Text Classification
Transformers
Safetensors
deberta
human value detection
text classification
multi-label clasification
Instructions to use VictorYeste/deberta-based-human-value-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorYeste/deberta-based-human-value-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VictorYeste/deberta-based-human-value-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorYeste/deberta-based-human-value-detection") model = AutoModelForSequenceClassification.from_pretrained("VictorYeste/deberta-based-human-value-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 240edfb340191c00d4eb184f3f933783c93dd8176c02f1d0872abf48b625a2a6
- Size of remote file:
- 4.73 kB
- SHA256:
- 413f275dec90e46d1e78e5d4e26f9d3b2534c0616bd0160a737c3ffd5d824b9e
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