Text Classification
Transformers
Safetensors
English
deberta-v2
citation-verification
retrieval-augmented-generation
rag
cross-lingual
deberta
cross-encoder
nli
attribution
Eval Results (legacy)
text-embeddings-inference
Instructions to use convexray/alignment-module-cross-encoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use convexray/alignment-module-cross-encoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="convexray/alignment-module-cross-encoder-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("convexray/alignment-module-cross-encoder-base") model = AutoModelForSequenceClassification.from_pretrained("convexray/alignment-module-cross-encoder-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# DualTrack Alignment Module
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> **Anonymous submission to ACL 2026**
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A cross-encoder model for detecting **citation drift** in Retrieval-Augmented Generation (RAG) systems. Given a user-facing claim, an evidence representation, and a source passage, the model predicts whether the citation is valid (the source supports the claim).
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# DualTrack Alignment Module
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A cross-encoder model for detecting **citation drift** in Retrieval-Augmented Generation (RAG) systems. Given a user-facing claim, an evidence representation, and a source passage, the model predicts whether the citation is valid (the source supports the claim).
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