JobSelect 0.11.1

A CLI Based AI Skill Classifier for Job Descriptions Build by Akshay Babu

Job Description Skill Classifier [JobSelect v0.10.5 & JobAnalyze 6k v1.0] (Multi-Label)

This project builds a lightweight text classification pipeline that predicts multiple technical skills from a job posting. Given a job description (optionally augmented with role and job type), the model outputs a ranked list of likely skills.

Installation:

• pip install jobselect

It uses:

• TF-IDF features over the combined text (job description + role + type)
• A PyTorch feed-forward neural network trained as a multi-label classifier
• Per-skill thresholding for evaluation and top-k ranked probabilities for inference

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What it does

1. Data preparation (model/prep/data_prep.py)

   • Reads cleaned job description data.
   • Normalizes/repairs common skill typos (e.g., tesnorflow/pytorch → tensorflow/pytorch).
   • Builds a multi-hot target vector of skills.
   • Fits a TF-IDF vectorizer (with n-grams) and splits into train/test.
   • Saves:
     • model/prep/prepared_data.npz (TF-IDF arrays + labels + indexes)
     • model/prep/vectorizer.pkl (fitted TF-IDF vectorizer)
     • model/prep/label_vocab.json (skill label vocabulary)

2. Model training (model/model.py)

   • Loads prepared TF-IDF arrays.
   • Defines a simple MLP:
     • Linear → ReLU → Dropout → Linear (one logit per skill)
   • Trains with BCEWithLogitsLoss (multi-label setting).
   • Saves:
     • model_out/skill_classifier.pt (model weights)
     • model_out/training_history.json (train/test loss curves)

3. Evaluation (model/eval.py)

   • Loads the trained model.
   • Applies a fixed sigmoid + threshold (0.5) to obtain binary skill predictions.
   • Reports:
     • Per-skill precision/recall/F1
     • Micro-F1 and Macro-F1
   • Compares against a simple baseline (frequency-driven / always-predict-most-frequent labels).

4. Prediction / Inference (model/pred.py)

   • Loads the TF-IDF vectorizer and trained model.
   • Creates TF-IDF features for the input text.
   • Outputs the top-k skills by probability.

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Use cases

• Resume/job-post matching (first-pass filtering of relevant skills)
• Job taxonomy building (discover recurring skills from postings)
• Recruiting analytics (aggregate predicted skill demand by seniority/role/type)
• Prototyping multi-label NLP classifiers (TF-IDF + MLP baseline)

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Project structure

.
├─ data/
│  ├─ raw/
│  │  └─ Job_descriptions.csv                 # Raw input dataset
│  ├─ sample_data/
│  │  └─ test.txt                             # Example JD, Role and Type for testing
│  └─ clean/
│     ├─ cleaned_job_descriptions.csv         # Cleaned master CSV
│     ├─ cleaned_job_descriptions_internships.csv
│     ├─ cleaned_job_descriptions_junior.csv
│     └─ cleaned_job_descriptions_senior.csv
│
├─ model/
│  ├─ prep/
│  │  ├─ data_prep.py                         # TF-IDF + multi-hot label creation + train/test split
│  │  ├─ sym_map.py                           # Synonym/phrase normalization map used during prep
│  │  ├─ vectorizer.pkl                       # Saved TF-IDF vectorizer (generated by data_prep)
│  │  ├─ prepared_data.npz                    # Saved arrays (generated by data_prep)
│  │  └─ label_vocab.json                     # Skill label vocabulary (generated by data_prep)
│  │
│  ├─ model.py                                # PyTorch multi-label classifier training
│  ├─ eval.py                                 # Thresholded evaluation + F1 metrics + baseline comparison
│  └─ pred.py                                 # Predict top-k skills for new text
│
├─ model_out/
│  ├─ skill_classifier.pt                     # Trained model weights (generated by model.py)
│  └─ training_history.json                   # Training loss history (generated by model.py)
│
├─ cli/
│  └─ jobselect.py                              # Rich terminal CLI for interactive prediction (uses model.pred.predict)
│
├─ test/
│  └─ test_model.py                           # Pytest checks expected artifacts exist in model_out/ and model/prep/
│
├─ notebooks/
│  ├─ 01_EDA.ipynb                            # Exploratory Data Analysis
│  └─ 02_Data_Engineering.ipynb               # Data engineering / cleaning notes
│
├─ pipeline.py                                # Executes notebooks + training/eval steps in order
├─ pyproject.toml                             # Installs as a cli tool
├─ requirements.txt
└─ README.md

---

Requirements

See requirements.txt for the exact dependencies.

---

Getting started

1) Clone and Install dependencies

git clone https://github.com/Ak47xdd/Job-Description-Analysis.git
pip install -r requirements.txt

2) Run data preparation (optional)

This builds the TF-IDF features and label vocabulary from the cleaned CSV.

python model/prep/data_prep.py

Expected outputs:

• model/prep/prepared_data.npz
• model/prep/vectorizer.pkl
• model/prep/label_vocab.json

3) Train the model (optional)

python model/model.py

Expected outputs:

• model_out/skill_classifier.pt
• model_out/training_history.json

4) Evaluate performance (optional)

python model/eval.py

Outputs include:

• Per-skill metrics (precision/recall/F1)
• Micro-F1 and Macro-F1
• Baseline comparison

5) Predict skills for a new job description

Option A: Use Python function

from model.pred import predict

data/sample_data/test.txt contains an example job description inside. You can also call the predict(job_desc, role=..., job_type=..., top_k=...) function from Python.

Option B: Use the interactive CLI

python cli/jobselect.py

or

pip install jobselect
jobselect

The CLI prompts for job description, role, and job type, then prints the top skills with probabilities.

---

How predictions work

• Text is concatenated as: "{job_desc} {role} {job_type}"
• TF-IDF transforms text into a fixed-size vector
• The network outputs one logit per skill
• Sigmoid converts logits → probabilities
• Skills are ranked by probability and the top-k are returned

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Important implementation notes

• Multi-label learning: Each skill is treated independently (binary relevance via sigmoid + BCEWithLogitsLoss).
• Evaluation threshold: model/eval.py uses a fixed threshold of 0.5. For production use, you may want per-label thresholds tuned on a validation set.
• Dataset size: The included notebooks and evaluation code suggest the dataset may be small; results can be limited by label frequency and data coverage.

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New features / capabilities

• Rich terminal CLI (cli/jobauto.py) using rich + pyfiglet for interactive top-skill display.
• Synonym/phrase normalization hook (model/prep/sym_map.py) applied during data preparation.
• Pipeline runner (pipeline.py) to execute notebooks and training steps in sequence.

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Customization ideas

• Improve text cleaning and skill normalization in data_prep.py
• Tune TF-IDF parameters (max_features, ngram_range, min_df)
• Replace the simple MLP with a stronger baseline (e.g., logistic regression on TF-IDF)
• Calibrate thresholds per label using validation data
• Add a CLI or web service endpoint for prediction

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References / Inspiration

This repository follows a common pattern for multi-label NLP baselines: TF-IDF features + a simple neural network + sigmoid-based multi-label outputs.