---
license: mit
tags:
- generated_from_trainer
model-index:
- name: resumes_model
  results: []
datasets:
- mpuig/job-experience
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