SentenceTransformer based on thenlper/gte-large
This is a sentence-transformers model finetuned from thenlper/gte-large. 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: thenlper/gte-large
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 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}) with Transformer model: 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})
(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("JFernandoGRE/gtelarge-colombian-elitenames")
# Run inference
sentences = [
'EL MAR',
'ZEA DE SPINEL MARIELENA',
'FONSECA MEDINA FLOR MARIA',
]
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]
Training Details
Training Dataset
Unnamed Dataset
- Size: 23,976 training samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label type string string int details - min: 4 tokens
- mean: 6.46 tokens
- max: 26 tokens
- min: 4 tokens
- mean: 8.24 tokens
- max: 28 tokens
- 0: ~87.50%
- 1: ~12.50%
- Samples:
sentence1 sentence2 label CA M
YURLY ANGELICA MENDOZA MENDEZ
0
JOSE MARIA
JOSE MARIA DOMINGUEZ VELASCO
0
O GOMEZ
JOSE DEMETRIO GOMEZ NARVAEZ
0
- Loss:
OnlineContrastiveLoss
Evaluation Dataset
Unnamed Dataset
- Size: 5,995 evaluation samples
- Columns:
sentence1
,sentence2
, andlabel
- Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label type string string int details - min: 4 tokens
- mean: 6.49 tokens
- max: 39 tokens
- min: 4 tokens
- mean: 8.18 tokens
- max: 23 tokens
- 0: ~89.20%
- 1: ~10.80%
- Samples:
sentence1 sentence2 label MARIA CECILIA VILLAMIZAR ANGULO
MARY CECILIA VILLAMIZAR ANGULO
0
GO GOMEZ
RAULARANGO GOMEZ
0
ALVAROJIMENEZ LINARES
ALVAROJIMENEZ LINAREZ
1
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 1e-05num_train_epochs
: 5warmup_ratio
: 0.182fp16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.182warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0667 | 100 | 0.5037 | 0.5726 |
0.1334 | 200 | 0.3707 | 0.5032 |
0.2001 | 300 | 0.1852 | 0.5350 |
0.2668 | 400 | 0.1513 | 0.4760 |
0.3336 | 500 | 0.1414 | 0.4331 |
0.4003 | 600 | 0.1268 | 0.4587 |
0.4670 | 700 | 0.1474 | 0.3920 |
0.5337 | 800 | 0.106 | 0.3886 |
0.6004 | 900 | 0.1205 | 0.2674 |
0.6671 | 1000 | 0.1186 | 0.4007 |
0.7338 | 1100 | 0.1089 | 0.3114 |
0.8005 | 1200 | 0.098 | 0.3934 |
0.8672 | 1300 | 0.0903 | 0.3584 |
0.9340 | 1400 | 0.1162 | 0.3693 |
1.0007 | 1500 | 0.12 | 0.3155 |
1.0674 | 1600 | 0.1145 | 0.3847 |
1.1341 | 1700 | 0.0987 | 0.2464 |
1.2008 | 1800 | 0.0908 | 0.2814 |
1.2675 | 1900 | 0.098 | 0.3297 |
1.3342 | 2000 | 0.0761 | 0.3088 |
1.4009 | 2100 | 0.0883 | 0.2902 |
1.4676 | 2200 | 0.1037 | 0.2578 |
1.5344 | 2300 | 0.0848 | 0.3500 |
1.6011 | 2400 | 0.0701 | 0.2834 |
1.6678 | 2500 | 0.0912 | 0.2429 |
1.7345 | 2600 | 0.0815 | 0.2146 |
1.8012 | 2700 | 0.0804 | 0.2155 |
1.8679 | 2800 | 0.0729 | 0.2373 |
1.9346 | 2900 | 0.0734 | 0.2314 |
2.0013 | 3000 | 0.0804 | 0.2570 |
2.0680 | 3100 | 0.0524 | 0.3019 |
2.1348 | 3200 | 0.0602 | 0.2900 |
2.2015 | 3300 | 0.0561 | 0.2553 |
2.2682 | 3400 | 0.0457 | 0.2436 |
2.3349 | 3500 | 0.0626 | 0.3225 |
2.4016 | 3600 | 0.0576 | 0.2204 |
2.4683 | 3700 | 0.0644 | 0.2630 |
2.5350 | 3800 | 0.0556 | 0.2038 |
2.6017 | 3900 | 0.0593 | 0.2694 |
2.6684 | 4000 | 0.0499 | 0.2262 |
2.7352 | 4100 | 0.0611 | 0.1960 |
2.8019 | 4200 | 0.0554 | 0.2043 |
2.8686 | 4300 | 0.0495 | 0.1858 |
2.9353 | 4400 | 0.0772 | 0.2147 |
3.0020 | 4500 | 0.0656 | 0.2513 |
3.0687 | 4600 | 0.0322 | 0.1809 |
3.1354 | 4700 | 0.0354 | 0.1908 |
3.2021 | 4800 | 0.0552 | 0.1639 |
3.2688 | 4900 | 0.0513 | 0.2011 |
3.3356 | 5000 | 0.0423 | 0.2323 |
3.4023 | 5100 | 0.0396 | 0.1624 |
3.4690 | 5200 | 0.0411 | 0.2187 |
3.5357 | 5300 | 0.0499 | 0.1867 |
3.6024 | 5400 | 0.0345 | 0.1755 |
3.6691 | 5500 | 0.0312 | 0.1708 |
3.7358 | 5600 | 0.0558 | 0.1832 |
3.8025 | 5700 | 0.0342 | 0.2056 |
3.8692 | 5800 | 0.0513 | 0.1858 |
3.9360 | 5900 | 0.0449 | 0.1792 |
4.0027 | 6000 | 0.044 | 0.1815 |
4.0694 | 6100 | 0.0329 | 0.1693 |
4.1361 | 6200 | 0.0481 | 0.1707 |
4.2028 | 6300 | 0.0328 | 0.1696 |
4.2695 | 6400 | 0.0269 | 0.1766 |
4.3362 | 6500 | 0.0299 | 0.1815 |
4.4029 | 6600 | 0.0374 | 0.2109 |
4.4696 | 6700 | 0.0449 | 0.2033 |
4.5364 | 6800 | 0.0277 | 0.2103 |
4.6031 | 6900 | 0.039 | 0.2088 |
4.6698 | 7000 | 0.0261 | 0.2045 |
4.7365 | 7100 | 0.0258 | 0.2051 |
4.8032 | 7200 | 0.0405 | 0.2069 |
4.8699 | 7300 | 0.0313 | 0.2051 |
4.9366 | 7400 | 0.0384 | 0.2039 |
Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.47.1
- PyTorch: 2.6.0+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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",
}
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Base model
thenlper/gte-large