
# 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>
{"language": ["en"], "license": "cc", "tags": ["text generation"], "datasets": ["quotes-500K"], "metrics": ["perplexity"]}
transformers pytorch jax gpt2 text-generation text generation en dataset:quotes-500K license:cc autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #text generation #en #dataset-quotes-500K #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# 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



## 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>
# 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 ## 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>
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #text generation #en #dataset-quotes-500K #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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 ## 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>
passage: TAGS
#transformers #pytorch #jax #gpt2 #text-generation #text generation #en #dataset-quotes-500K #license-cc #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# 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## 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>