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---
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
><span style="color:red">This article is under development. Please use the model for retraining on your data, not a "ready to use" solution.</span>
## 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.*
2. **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.*
3. **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.*
4. **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.*
5. **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.*
6. **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.*
7. **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](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert).
## Data Description
[In progress]
## Using
Example of single-label classification:
```python
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]