samandar1105/sentiment-classifier

RoBERTa Fine-Tuned for Sentiment Analysis

This model classifies English text as either Positive 😊 or Negative 😞.

Fine-tuned from roberta-base on a combination of SST-2 (Stanford Sentiment Treebank) and IMDB movie reviews.

Performance

Metric	Score
Accuracy	0.9292 (92.92%)
F1 Score	0.9413

Evaluated on 20,000 held-out IMDB test samples.

How to Use

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="samandar1105/sentiment-classifier"
)

result = classifier("This movie was absolutely fantastic!")
print(result)
# [{'label': 'positive', 'score': 0.998}]

Labels

ID	Label	Meaning
0	negative	Negative sentiment
1	positive	Positive sentiment

Training Details

Parameter	Value
Base model	roberta-base
Training data	SST-2 (67K) + IMDB (25K) = 92K samples
Epochs	4
Batch size	32
Learning rate	2e-5
Max sequence length	256
Warmup ratio	0.1
Weight decay	0.01

Limitations

• Trained on English text only
• Optimized for movie/review-style text
• Binary only (positive / negative) — no neutral class