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SATA
Sentiment Analyzer Trained by Araf
SATA is a PyTorch-based transformer model fine-tuned for emotion (sentiment) classification on English text. It is built on top of the roberta-base architecture and trained using the Hugging Face emotion dataset.
The goal of this model is to classify short pieces of text into one of several core emotional categories. SATA is designed to be simple to use, reproducible, and easily deployable via the Hugging Face ecosystem.
Model Overview
- Model name: SATA
- Meaning: Sentiment Analyzer Trained by Araf
- Base model: roberta-base
- Framework: PyTorch
- Task: Emotion / sentiment classification
- Language: English
Dataset
The model was fine-tuned on the Emotion dataset from Hugging Face Datasets.
- Dataset size: ~20k samples
- Text domain: Short informal English texts
- Splits: Train / Validation / Test
Emotion Labels
The model predicts one of the following six classes:
| Label ID | Emotion |
|---|---|
| 0 | Sadness |
| 1 | Joy |
| 2 | Love |
| 3 | Anger |
| 4 | Fear |
| 5 | Surprise |
Training Details
- Optimizer: AdamW
- Learning rate: 2e-5
- Batch size: 16
- Epochs: 3
- Max sequence length: 128
- Loss function: Cross-entropy loss
The model was trained using a custom PyTorch training loop (no Hugging Face Trainer abstraction).
Validation Performance
- Validation Accuracy: 93%
Accuracy was measured on the official validation split of the Emotion dataset.
Usage Example
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="captainaraf/sata"
)
classifier("I am extremely happy today")
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