Text Classification
Transformers
Safetensors
English
deberta-v2
deberta-v3
human value detection
schwartz values
moral values
political text
retrieval augmented classification
rag
multi-label classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use VictorYeste/value-context-rag-deberta-v3-base-doc-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorYeste/value-context-rag-deberta-v3-base-doc-rag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VictorYeste/value-context-rag-deberta-v3-base-doc-rag")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorYeste/value-context-rag-deberta-v3-base-doc-rag") model = AutoModelForSequenceClassification.from_pretrained("VictorYeste/value-context-rag-deberta-v3-base-doc-rag") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ceef70ee5c068f2e8ec2ea787d1566d2f5846b612f0e0a3879edffa246dc91cf
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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