JOBSELECT LABS / MODELS
Small models. Practical job intelligence.
Explore the AI models behind JobSelect's job-description analysis tools.
JobAnalyze 6k
A lightweight multi-label PyTorch model that predicts a fixed set of skills and keywords from a job description together with a provided role and job type.
Task
Multi-label classification
Framework
PyTorch
Features
TF-IDF
Hidden layer
32 units
Output
Skill probabilities
Vocabulary
48 labels
How it works
01
Job Description + Role + Job Type
02
Text Concatenation
03
TF-IDF
04
Linear Layer
05
ReLU
06
Dropout
07
Linear Layer
08
Sigmoid
09
Ranked Skills
EXAMPLE OUTPUT
Top skills
Illustrative frontend output only; no inference is performed here.
Evaluation snapshot
Micro-F1
0.624
Macro-F1
0.420
Baseline Micro-F1
0.538
Baseline Macro-F1
0.152
SAMPLE INFERENCE TEST
93.34% recall
14 / 15 target skills. Sample result, not universal accuracy.
Training data & label space
The documented published dataset contains 609 rows.
Know the limits
Fixed vocabulary. Predictions are constrained to the documented skill vocabulary.
Dataset coverage. Performance depends on labelled-data coverage and quality.
Not a hiring predictor. The model analyzes job-skill signals; it does not determine whether a candidate will be hired.
Scores are model outputs. They should not automatically be treated as calibrated real-world probabilities.