---
base_model:
- google-bert/bert-base-uncased
---
# Model Card: **Emotion Detection Model**

## Model Overview
This model is trained to detect nuanced emotions in text data, specifically focusing on dialogues from the TV show *Friends* and additional curated online content. By leveraging advanced deep learning architectures such as BERT and GPT, the model performs multi-label classification to identify up to 2–3 emotions per input dialogue. The targeted emotions include:
- Happiness
- Sadness
- Anger
- Fear
- Surprise
- Disgust
- Love
- Excitement
- Anticipation
- Contentment
- Confusion
- Frustration
- Nostalgia

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## Intended Use
- **Primary Use Case:** Emotion detection in textual data, particularly for analyzing media content like TV shows, movies, or social media.
- **Scope of Application:** Suitable for analyzing dialogue or conversational data to identify emotional trends, character development, or audience engagement.

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## Dataset
### Source
- Dialogues from the TV show *Friends*.

### Labeling
- Dialogues were labeled with 2–3 emotions per text using OpenAI's ChatGPT API.
- Labels represent a rich variety of emotional states.

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## Methodology
### Labeling Process
- ChatGPT API was used to classify text into multiple emotions.
- Outputs were reviewed for accuracy and adjusted to minimize misclassification.

### Model Architecture
- **Pre-trained Models Used:** BERT
- Fine-tuned on labeled dialogue data for emotion detection.
- Supports multi-label classification to capture multiple emotions simultaneously.