mindflux-sentiment – English Sentiment Classification
Overview
mindflux-sentiment is a high-performance sentiment analysis model built upon RoBERTa-large, fine-tuned for binary sentiment classification of English-language text. It predicts either positive (1) or negative (0) sentiment, and is suitable for various text domains including reviews, tweets, and user feedback.
This model is derived from a robust general-purpose sentiment classification approach, optimized across diverse datasets to ensure strong generalization performance.
🧪 Predictions on Your Own Data
To run predictions on your own text data, simply use the Hugging Face pipeline interface. Here's an example:
from transformers import pipeline
sentiment_pipeline = pipeline("sentiment-analysis", model="MIAOAI/mindflux-sentiment")
print(sentiment_pipeline("I absolutely love using the MindFlux platform!"))
Alternatively, you can use Google Colab for free GPU-based inference or batch sentiment predictions.
🚀 Model Usage in Hugging Face Pipelines
from transformers import pipeline
sentiment_pipeline = pipeline("sentiment-analysis", model="MIAOAI/mindflux-sentiment")
result = sentiment_pipeline("The new features are amazing and very user-friendly.")
print(result)
🛠️ Fine-tuning and Transfer Learning
mindflux-sentiment can be used as a base model for further fine-tuning on domain-specific text data. See the Transformers fine-tuning guide for how to adapt the model to your custom sentiment labels or multi-class tasks.
📊 Performance
The model has been evaluated across 15 diverse benchmark datasets and demonstrates superior generalization performance compared to baseline sentiment models trained on a single corpus (e.g., SST-2).
| Dataset | Baseline Model | mindflux-sentiment |
|---|---|---|
| McAuley & Leskovec (Reviews) | 84.7 | 98.0 |
| McAuley & Leskovec (Review Titles) | 65.5 | 87.0 |
| Yelp Academic Dataset | 84.8 | 96.5 |
| Maas et al. (IMDB) | 80.6 | 96.0 |
| Kaggle Reviews | 87.2 | 96.0 |
| Pang & Lee (2005) | 89.7 | 91.0 |
| Twitter (Nakov et al., 2013) | 70.1 | 88.5 |
| Twitter (Shamma, 2009) | 76.0 | 87.0 |
| Amazon Reviews - Books | 83.0 | 92.5 |
| Amazon Reviews - DVDs | 84.5 | 92.5 |
| Amazon Reviews - Electronics | 74.5 | 95.0 |
| Amazon Reviews - Kitchen | 80.0 | 98.5 |
| SST-1 (Pang et al., 2002) | 73.5 | 95.5 |
| Twitter (Speriosu et al., 2011) | 71.5 | 85.5 |
| Social Media (Hartmann et al., 2019) | 65.5 | 98.0 |
| Average | 78.1 | 93.2 |
⚙️ Fine-tuning Hyperparameters
learning_rate = 2e-5num_train_epochs = 3.0warmup_steps = 500weight_decay = 0.01
Default values for other parameters follow the Hugging Face Trainer defaults.
🔖 Citation
If you use this model in your research or product, please cite it as:
@misc{mindflux2025,
title={mindflux-sentiment: A High-Performance English Sentiment Classifier},
author={MindFlux AI Team},
year={2025},
url={https://huggingface.co/MIAOAI/mindflux-sentiment}
}
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