A transformer-based sentiment classification model developed for the NYXAR AI Intelligence & Observability Platform. The model leverages a fine-tuned RoBERTa architecture optimized for customer feedback intelligence and predicts sentiment across three classes: Positive, Neutral, and Negative.

Framework: Hugging Face Transformers, ONNX Runtime, RoBERTa Language: English License: MIT Base Model: roberta-base

Training Data

The model was trained on SetFit/amazon_reviews_multi_en, an English Amazon reviews dataset commonly used for sentiment classification tasks.

Intended Use

This model is designed for customer feedback analysis, product review monitoring, support ticket intelligence, sentiment trend analysis, and enterprise AI intelligence workflows.

Limitations

The model may struggle with sarcasm, irony, ambiguous sentiment expressions, domain-specific terminology not represented in the training data, and highly subjective reviews. Predictions should be used as supporting signals rather than business-critical decisions.

Performance

Metric	Score
Accuracy	77.56%
Precision	77.99%
Recall	77.56%
F1 Score	77.76%

Usage

Assumes tokenizer and model_quantized.onnx are in ./onnx/

python

import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer
from scipy.special import softmax

model_path = "./onnx"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Load ONNX model
session = ort.InferenceSession(
    f"{model_path}/model_quantized.onnx"
)

text = "The product exceeded expectations."

# Tokenize
inputs = tokenizer(
    text,
    return_tensors="np",
    truncation=True,
    padding=True
)

# Convert tokenizer outputs to ONNX inputs
ort_inputs = {
    k: v.astype(np.int64)
    for k, v in inputs.items()
}

# Run inference
outputs = session.run(None, ort_inputs)

# Get logits
logits = outputs[0]

# Compute probabilities
probs = softmax(logits, axis=-1)

prediction = int(np.argmax(probs, axis=-1)[0])
confidence = float(np.max(probs))

print(prediction, confidence)

Sanjeev2501/nyxar-roberta-sentiment

NYXAR RoBERTa Sentiment Model

Model Description