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# 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
{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer", "gpt2", "generation"], "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"}], "model-index": [{"name": "resumes_model", "results": []}]}
transformers pytorch gpt2 text-generation generated_from_trainer generation en dataset:mpuig/job-experience license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #generation #en #dataset-mpuig/job-experience #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# 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
- 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
# 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
- 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
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #generation #en #dataset-mpuig/job-experience #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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
- 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
TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #generation #en #dataset-mpuig/job-experience #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# 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
- 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