Instructions to use Modotte/AIRealNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Modotte/AIRealNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Modotte/AIRealNet") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Modotte/AIRealNet") model = AutoModelForImageClassification.from_pretrained("Modotte/AIRealNet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -166,6 +166,17 @@ Future iterations aim to:
|
|
| 166 |
|
| 167 |
---
|
| 168 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 169 |
## References
|
| 170 |
|
| 171 |
* Microsoft SwinV2 Tiny: [https://github.com/microsoft/Swin-Transformer](https://github.com/microsoft/Swin-Transformer)
|
|
|
|
| 166 |
|
| 167 |
---
|
| 168 |
|
| 169 |
+
### Citation
|
| 170 |
+
```bibtex
|
| 171 |
+
@misc{xenarcai_sparkembedding_2025,
|
| 172 |
+
title={SparkEmbedding-300m: A Fine-Tuned Multilingual Embedding Model for Cross-Lingual Retrieval},
|
| 173 |
+
author={Parvesh Rawal},
|
| 174 |
+
publisher={Hugging Face},
|
| 175 |
+
year={2025},
|
| 176 |
+
url={https://huggingface.co/XenArcAI/AIRealNet}
|
| 177 |
+
}
|
| 178 |
+
```
|
| 179 |
+
|
| 180 |
## References
|
| 181 |
|
| 182 |
* Microsoft SwinV2 Tiny: [https://github.com/microsoft/Swin-Transformer](https://github.com/microsoft/Swin-Transformer)
|