Text Ranking
Transformers
Safetensors
multilingual
t5gemma2
text2text-generation
reranker
encoder-decoder
FBNL
Retrieval
RAG
Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Large with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Large") model = AutoModelForMultimodalLM.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Large") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 9ce4f695d2bbd2b3ec437c0b122744ca168915f9bd2a84069b947632f14b8089
- Size of remote file:
- 224 kB
- SHA256:
- 05b52fbc80c52bbb76fbf05e34cd0173fea0b04afdf9305711359f0315a7877d
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