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hoorSobh/
roberta-goemotions-ablation4-asl

Name: roberta-goemotions-ablation4-asl
Availability: InStock

multi-label-classification

goemotions

go_emotions

mit

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RoBERTa-base Fine-tuned on GoEmotions — Ablation 4 (Clipped ASL)

This model is part of an undergraduate thesis: "LLM-Based Emotion-Aware Adaptive Learning Assistant"

Model Description

RoBERTa-base fine-tuned on the GoEmotions dataset (Demszky et al., ACL 2020) for 28-class multi-label emotion classification, using Clipped Asymmetric Loss.

This is Ablation 4 in a six-ablation study. The final classifier (Ablation 6, with LLRD + cosine annealing) is hosted separately.

Training Details

Parameter	Value
Base model	roberta-base
Dataset	GoEmotions (43,410 train / 5,426 val / 5,427 test)
Loss function	Clipped Asymmetric Loss (γ_pos=1, γ_neg=3, clip=0.04)
Learning rate	2e-5
Batch size	32
Best epoch	3
Threshold	0.5

Test Set Results (GoEmotions, threshold=0.5)

Metric	Value
Macro-F1	0.5335
Micro-F1	0.5912
Precision	0.5101
Recall	0.5752
Zero-F1 classes	0
Grief F1	0.5333

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

LABELS = [
    "admiration", "amusement", "anger", "annoyance", "approval",
    "caring", "confusion", "curiosity", "desire", "disappointment",
    "disapproval", "disgust", "embarrassment", "excitement", "fear",
    "gratitude", "grief", "joy", "love", "nervousness",
    "optimism", "pride", "realization", "relief", "remorse",
    "sadness", "surprise", "neutral"
]

tokenizer = AutoTokenizer.from_pretrained("hoorSobh/roberta-goemotions-ablation4-asl")
model = AutoModelForSequenceClassification.from_pretrained(
    "hoorSobh/roberta-goemotions-ablation4-asl",
    num_labels=28,
    problem_type="multi_label_classification"
)
model.eval()

text = "I finally understood how recursion works — this is so satisfying!"
inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
probs = torch.sigmoid(logits).squeeze()
top1 = LABELS[probs.argmax().item()]
print(f"Dominant emotion: {top1}")

Citation

If you use this model, please cite:

Demszky et al. (2020). GoEmotions. ACL 2020.
Ramakrishnan & Dhinesh Babu (2025). IEEE Access. DOI: 10.1109/ACCESS.2025.3557091