988	0.0862
1.6541	1100	0.0876	0.0876
1.8045	1200	0.1004	0.0781
1.9549	1300	0.0669	0.0896
2.1053	1400	0.0765	0.0745
2.2556	1500	0.0623	0.0798
2.4060	1600	0.0705	0.0671
2.5564	1700	0.0429	0.0678
2.7068	1800	0.0612	0.0780
2.8571	1900	0.0605	0.0594
3.0075	2000	0.0541	0.0654
3.1579	2100	0.0554	0.0544
3.3083	2200	0.0423	0.0528
3.4586	2300	0.0379	0.0552
3.6090	2400	0.0384	0.0541
3.7594	2500	0.0312	0.0534
3.9098	2600	0.0508	0.0569
4.0602	2700	0.0374	0.0548
4.2105	2800	0.0239	0.0495
4.3609	2900	0.034	0.0489
4.5113	3000	0.0419	0.0504
4.6617	3100	0.0248	0.0489
4.8120	3200	0.0437	0.0482
4.9624	3300	0.0357	0.0492

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

	sentence1	sentence2	label
type	string	string	int
details	min: 4 tokens mean: 8.5 tokens max: 15 tokens	min: 4 tokens mean: 8.85 tokens max: 15 tokens	0: ~83.10% 1: ~16.90%

sentence1	sentence2	label
`JUAN CARLOS CORTES GONZALEZ`	`JEAN CARLOS CORTES GONZALEZ`	`0`
`SERGIO ANTONIO PACHECO AHUMAITRE`	`SERGIO ANTONIO PACHECO AUMAITRE`	`1`
`JOSÉ MAURICIO ZULUAGA TOBÓN`	`JOSE HERNANDO ZULUAGA MARIN`	`0`