Text Classification
Scikit-learn
English
occupation-classification
human-in-the-loop
business-systems-analysis
tfidf
logistic-regression
onet
Instructions to use PabloRod11/occupation-hitl-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use PabloRod11/occupation-hitl-classifier with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("PabloRod11/occupation-hitl-classifier", "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 training_metadata.json from PabloRod11/occupation-hitl-classifier: direct link, hf CLI and curl.
- Browser
- Download file 897 Bytes
-
https://huggingface.co/PabloRod11/occupation-hitl-classifier/resolve/main/training_metadata.json
- Command line
-
hf download hf://PabloRod11/occupation-hitl-classifier/training_metadata.json
-
curl -L -o training_metadata.json https://huggingface.co/PabloRod11/occupation-hitl-classifier/resolve/main/training_metadata.json
897 Bytes
| { | |
| "model_version": "onet31-tfidf-8cc1b32a235b", | |
| "trained_at_utc": "2026-10-16T07:16:09.864107+00:00", | |
| "seed": 42, | |
| "python": "3.12.14", | |
| "dependencies": { | |
| "numpy": "2.5.4", | |
| "scipy": "1.18.1", | |
| "scikit-learn": "1.9.1" | |
| }, | |
| "dataset_sha256": { | |
| "train": "efebcb4d3fe95194fe7de4f9bd4bebf776fcc7d51646a3a677388edf65a58691", | |
| "validation": "16a080a05ebee845b9a17e7e2bb19ba045d16b8cf7adbbd2beedf83f2e12fe2b", | |
| "test": "d3aa7631bf16ecd7f963ee815d4d99439c47d26d6fb7f9a31cc05bcaf6da2554" | |
| }, | |
| "split_counts": { | |
| "train": 689, | |
| "validation": 143, | |
| "test": 173 | |
| }, | |
| "split_group_counts": { | |
| "train": 35, | |
| "validation": 10, | |
| "test": 10 | |
| }, | |
| "parameters": { | |
| "C": 4.0, | |
| "max_iter": 2000, | |
| "max_features": 20000, | |
| "ngram_range": [ | |
| 1, | |
| 2 | |
| ] | |
| }, | |
| "model_sha256": "128c9d92d87a434a9039a7a8ce8c116603c418ccce17e0ef04e8c01de5c10e11" | |
| } |