# Summary
The app was conceived with the idea of recreating and generate new dialogs for existing games.
In order to generate a dataset for training the steps followed were:
1. Download from [Assassins Creed Fandom Wiki](https://assassinscreed.fandom.com/wiki/Special:Export) from the category "Memories relived using the Animus HR-8.5".
2. Keep only text elements from XML.
3. Keep only the dialog section.
4. Parse wikimarkup with [wikitextparser](https://pypi.org/project/wikitextparser/).
5. Clean description of dialog's context.
Due to the small size of the dataset obtained, a transfer learning approach was considered based on a pretrained ["Dialog GPT" model](https://huggingface.co/microsoft/DialoGPT-small).
{}
transformers pytorch jax gpt2 text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# Summary
The app was conceived with the idea of recreating and generate new dialogs for existing games.
In order to generate a dataset for training the steps followed were:
1. Download from Assassins Creed Fandom Wiki from the category "Memories relived using the Animus HR-8.5".
2. Keep only text elements from XML.
3. Keep only the dialog section.
4. Parse wikimarkup with wikitextparser.
5. Clean description of dialog's context.
Due to the small size of the dataset obtained, a transfer learning approach was considered based on a pretrained "Dialog GPT" model.
# Summary
The app was conceived with the idea of recreating and generate new dialogs for existing games.
In order to generate a dataset for training the steps followed were:
1. Download from Assassins Creed Fandom Wiki from the category "Memories relived using the Animus HR-8.5".
2. Keep only text elements from XML.
3. Keep only the dialog section.
4. Parse wikimarkup with wikitextparser.
5. Clean description of dialog's context.
Due to the small size of the dataset obtained, a transfer learning approach was considered based on a pretrained "Dialog GPT" model.
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Summary
The app was conceived with the idea of recreating and generate new dialogs for existing games.
In order to generate a dataset for training the steps followed were:
1. Download from Assassins Creed Fandom Wiki from the category "Memories relived using the Animus HR-8.5".
2. Keep only text elements from XML.
3. Keep only the dialog section.
4. Parse wikimarkup with wikitextparser.
5. Clean description of dialog's context.
Due to the small size of the dataset obtained, a transfer learning approach was considered based on a pretrained "Dialog GPT" model.
passage: TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Summary
The app was conceived with the idea of recreating and generate new dialogs for existing games.
In order to generate a dataset for training the steps followed were:
1. Download from Assassins Creed Fandom Wiki from the category "Memories relived using the Animus HR-8.5".
2. Keep only text elements from XML.
3. Keep only the dialog section.
4. Parse wikimarkup with wikitextparser.
5. Clean description of dialog's context.
Due to the small size of the dataset obtained, a transfer learning approach was considered based on a pretrained "Dialog GPT" model.