mpuig/job-experience

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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 &#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

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
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: 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