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captainaraf/
sata

Name: sata
Rating: 2.5 (5 reviews)

text-embeddings-inference

endpoints_compatible

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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

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="captainaraf/sata"
)

classifier("I am extremely happy today")