| --- |
| 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 "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 |
| - [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 |
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|
|
| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.13.0+cu116 |
| - Datasets 2.8.0 |
| - Tokenizers 0.13.2 |
