distilbert-bigfive-sentence-transformer-lora

distilBERT-based Big-5 personality scorer

This is a sentence-transformers model finetuned from distilbert/distilbert-base-multilingual-cased 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: distilbert/distilbert-base-multilingual-cased <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
• Maximum Sequence Length: 512 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': 'DistilBertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): 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/distilbert-bigfive-sentence-transformer")
# Run inference
sentences = [
    'Can be organized but not rigidly so.',
    'Se adapta bien a nuevas ideas, sin renunciar a la practicidad.',
    'Valora las relaciones y se esfuerza por ser considerado.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 5]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.2455, -0.3998],
#         [ 0.2455,  1.0000, -0.3603],
#         [-0.3998, -0.3603,  1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

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: 12 tokens</li><li>mean: 22.37 tokens</li><li>max: 47 tokens</li></ul>	<ul><li>size: 5 elements</li></ul>

• Samples:

  sentence	label
  <code>Presenta signos evidentes de inquietud o depresión cuando no tiene una interacción social y atención frecuentes.</code>	<code>[-1.0, -1.0, 1.0, -1.0, -1.0]</code>
  <code>Normalmente, son personas corteses y dispuestas a llegar a acuerdos, aunque no a costa de sus principios fundamentales o de la justicia.</code>	<code>[-1.0, -1.0, -1.0, 0.5, -1.0]</code>
  <code>Sus emociones van y vienen, y algunos días son más difíciles que otros.</code>	<code>[-1.0, -1.0, -1.0, -1.0, 0.5]</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: 8 tokens</li><li>mean: 22.07 tokens</li><li>max: 37 tokens</li></ul>	<ul><li>size: 5 elements</li></ul>

• Samples:

  sentence	label
  <code>Demonstrates a practical approach to goals with occasional lapses in consistency.</code>	<code>[-1.0, 0.5, -1.0, -1.0, -1.0]</code>
  <code>Equilibra el deseo de orden con la capacidad de adaptarse a los cambios inesperados.</code>	<code>[-1.0, 0.5, -1.0, -1.0, -1.0]</code>
  <code>Equilibra la tradición con la curiosidad, y está abierto a nuevas ideas, siempre que sean razonables.</code>	<code>[0.5, -1.0, -1.0, -1.0, -1.0]</code>

• Loss: <code>main.MultiLabelBCEWithLogitsLoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• num_train_epochs: 10
• learning_rate: 0.0004510471199446068
• warmup_steps: 0.1
• per_device_eval_batch_size: 64
• hub_revision: v2

All Hyperparameters

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

• per_device_train_batch_size: 64
• num_train_epochs: 10
• max_steps: -1
• learning_rate: 0.0004510471199446068
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• 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: False
• 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: 64
• 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: v2
• 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: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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: 7.5 minutes

Framework Versions

• Python: 3.13.11
• Sentence Transformers: 5.5.1
• Transformers: 5.12.0
• PyTorch: 2.12.0+cu132
• Accelerate: 1.14.0
• Datasets: 2.19.1
• 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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