pytorch jax gpt2 lm-head causal-lm en dataset:quotes-500K transformers text generation text-generation
.gitattributes README.md added_tokens.json config.json eval_results_lm.txt flax_model.msgpack merges.txt pytorch_model.bin special_tokens_map.json tokenizer_config.json training_args.bin vocab.json
---
language: 
- en
thumbnail: 
tags:
- text generation
license: 
datasets:
- quotes-500K
metrics:
- perplexity 

---

# Quotes Generator

## Model description

This is a GPT2 model fine-tuned on the Quotes-500K dataset.  

## Intended uses & limitations

For a given user prompt, it can generate motivational quotes starting with it.

#### How to use

```python
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("nandinib1999/quote-generator")
model = AutoModelWithLMHead.from_pretrained("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>