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info.md

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+# GoEmotions Dataset (Preprocessed)
+
+This directory contains the **preprocessed GoEmotions dataset** for **multi-label emotion classification**. The dataset has been standardized to ensure compatibility with transformer-based models and consistency with other emotion benchmarks used in this project.
+
+---
+
+## 📂 Dataset Structure
+
+The dataset is split into:
+
+* `train`
+* `validation`
+* `test`
+
+Each split is provided as a tabular file with a uniform schema.
+
+---
+
+## 📊 Data Format
+
+Each row represents a single text sample with multi-label emotion annotations.
+
+**Columns:**
+
+* **`text`** – Preprocessed textual input
+* **Emotion labels (`0`–`27`)** – Binary indicators (`1` = emotion present, `0` = absent)
+
+```
+text | 0 | 1 | 2 | ... | 27
+```
+
+Multiple emotions may be active for a single sample.
+
+---
+
+## 🏷️ Emotion Label Mapping (28 Classes)
+
+| Index | Emotion        |
+| ----: | -------------- |
+|     0 | Admiration     |
+|     1 | Amusement      |
+|     2 | Anger          |
+|     3 | Annoyance      |
+|     4 | Approval       |
+|     5 | Caring         |
+|     6 | Confusion      |
+|     7 | Curiosity      |
+|     8 | Desire         |
+|     9 | Disappointment |
+|    10 | Disapproval    |
+|    11 | Disgust        |
+|    12 | Embarrassment  |
+|    13 | Excitement     |
+|    14 | Fear           |
+|    15 | Gratitude      |
+|    16 | Grief          |
+|    17 | Joy            |
+|    18 | Love           |
+|    19 | Nervousness    |
+|    20 | Optimism       |
+|    21 | Pride          |
+|    22 | Realization    |
+|    23 | Relief         |
+|    24 | Remorse        |
+|    25 | Sadness        |
+|    26 | Surprise       |
+|    27 | Neutral        |
+
+---
+
+## 🔧 Preprocessing Summary
+
+* Converted original annotations to **multi-one-hot encoding**
+* Standardized label dimensions (28 emotions)
+* Removed unused metadata
+* Preserved semantic and emotional content
+* Applied preprocessing **prior to tokenization**
+
+---
+
+## ✅ Intended Use
+
+* Multi-label emotion classification
+* Transformer-based model training and evaluation
+* Cross-dataset benchmarking
+* Emotion representation learning
+
+---
+
+## 📚 Citation
+
+If you use this dataset, please cite the original work:
+
+Demszky et al. (2020).
+**GoEmotions: A Dataset of Fine-Grained Emotions.**
+Proceedings of ACL 2020.
+
+```bibtex
+@inproceedings{demszky2020goemotions,
+  title     = {GoEmotions: A Dataset of Fine-Grained Emotions},
+  author    = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
+  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+  year      = {2020}
+}
+```
+
+---