sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:23976 loss:OnlineContrastiveLoss arxiv:1908.10084 base_model:thenlper/gte-large base_model:finetune:thenlper/gte-large autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: thenlper/gte-large
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11480
- loss:OnlineContrastiveLoss
widget:
- source_sentence: PEÑA JARAMILLO
  sentences:
  - OSCAR ALBERTO ARREDONDO CASTANO
  - JAIME ALBERTO QUINETRO SABOYA
  - NESTOR HENRRY REYES PEÑA
- source_sentence: ALBERTO ANTONIO ZAPATA ELJACH ANUAR
  sentences:
  - ' SANTIAGOPE MORENO'
  - DIEGOALVAREZ HERNANDEZ
  - GABRIEL ALVARO ZAPATA B
- source_sentence: PAULA ANDREA VARGAS LOPEZ
  sentences:
  - LUZ MILENE GONZALEZ BRAVO
  - FLAVIO ALBERTO DE JESUS ROLDAN MARTINEZ
  - CAMILIA ANDREA VARGAS LOPEZ
- source_sentence: RAFAEL ANTONIO MARTINEZ RODRIGUEZ
  sentences:
  - RAFAEL TOMAS MARTINEZ RODRIGUEZ
  - LEONOR DE
  - MARTHA EUGEN GARCIA DE MARTINEZ VILLALBA
- source_sentence: ADRIANA JOSEFINA GRATEROL DE GUTIERREZ
  sentences:
  - CLAUDIA ROCIO RINCON SANCHEZ
  - JOHSON ENRIQUE CORTES CRTES
  - GUSTAVO LONDONO GUTIERREZ
---

# SentenceTransformer based on thenlper/gte-large

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-large](https://huggingface.co/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](https://huggingface.co/thenlper/gte-large) <!-- at revision 4bef63f39fcc5e2d6b0aae83089f307af4970164 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
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-elitenames2")
# Run inference
sentences = [
    'ADRIANA JOSEFINA GRATEROL DE GUTIERREZ',
    'GUSTAVO LONDONO GUTIERREZ',
    'CLAUDIA ROCIO RINCON SANCHEZ',
]
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 11,480 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                        | label                                           |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                           | string                                                                           | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.22 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.74 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~82.40%</li><li>1: ~17.60%</li></ul> |
* Samples:
  | sentence1                                            | sentence2                                     | label          |
  |:-----------------------------------------------------|:----------------------------------------------|:---------------|
  | <code>ADELY ROMERO</code>                            | <code> PETERMANJARREZ ROMERO</code>           | <code>0</code> |
  | <code>JENIFERCAÑAVERAL QUINTERO</code>               | <code>JENIFER MARIA CAÑAVERAL QUINTERO</code> | <code>1</code> |
  | <code>ALBERTO ALBARRACIN VILLAMIZAR ESSENFELL</code> | <code>ANUAR ALBERTO PEREZ ESCAF</code>        | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### Unnamed Dataset

* Size: 2,870 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                        | label                                           |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                           | string                                                                           | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.25 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.65 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>0: ~82.60%</li><li>1: ~17.40%</li></ul> |
* Samples:
  | sentence1                                                                       | sentence2                                   | label          |
  |:--------------------------------------------------------------------------------|:--------------------------------------------|:---------------|
  | <code>PEDRO NEL SIERRA CARDONA E HIJOS S EN C EN LIQUIDACION LIQUIDACION</code> | <code>PEDRO NEL SIERRA CARDONA</code>       | <code>1</code> |
  | <code>ALIKY LONDOÑO BOTERO</code>                                               | <code>ELVIA CRISTINA LONDOÑO MENECES</code> | <code>0</code> |
  | <code>FERNANDO GUTIERREZ DE PIÑERES HERZBERG</code>                             | <code>HER GUTIERREZ DE PIÑERES</code>       | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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
<details><summary>Click to expand</summary>

- `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
- `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`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.1393 | 100  | 0.2368        | 0.2417          |
| 0.2786 | 200  | 0.1276        | 0.2186          |
| 0.4178 | 300  | 0.1381        | 0.1803          |
| 0.5571 | 400  | 0.1242        | 0.1682          |
| 0.6964 | 500  | 0.1113        | 0.1741          |
| 0.8357 | 600  | 0.1047        | 0.1321          |
| 0.9749 | 700  | 0.0906        | 0.1298          |
| 1.1142 | 800  | 0.0701        | 0.1270          |
| 1.2535 | 900  | 0.0702        | 0.1135          |
| 1.3928 | 1000 | 0.0807        | 0.0960          |
| 1.5320 | 1100 | 0.0632        | 0.0980          |
| 1.6713 | 1200 | 0.0666        | 0.0931          |
| 1.8106 | 1300 | 0.0773        | 0.0921          |
| 1.9499 | 1400 | 0.0738        | 0.0821          |
| 2.0891 | 1500 | 0.0585        | 0.0807          |
| 2.2284 | 1600 | 0.0359        | 0.0838          |
| 2.3677 | 1700 | 0.0509        | 0.0757          |
| 2.5070 | 1800 | 0.0393        | 0.0811          |
| 2.6462 | 1900 | 0.0437        | 0.0774          |
| 2.7855 | 2000 | 0.0258        | 0.0802          |
| 2.9248 | 2100 | 0.0437        | 0.0825          |
| 3.0641 | 2200 | 0.0297        | 0.0789          |
| 3.2033 | 2300 | 0.0308        | 0.0788          |
| 3.3426 | 2400 | 0.0411        | 0.0772          |
| 3.4819 | 2500 | 0.0322        | 0.0794          |
| 3.6212 | 2600 | 0.0268        | 0.0793          |
| 3.7604 | 2700 | 0.036         | 0.0839          |
| 3.8997 | 2800 | 0.033         | 0.0821          |
| 4.0390 | 2900 | 0.0299        | 0.0794          |
| 4.1783 | 3000 | 0.0226        | 0.0797          |
| 4.3175 | 3100 | 0.0198        | 0.0760          |
| 4.4568 | 3200 | 0.0293        | 0.0771          |
| 4.5961 | 3300 | 0.0274        | 0.0747          |
| 4.7354 | 3400 | 0.0162        | 0.0746          |
| 4.8747 | 3500 | 0.0351        | 0.0745          |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.4.1
- Tokenizers: 0.21.1

## 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",
}
```

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