Text Classification
Transformers
English
tsunami
hydrodynamics
research
wave-analysis
bathymetric modulation
coastal inundation
long-wave dynamics
real-time forecasting
shallow-water equations
spectral energy analysis
wave propagation
Instructions to use Gitdeeper4/chi-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gitdeeper4/chi-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gitdeeper4/chi-model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gitdeeper4/chi-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # TSU-WAVE CHI Model | |
| import numpy as np | |
| def predict_chi(wcc, kpr, hfsi, becf, sdb, sbsp, smvi): | |
| """Simple CHI prediction model""" | |
| weights = { | |
| 'wcc': 0.12, 'kpr': 0.19, 'hfsi': 0.24, | |
| 'becf': 0.21, 'sdb': 0.08, 'sbsp': 0.11, 'smvi': 0.05 | |
| } | |
| # Normalize parameters (simplified) | |
| params = { | |
| 'wcc': min(wcc/1.58, 1.0), | |
| 'kpr': min(kpr/2.0, 1.0), | |
| 'hfsi': 1 - min(hfsi/1.0, 1.0), | |
| 'becf': min(becf/6.0, 1.0), | |
| 'sdb': 1 - min(sdb/3.5, 1.0), | |
| 'sbsp': min(sbsp/1.2, 1.0), | |
| 'smvi': min(smvi/0.6, 1.0) | |
| } | |
| chi = sum(weights[p] * params[p] for p in weights) | |
| return chi | |
| if __name__ == '__main__': | |
| # Tōhoku 2011 example | |
| chi = predict_chi(1.56, 1.89, 0.31, 7.3, 0.8, 1.18, 0.38) | |
| print(f'CHI: {chi:.3f}') | |