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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