| | --- |
| | datasets: |
| | - jstet/quotes-500k |
| | language: |
| | - en |
| | metrics: |
| | - perplexity |
| | tags: |
| | - text-generation-inference |
| | --- |
| | # Quotes Generator |
| |
|
| | ## Project description |
| |
|
| | Fine-tuned GPT2 model on the Quotes-500K dataset. For a given user prompt, it can generate motivational quotes starting with it. The model weights are deployed on Hugging Face Models Hub. |
| | Check it out at https://huggingface.co/nandinib1999/quote-generator. |
| |
|
| | ## Training data |
| |
|
| | This is the distribution of the total dataset into training, validation and test dataset for the fine-tuning task. |
| |
|
| | <table style="width:30%"> |
| | <tr> |
| | <th>train</th> |
| | <td>349796</td> |
| | </tr> |
| | <tr> |
| | <th>validation</th> |
| | <td>99942</td> |
| | </tr> |
| | <tr> |
| | <th>test</th> |
| | <td>49971</td> |
| | </tr> |
| | </table> |
| |
|
| | ## Training procedure |
| |
|
| | The model was fine-tuned using the Google Colab GPU for one epoch. The weights of the pre-trained GPT2 model were used as a base. |
| |
|
| | ## Eval results |
| |
|
| | <table style="width:30%"> |
| | <tr> |
| | <th>Epoch</th> |
| | <th>Perplexity</th> |
| | </tr> |
| | <tr> |
| | <td>1</td> |
| | <td>15.180</td> |
| | </tr> |
| | </table> |
