| 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 |