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Model Card for mpuig/job-experience

This model is a fine-tuned version of GPT-2 to generate fake job experience descriptions.

While this may not have practical applications in the real world, it served as a valuable learning experience for understanding the process of fine-tuning a language learning model. Through this repository, I hope to share my insights and findings on the capabilities and limitations of GPT-2 in generating job experiences.

The goal was to obtain a model where, starting with a sentence like "As a Software Engineer, I ", the model generates a complete new sentence related to the job title ("Software Engineer") like:

"As a software architect, I coordinated with the Marketing department to identify problems encountered and provide solutions to resolve them."

  • Resources for more information: More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0

Training results

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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Dataset used to train mpuig/job-experience