# Schwartz Value Classifier
This classifier is intended to predict the existence of social values from text snippets.

*Disclaimer: this is not the official repo published by the authors of the paper, and may not truly replicate the performance described in the original study* 

## Value dimensions
In this model we follow the 10-dimensional categorization of the Schwartz values. [link](https://en.wikipedia.org/wiki/Theory_of_basic_human_values)

1. security – safety, harmony, and stability of society, of relationships, and of self
2. power – social status and prestige, control or dominance over people and resources
3. achievement – personal success through demonstrating competence according to social standards
4. hedonism – pleasure or sensuous gratification for oneself
5. stimulation – excitement, novelty and challenge in life
6. self-direction – independent thought and action—choosing, creating, exploring
7. universalism – understanding, appreciation, tolerance, and protection for the welfare of all people and for nature
8. benevolence – preserving and enhancing the welfare of those with whom one is in frequent personal contact (the 'in-group')
9. conformity – restraint of actions, inclinations, and impulses likely to upset or harm others and violate social expectations or norms
10. tradition – respect, commitment, and acceptance of the customs and ideas that one's culture or religion provides
    
## Datasets
This model is finetuned on two datasets: ValueNet (A New Dataset for Human Value Driven Dialogue System, Qiu et al. 2021) and Touche23-ValueEval (The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments, Mirzakhmedova et al., 2023).

We follow the original paper to convert both datasets into a binary classification task for each dimension.
- ValueNet
  - A sentence has a positive label if the original label contains 1 (positive) or -1 (negative), and 0 if the original label is 0.
- ValueEval
  - A sentence is assigned a positive label if the original label vector is marked 1 for that dimension.
  - Since the original paper follows a 20-dimension refined categorization, we map them back to 10 dimensions. Therefore, the same sentence appears ten times, once for each dimension.

## How to use
Start your sentence with a label that indicates which dimension to measure. An example would be:

- \<power> [SEP] staying out late after telling my girlfriend I could be home early

Please make sure to follow the exact format "<value\_name>" at the beginning of the sentence as this is a special token in the tokenizer: any spaces or different formats will not be encoded correctly.

## Performances
- macro F1 score
  - on ValueNet: 0.648
  - on ValueEval: 0.744
  - Combined: 0.759
  
- ROC-AUC
  - on ValueNet: 0.736
  - on ValueEval:0.847
  - Combined: 0.855

## Training details
- Base model: bert-base-uncased
- Epochs: 10 w/ early stopping after no F1 increase in 3 epochs
- Learning rate: 5e-5 w/ warmup for 0.03 steps and subsequent linear decay
- Batch size: 32
- Upsampled training set to maintain 1:1 balance for pos:neg labels.

## References
- Do Differences in Values Influence Disagreements in Online Discussions? (EMNLP'23) [link](https://aclanthology.org/2023.emnlp-main.992/)
{"language": ["en"], "license": "mit", "tags": ["social-values"], "datasets": ["webis/Touche23-ValueEval"], "metrics": ["f1"]}
transformers safetensors bert text-classification social-values en dataset:webis/Touche23-ValueEval license:mit autotrain_compatible endpoints_compatible region:us