ola-owo/distilbert-bigfive-sentence-transformer

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
• 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

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 = [
    'Shows little interest in exploring unconventional viewpoints or imaginative scenarios.',
    'Can be outgoing in familiar settings but also enjoys solitude.',
    'They require constant reassurance, feel persecuted by minor feedback, and oscillate between rage and hopelessness.',
]
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, 0.9112, 0.9027],
#         [0.9112, 1.0000, 0.9922],
#         [0.9027, 0.9922, 1.0000]])

Training Details

Training Dataset

ola-owo/big-five-personality-traits

• Dataset: ola-owo/big-five-personality-traits
• Size: 2,250 training samples
• Columns: sentence and label
• Approximate statistics based on the first 100 samples:

  	sentence	label
  type	string	list
  modality	text
  details	min: 10 tokens mean: 21.44 tokens max: 40 tokens	size: 5 elements

• Samples:

  sentence	label
  They feel exhausted after group travel, wait for others to approach them, and enjoy peaceful nature walks alone.	[0.5, 0.5, 0.0, 0.5, 0.5]
  Mantiene un enfoque constante y una atención meticulosa al detalle.	[0.5, 0.85, 0.5, 0.5, 0.5]
  Participates in social activities as opportunities arise but does not actively seek them out.	[0.5, 0.5, 0.5, 0.5, 0.5]

• Loss: main.MultiLabelBCEWithLogitsLoss

Evaluation Dataset

ola-owo/big-five-personality-traits

• Dataset: ola-owo/big-five-personality-traits
• Size: 250 evaluation samples
• Columns: sentence and label
• Approximate statistics based on the first 100 samples:

  	sentence	label
  type	string	list
  modality	text
  details	min: 11 tokens mean: 22.88 tokens max: 37 tokens	size: 5 elements

• Samples:

  sentence	label
  Keeps tasks organized and well-planned.	[0.5, 0.85, 0.5, 0.5, 0.5]
  Tiende a ser espontáneo y desorganizado, con poca atención a los planes a largo plazo o a los detalles.	[0.5, 0.0, 0.5, 0.5, 0.5]
  Is comfortable with alone time, but also enjoys occasional socializing.	[0.5, 0.5, 0.15000000000000002, 0.5, 0.5]

• Loss: main.MultiLabelBCEWithLogitsLoss

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 0.001
• num_train_epochs: 10
• warmup_steps: 0.1
• fp16: True
• load_best_model_at_end: True
• push_to_hub: True
• hub_revision: main

All Hyperparameters

Click to expand

• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 0.001
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 10
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0.1
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• 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
• push_to_hub: True
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: main
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• 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
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
1.0	36	0.6944	0.7037

Training Time

• Training: 8.6 seconds

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.5.1
• Transformers: 5.0.0
• PyTorch: 2.11.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• 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",
}