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
| [ | |
| "Self-direction: thought", | |
| "Self-direction: action", | |
| "Stimulation", | |
| "Hedonism", | |
| "Achievement", | |
| "Power: dominance", | |
| "Power: resources", | |
| "Face", | |
| "Security: personal", | |
| "Security: societal", | |
| "Tradition", | |
| "Conformity: rules", | |
| "Conformity: interpersonal", | |
| "Humility", | |
| "Benevolence: caring", | |
| "Benevolence: dependability", | |
| "Universalism: concern", | |
| "Universalism: nature", | |
| "Universalism: tolerance" | |
| ] | |