Text Ranking
sentence-transformers
Safetensors
xlm-roberta
reranker
cross-encoder
islam
multilingual
text-embeddings-inference
Instructions to use sirathic/intiqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sirathic/intiqa with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("sirathic/intiqa") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Intiqa: 12-layer reranker distilled from our fine-tuned Qwen3-Reranker-0.6B, with benchmarks
7154473 verified Download tokenizer.json from sirathic/intiqa: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/sirathic/intiqa/resolve/main/tokenizer.json
- Command line
-
hf download hf://sirathic/intiqa/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sirathic/intiqa/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 812880d891afa37f6d5f1e79856fb85ed0c54f91d54dfe564acbca782169c8b8
- Size of remote file:
- 17.1 MB
- SHA256:
- 6129e14723e516c4e1d6bfaedcdc91f2343e4d68200eb1a71c90a925c7f60f5d
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