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bert-tiny-cognitive-bias

Text Classification
Transformers
PyTorch
Safetensors
English
bert
psychology
cognitive distortions
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metadata
license: bsd-3-clause
language:
  - en
pipeline_tag: text-classification
tags:
  - psychology
  - cognitive distortions
widget:
  - text: We have known each other since childhood.
    example_title: No Distortion
  - text: I can't believe I forgot to do that, I'm such an idiot.
    example_title: Personalization
  - text: I feel like I'm disappointing others.
    example_title: Emotional Reasoning
  - text: All doctors are arrogant and don't really care about their patients.
    example_title: Overgeneralizing
  - text: They are too young to hear it.
    example_title: Labeling
  - text: She must never make any mistakes in her work.
    example_title: Should Statements
  - text: If I don't finish this project on time, my boss will fire me.
    example_title: Catastrophizing
  - text: If I keep working hard, they will eventually give me a raise.
    example_title: Reward Fallacy

Classification of Cognitive Distortions using Bert

This article is under development. Please use the model for retraining on your data, not a "ready to use" solution.

Problem Description

Cognitive distortion refers to patterns of biased or distorted thinking that can lead to negative emotions, behaviors, and beliefs. These distortions are often automatic and unconscious, and can affect a person's perception of reality and their ability to make sound judgments.

Some common types of cognitive distortions include:

  1. Personalization: Blaming oneself for things that are outside of one's control.

Examples:

  • She looked at me funny, she must be judging me.
  • I can't believe I made that mistake, I'm such a screw up.
  1. Emotional Reasoning: Believing that feelings are facts, and letting emotions drive one's behavior.

Examples:

  • I feel like I'm not good enough, so I must be inadequate.
  • They never invite me out, so they must not like me.
  1. Overgeneralizing: Drawing broad conclusions based on a single incident or piece of evidence.

Examples:

  • He never listens to me, he just talks over me.
  • Everyone always ignores my needs.
  1. Labeling: Attaching negative or extreme labels to oneself or others based on specific behaviors or traits.

Examples:

  • I'm such a disappointment.
  • He's a total jerk.
  1. Should Statements: Rigid, inflexible thinking that is based on unrealistic or unattainable expectations of oneself or others.

Examples:

  • I must never fail at anything.
  • They have to always put others' needs before their own.
  1. Catastrophizing: Assuming the worst possible outcome in a situation and blowing it out of proportion.

Examples:

  • It's all going to be a waste of time, they're never going to succeed.
  • If I don't get the promotion, my entire career is over.
  1. Reward Fallacy: Belief that one should be rewarded or recognized for every positive action or achievement.

Examples:

  • If I work hard enough, they will give me the pay raise I want.
  • If they don't appreciate my contributions, I'll start slacking off.

Model Description

This is one of the smaller BERT variants, pretrained model on English language using a masked language modeling objective. BERT was introduced in this paper and first released in this repository.

Data Description

[In progress]

Using

Example of single-label classification:

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("amedvedev/bert-tiny-cognitive-bias")
model = AutoModelForSequenceClassification.from_pretrained("amedvedev/bert-tiny-cognitive-bias")

inputs = tokenizer("He must never disappoint anyone.", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]

Metrics

Model accuracy by labels:

Precision Recall F1
No Distortion 0.84 0.74 0.79
Personalization 0.86 0.89 0.87
Emotional Reasoning 0.88 0.96 0.92
Overgeneralizing 0.80 0.88 0.84
Labeling 0.84 0.80 0.82
Should Statements 0.88 0.95 0.91
Catastrophizing 0.88 0.86 0.87
Reward Fallacy 0.87 0.95 0.91

Average model accuracy:

Accuracy Top-3 Accuracy Top-5 Accuracy Precision Recall F1
0.86 ± 0.04 0.99 ± 0.01 0.99 ± 0.01 0.86 ± 0.04 0.85 ± 0.04 0.85 ± 0.04

References

[In progress]