Instructions to use kurtulus7182/SEPrimer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use kurtulus7182/SEPrimer with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("kurtulus7182/SEPrimer", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download config.schema.json from kurtulus7182/SEPrimer: direct link, hf CLI and curl.
- Browser
- Download file 2.31 kB
-
https://huggingface.co/kurtulus7182/SEPrimer/resolve/main/config.schema.json
- Command line
-
hf download hf://kurtulus7182/SEPrimer/config.schema.json
-
curl -L -o config.schema.json https://huggingface.co/kurtulus7182/SEPrimer/resolve/main/config.schema.json
2.31 kB
| { | |
| "$schema": "https://json-schema.org/draft/2020-12/schema", | |
| "title": "Primer v2 model config", | |
| "type": "object", | |
| "required": [ | |
| "schema_version", | |
| "features", | |
| "ranges", | |
| "labels", | |
| "decision_threshold", | |
| "acceptance_accuracy", | |
| "metrics", | |
| "input", | |
| "output", | |
| "model_sha256", | |
| "status" | |
| ], | |
| "properties": { | |
| "schema_version": { | |
| "const": 1 | |
| }, | |
| "features": { | |
| "const": [ | |
| "seviye", | |
| "karar_suresi_sn", | |
| "onceki_yanlis_sayisi", | |
| "ipucu_sayisi", | |
| "sahne_no" | |
| ] | |
| }, | |
| "decision_threshold": { | |
| "const": 0.6 | |
| }, | |
| "acceptance_accuracy": { | |
| "const": 0.75 | |
| }, | |
| "tolerance": { | |
| "const": 0.0001 | |
| }, | |
| "status": { | |
| "enum": [ | |
| "synthetic-demo", | |
| "real-data-candidate" | |
| ] | |
| }, | |
| "model_sha256": { | |
| "type": "string", | |
| "pattern": "^[a-f0-9]{64}$" | |
| }, | |
| "input": { | |
| "type": "object", | |
| "required": [ | |
| "name", | |
| "dtype", | |
| "shape" | |
| ], | |
| "properties": { | |
| "name": { | |
| "const": "features" | |
| }, | |
| "dtype": { | |
| "const": "float32" | |
| }, | |
| "shape": { | |
| "const": [ | |
| null, | |
| 5 | |
| ] | |
| } | |
| } | |
| }, | |
| "output": { | |
| "type": "object", | |
| "required": [ | |
| "name", | |
| "positive_class_index" | |
| ], | |
| "properties": { | |
| "name": { | |
| "type": "string" | |
| }, | |
| "positive_class_index": { | |
| "const": 1 | |
| } | |
| } | |
| }, | |
| "metrics": { | |
| "type": "object", | |
| "required": [ | |
| "accuracy", | |
| "onnx_max_abs_error", | |
| "real_sessions" | |
| ], | |
| "properties": { | |
| "accuracy": { | |
| "type": "number", | |
| "minimum": 0.75, | |
| "maximum": 1 | |
| }, | |
| "majority_baseline": { | |
| "type": "number", | |
| "minimum": 0, | |
| "maximum": 1 | |
| }, | |
| "onnx_max_abs_error": { | |
| "type": "number", | |
| "minimum": 0, | |
| "exclusiveMaximum": 0.0001 | |
| }, | |
| "real_sessions": { | |
| "type": "integer", | |
| "minimum": 0 | |
| } | |
| } | |
| } | |
| } | |
| } | |