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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5627e49e140478b06d08a00ffc8fb4f836770fdddd477949683a4ee2808a51c
- Size of remote file:
- 1.74 GB
- SHA256:
- c96f2f423b0b0986608277654a327563555117ed73c8aaf5a69e94b8e49b6b39
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