ola-owo/big5-sentence-transformer-lora

Big-5 personality scorer based on embeddinggemma-300m with LoRA adapter

This is a sentence-transformers model finetuned from google/embeddinggemma-300m on the ola-owo/big-five-personality-traits dataset. It maps sentences & paragraphs to a 5-dimensional dense vector space and can be used for feature extraction.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: google/embeddinggemma-300m <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
• —Maximum Sequence Length: 2048 tokens
• —Output Dimensionality: 5 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text
• —Training Dataset:
• —ola-owo/big-five-personality-traits
• —Languages: en, es <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Gemma3TextModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
  (4): Normalize({})
  (5): Dense({'in_features': 768, 'out_features': 5, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

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("ola-owo/big5-sentence-transformer-lora")
# Run inference
queries = [
    'Se siente más viva en entornos sociales y dinámicos.',
]
documents = [
    'A menudo no cumple con los plazos y deja tareas incompletas, sin mostrar una gran responsabilidad.',
    'Muestra un comportamiento tranquilo y reservado, con momentos de entusiasmo social.',
    'Demonstrates flexible thinking that adapts between conventional and innovative approaches as needed.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 5] [3, 5]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[-0.0554, -0.9912, -0.4846]])

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Training Details

Training Dataset

ola-owo/big-five-personality-traits

• —Dataset: ola-owo/big-five-personality-traits
• —Size: 2,250 training samples
• —Columns: <code>sentence</code> and <code>label</code>
• —Approximate statistics based on the first 100 samples: | | sentence | label | |:---------|:----------------------------------------------------------------------------------|:-----------------------------------| | type | string | list | | modality | text | | | details | <ul><li>min: 8 tokens</li><li>mean: 19.07 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>size: 5 elements</li></ul> |
• —Samples: | sentence | label | |:-------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------| | <code>Intentos de mantener el orden, pero a menudo prioriza la comodidad sobre la estructura.</code> | <code>[-1.0, 0.15000000000000002, -1.0, -1.0, -1.0]</code> | | <code>Tiende a ser reservada y menos asertiva, y rara vez busca emociones o llamar la atención.</code> | <code>[-1.0, -1.0, 0.15000000000000002, -1.0, -1.0]</code> | | <code>Strikes a balance between socializing and alone time, feeling content with either.</code> | <code>[-1.0, -1.0, 0.5, -1.0, -1.0]</code> |
• —Loss: <code>_main_.MultiLabelBCEWithLogitsLoss</code>

Evaluation Dataset

ola-owo/big-five-personality-traits

• —Dataset: ola-owo/big-five-personality-traits
• —Size: 250 evaluation samples
• —Columns: <code>sentence</code> and <code>label</code>
• —Approximate statistics based on the first 100 samples: | | sentence | label | |:---------|:----------------------------------------------------------------------------------|:-----------------------------------| | type | string | list | | modality | text | | | details | <ul><li>min: 9 tokens</li><li>mean: 19.09 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>size: 5 elements</li></ul> |
• —Samples: | sentence | label | |:----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------| | <code>Maintains excellent organization and pays strong attention to detail in nearly everything.</code> | <code>[-1.0, 0.85, -1.0, -1.0, -1.0]</code> | | <code>Enjoys occasional company but generally prefers quieter, low-key environments.</code> | <code>[-1.0, -1.0, 0.15000000000000002, -1.0, -1.0]</code> | | <code>Will sacrifice personal interests and endure unfairness to avoid conflict or help someone in need.</code> | <code>[-1.0, -1.0, -1.0, 1.0, -1.0]</code> |
• —Loss: <code>_main_.MultiLabelBCEWithLogitsLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• —num_train_epochs: 20
• —learning_rate: 9.697682561279368e-05
• —warmup_steps: 0.1
• —auto_find_batch_size: True

All Hyperparameters

<details><summary>Click to expand</summary>

• —per_device_train_batch_size: 8
• —num_train_epochs: 20
• —max_steps: -1
• —learning_rate: 9.697682561279368e-05
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_steps: 0.1
• —optim: adamwtorchfused
• —optim_args: None
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —optim_target_modules: None
• —gradient_accumulation_steps: 1
• —average_tokens_across_devices: True
• —max_grad_norm: 1.0
• —label_smoothing_factor: 0.0
• —bf16: False
• —fp16: False
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —use_liger_kernel: False
• —liger_kernel_config: None
• —use_cache: False
• —neftune_noise_alpha: None
• —torch_empty_cache_steps: None
• —auto_find_batch_size: True
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —include_num_input_tokens_seen: no
• —log_level: passive
• —log_level_replica: warning
• —disable_tqdm: False
• —project: huggingface
• —trackio_space_id: None
• —trackio_bucket_id: None
• —trackio_static_space_id: None
• —per_device_eval_batch_size: 8
• —prediction_loss_only: True
• —eval_on_start: False
• —eval_do_concat_batches: True
• —eval_use_gather_object: False
• —eval_accumulation_steps: None
• —include_for_metrics: []
• —batch_eval_metrics: False
• —save_only_model: False
• —save_on_each_node: False
• —enable_jit_checkpoint: False
• —push_to_hub: False
• —hub_private_repo: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_always_push: False
• —hub_revision: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —restore_callback_states_from_checkpoint: False
• —full_determinism: False
• —seed: 42
• —data_seed: None
• —use_cpu: False
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —dataloader_prefetch_factor: None
• —remove_unused_columns: True
• —label_names: None
• —train_sampling_strategy: random
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —ddp_static_graph: None
• —ddp_backend: None
• —ddp_timeout: 1800
• —fsdp: None
• —fsdp_config: None
• —deepspeed: None
• —debug: []
• —skip_memory_metrics: True
• —do_predict: False
• —resume_from_checkpoint: None
• —warmup_ratio: None
• —local_rank: -1
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

Training Time

• —Training: 1.6 hours

Framework Versions

• —Python: 3.14.6
• —Sentence Transformers: 5.6.0
• —Transformers: 5.12.1
• —PyTorch: 2.12.1+cu132
• —Accelerate: 1.14.0
• —Datasets: 5.0.0
• —Tokenizers: 0.22.2

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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