codewithdark/Gomotions-tokenizer

---

license: mit dataset_info: features:

• name: input_ids sequence: int32
• name: attention_mask sequence: int8
• name: labels sequence: int64 splits:
• name: train num_bytes: 121374360 num_examples: 43410
• name: validation num_bytes: 15171096 num_examples: 5426
• name: test num_bytes: 15173892 num_examples: 5427 download_size: 2670120 dataset_size: 151719348 configs:
• config_name: default data_files:
  • split: train path: data/train-*
  • split: validation path: data/validation-*
  • split: test path: data/test-*

📚 GoEmotions Dataset (Processed for Multi-Label Classification)

📖 Dataset Overview

This dataset is a preprocessed version of the GoEmotions dataset, containing multi-label emotion annotations for text inputs. It consists of train, validation, and test splits.

🔢 Dataset Statistics

Split	Samples
Train	XX,XXX
Validation	X,XXX
Test	X,XXX

📌 Features

Feature	Type	Description
input_ids	list[int]	Tokenized input text
attention_mask	list[int]	Attention mask for tokens
labels	list[int]	Multi-label emotion encoding

📂 How to Load

from datasets import load_dataset

dataset = load_dataset("codewithdark/go-emotions-processed")
print(dataset["train"][0])

🏋️‍♂️ Preprocessing Steps

• Tokenization: bert-base-uncased
• Multi-label encoding: Binary encoding of emotions
• Train/Validation/Test split: 80/10/10

🎯 Labels (Emotions)

The dataset contains 27 emotion categories, including:

• Admiration, Joy, Sadness, Anger, Optimism, Disgust, Love, etc.

🛠️ Citation

If you use this dataset, please cite:

@misc{go_emotions_dataset,
  author = {Google AI},
  title = {GoEmotions Dataset},
  year = {2021},
  url = {https://huggingface.co/datasets/go_emotions}
}