readme: ---
  license: mit
  tags:
  - generated_from_trainer
  - gpt2
  - generation
  model-index:
  - name: resumes_model
    results: []
  datasets:
  - mpuig/job-experience
  widget:
  - text: As a Software Developer, I 
    example_title: Software Developer
  - text: As a Software Architect, I 
    example_title: Software Architect
  - text: As a web developer, I 
    example_title: Web Developer
  
  language:
  - en
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Model Card for mpuig/job-experience
  
  This model is a fine-tuned version of [GPT-2](https://huggingface.co/gpt2) 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 &#34;As a Software Engineer, I &#34;, the model generates a complete new sentence related to the job title (&#34;Software Engineer&#34;) like:
  
  &#34;_As a software architect, I coordinated with the Marketing department to identify problems encountered and provide solutions to resolve them._&#34;
  
  - **Resources for more information:** More information needed
      - [GitHub Repo](https://github.com/mpuig/gpt2-fine-tuning/)
  
  ## 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