README.md

metadata

license: apache-2.0
language:
  - en
metrics:
  - precision
base_model:
  - distilbert/distilbert-base-uncased
pipeline_tag: text-classification
tags:
  - pytorch

Fake Job Predictor

Data

1. Data trained comes from this Kaggle repository: https://www.kaggle.com/datasets/shivamb/real-or-fake-fake-jobposting-prediction
2. Original data size is around 18k samples. To avoid the class imbalacing problem, it was undersampled the majority class (true jobs).
3. Final dataset used to train has a size of 4k sample.

Model

1. Multi-head neural network. One head is used for each feature (description, requirements, and benefits of the job).
2. Best metrics achieved (over validation data-split): Precision: 0.83, Recall: 0.65, F1-score: 0.71
3. Code used for training comes from this GitHub repo: https://github.com/sebassaras02/AdvancedDLCourse/blob/master/02_transformers_nlp/bert.ipynb

Components:

Text Encoder: distilbert-base-uncased is used to encode the textual input into a dense vector.

Future work:

Train over larger datasets and with more computer resources