Create README.md

README.md

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+---
+license: bsd-3-clause
+language:
+- en
+pipeline_tag: text-classification
+tags:
+- psychology
+- cognitive distortions
+widget:
+- text: "He is my best friend, and 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 always disappointing others."
+  example_title: "Emotional Reasoning"
+- text: "All doctors are arrogant and don't really care about their patients."
+  example_title: "Overgeneralizing"
+- text: "It's bad for their little baby ear."
+  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
+
+## 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]