Text Classification
Transformers
Safetensors
modernbert
personality
big-five
regression
text-embeddings-inference
Instructions to use Ritsuk0/aleph-big5-fullp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ritsuk0/aleph-big5-fullp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ritsuk0/aleph-big5-fullp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ritsuk0/aleph-big5-fullp") model = AutoModelForSequenceClassification.from_pretrained("Ritsuk0/aleph-big5-fullp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Aleph Big 5: FullP
ModernBERT-large fine-tuned to predict a person's Big 5 personality traits from their writing. It has a regression head of five outputs (one per trait), which are scores between 0 and 1 (multiply by 100 to get a 0-100 score). The output order is openness, conscientiousness, extraversion, agreeableness, and neuroticism.
We trained it on the Essays dataset, the PAN 2015 Author Profiling dataset (tweets), a subset of PANDORA (Reddit comments), and our own synthetic dataset of Reddit-style comments. The model takes up to 4096 tokens, so for longer texts we split them into chunks and average the predictions.
| Test set | Mean Pearson r (90% CI) |
|---|---|
| Aleph Big 5 Reddit (142 users) | .141 [.087, .194] |
| PAN 2015 English (142 users) | .398 [.345, .450] |
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Model tree for Ritsuk0/aleph-big5-fullp
Base model
answerdotai/ModernBERT-large