NYXAR RoBERTa Sentiment Model
Model Description
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
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
model_path = "./onnx"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = ORTModelForSequenceClassification.from_pretrained(
model_path,
file_name="model.onnx"
)
text = "The product exceeded expectations."
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
padding=True
)
outputs = model(**inputs)
prediction = outputs.logits.argmax(dim=-1)
NYXAR Platform
This model is part of the NYXAR AI Intelligence & Observability Platform, a production-style AI system combining multi-model sentiment inference, batch intelligence workflows, LLM-powered insights, operational telemetry, and observability-driven analytics.
Version: v1.0.0
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