---
license: apache-2.0
datasets:
- openrelay/openrelay-dataset
language:
- en
metrics:
- accuracy
- f1
base_model: distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
- openrelay
- productivity
- summarization
- semantic-search
- Q&A
- workflow
---

# openrelay-ai-v1

**openrelay-ai-v1** is the first-generation AI model from [OpenRelay](https://openrelay.live/), a modern tech media and productivity platform. This model is designed to power a range of intelligent features across the OpenRelay ecosystem, including content understanding, semantic search, summarization, Q&A, recommendations, and workflow automation.

---

## 🧠 Model Highlights

- **Content Categorization:**  
  Automatically tags and organizes articles, reviews, and resources by topic.

- **Semantic Search:**  
  Find tools, guides, and discussions using natural language queries.

- **Summarization:**  
  Generates concise summaries and key takeaways for long-form reviews and blog posts.

- **Sentiment Analysis:**  
  Detects the tone of reviews and community feedback.

- **Q&A / Chatbot:**  
  Powers instant answers to questions about tools, workflows, and platform features.

- **Personalization:**  
  Underpins recommendation systems for personalized content and tool suggestions.

---

## 🏗️ Technical Details

- **Architecture:** Transformer-based (e.g., distilbert-base-uncased, fine-tuned for OpenRelay tasks)
- **Training Data:** Curated OpenRelay content (articles, reviews, comments), public tech/productivity datasets
- **Supported Tasks:** Text classification, summarization, semantic search, Q&A

---

## 🚀 Usage

You can load and use `openrelay-ai-v1` with Hugging Face Transformers:

```python
from transformers import pipeline

# Example: Text Classification
classifier = pipeline("text-classification", model="openrelay/openrelay-ai-v1")
result = classifier("Notion is a versatile productivity tool.")
print(result)

# Example: Summarization (if supported)
summarizer = pipeline("summarization", model="openrelay/openrelay-ai-v1")
summary = summarizer("Paste your OpenRelay article text here.")
print(summary)
```

---

## 📊 Metrics

- **Accuracy** and **F1-score** measured on OpenRelay-categorized test sets and public benchmarks.

---

## 📄 License

This model is licensed under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

---

## 🤗 About OpenRelay

OpenRelay is a tech media and productivity platform focused on tool reviews, workflow optimization, and community-powered guides.  
Visit [openrelay.live](https://openrelay.live/) or follow us on [Instagram](https://instagram.com/openrelay_ig), [X](https://x.com/openrelay_x), [YouTube](https://www.youtube.com/@openrelay), [LinkedIn](https://www.linkedin.com/showcase/openrelay), [Threads](https://www.threads.com/@openrelay_ig), and [Facebook](https://www.facebook.com/openrelay/).

---

## 📝 Citation

If you use `openrelay-ai-v1` in your research or application, please cite this repository and the OpenRelay platform:

```
@misc{openrelay-ai-v1,
  title={openrelay-ai-v1: OpenRelay Platform AI Model},
  author={OpenRelay Team},
  howpublished={\url{https://huggingface.co/openrelay/openrelay-ai-v1}},
  year={2025}
}
```

---

**openrelay-ai-v1** marks the beginning of OpenRelay's AI-driven evolution, enabling smarter workflows and a more engaging, personalized user experience.