vladinc
/

bigfive-regression-model

@@ -1,199 +1,120 @@
 ---
 library_name: transformers
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 ---
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 ---
 library_name: transformers
+tags:
+- big-five
+- regression
+- psychology
+- transformer
+- text-analysis
+license: mit
+datasets:
+- jingjietan/essays-big5
+language:
+- en
 ---
+# 🧠 Big Five Personality Regression Model
+This model predicts Big Five personality traits — Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism — from English free-text inputs. The output is a set of five continuous values between 0.0 and 1.0, corresponding to each trait.
+---
 ## Model Details
 ### Model Description
+- **Developed by:** [vladinc](https://huggingface.co/vladinc)
+- **Model type:** `distilbert-base-uncased`, fine-tuned
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** `distilbert-base-uncased`
+- **Trained on:** ~8,700 essays from the `jingjietan/essays-big5` dataset
+### Model Sources
+- **Repository:** [https://huggingface.co/vladinc/bigfive-regression-model](https://huggingface.co/vladinc/bigfive-regression-model)
+---
 ## Uses
 ### Direct Use
+This model can be used to estimate personality profiles from user-written text. It may be useful in psychological analysis, conversational profiling, or educational feedback systems.
 ### Out-of-Scope Use
+- Not intended for clinical or diagnostic use.
+- Should not be used to make hiring, legal, or psychological decisions.
+- Not validated across cultures or demographic groups.
+---
 ## Bias, Risks, and Limitations
+- Trained on essay data; generalizability to tweets, messages, or other short-form texts may be limited.
+- Traits like Extraversion and Neuroticism had higher validation MSE, suggesting reduced predictive reliability.
+- Cultural and linguistic biases in training data may influence predictions.
 ### Recommendations
+Do not use predictions from this model in isolation. Supplement with human judgment and/or other assessment tools.
+---
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("vladinc/bigfive-regression-model")
+tokenizer = AutoTokenizer.from_pretrained("vladinc/bigfive-regression-model")
+text = "I enjoy reflecting on abstract concepts and trying new things."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+print(outputs.logits)  # 5 float scores between 0.0 and 1.0
+Training Details
+Training Data
+Dataset: jingjietan/essays-big5
+Format: Essay text + 5 numeric labels for personality traits
+Training Procedure
+Epochs: 3
+Batch size: 8
+Learning rate: 2e-5
+Loss Function: Mean Squared Error
+Metric for Best Model: MSE on Openness
+Evaluation
+Metrics
+Trait	Validation MSE
+Openness	0.324
+Conscientiousness	0.537
+Extraversion	0.680
+Agreeableness	0.441
+Neuroticism	0.564
+Citation
+If you use this model, please cite it:
+BibTeX:
+bibtex
+Copy
+Edit
+@misc{vladinc2025bigfive,
+  title={Big Five Personality Regression Model},
+  author={vladinc},
+  year={2025},
+  howpublished={\\url{https://huggingface.co/vladinc/bigfive-regression-model}}
+}
+Contact
+If you have questions or suggestions, feel free to reach out via the Hugging Face profile.