Instructions to use vyver7952/autotrain-foreign-exchange-idr-usd-50442120509 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vyver7952/autotrain-foreign-exchange-idr-usd-50442120509 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vyver7952/autotrain-foreign-exchange-idr-usd-50442120509", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - autotrain | |
| - tabular | |
| - regression | |
| - tabular-regression | |
| datasets: | |
| - vyver7952/autotrain-data-foreign-exchange-idr-usd | |
| co2_eq_emissions: | |
| emissions: 0.1187798673649329 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Single Column Regression | |
| - Model ID: 50442120509 | |
| - CO2 Emissions (in grams): 0.1188 | |
| ## Validation Metrics | |
| - Loss: 13.859 | |
| - R2: 0.999 | |
| - MSE: 192.085 | |
| - MAE: 13.842 | |
| - RMSLE: 0.001 | |
| ## Usage | |
| ```python | |
| import json | |
| import joblib | |
| import pandas as pd | |
| model = joblib.load('model.joblib') | |
| config = json.load(open('config.json')) | |
| features = config['features'] | |
| # data = pd.read_csv("data.csv") | |
| data = data[features] | |
| data.columns = ["feat_" + str(col) for col in data.columns] | |
| predictions = model.predict(data) # or model.predict_proba(data) | |
| ``` |