Instructions to use Querit/Querit-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Querit/Querit-4B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Querit/Querit-4B") model = AutoModel.from_pretrained("Querit/Querit-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -44,10 +44,6 @@ As of August 29, 2026, Querit-Reranker-4B achieves the best average score of **7
|
|
| 44 |
|
| 45 |
If you find Querit-Reranker useful for your research or applications, please cite our paper:
|
| 46 |
|
| 47 |
-
**Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation**
|
| 48 |
-
Yunfei Zhong, Jun Yang, Wei Huang, Yinqiong Cai, Haosheng Qian, Yixing Fan, Ruqing Zhang, Lixin Su, Daiting Shi, and Jiafeng Guo.
|
| 49 |
-
arXiv:2606.19037, 2026.
|
| 50 |
-
|
| 51 |
```bibtex
|
| 52 |
@misc{zhong2026queritrerankertrainingcompactmultilingual,
|
| 53 |
title={Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation},
|
|
|
|
| 44 |
|
| 45 |
If you find Querit-Reranker useful for your research or applications, please cite our paper:
|
| 46 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 47 |
```bibtex
|
| 48 |
@misc{zhong2026queritrerankertrainingcompactmultilingual,
|
| 49 |
title={Querit-Reranker: Training Compact Multilingual Rerankers via Efficient Label-Free Distribution Adaptation},
|