Create README.md

README.md

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+---
+base_model:
+- google-bert/bert-base-uncased
+---
+# Model Card: **Emotion Detection Model for TV Show Dialogues**
+
+## 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
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+## 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.