![finetune idiom finetune idiom](https://img.buzzfeed.com/buzzfeed-static/static/2017-12/15/18/enhanced/buzzfeed-prod-fastlane-02/enhanced-28917-1513379213-2.jpg)
0.7 - 1.0) to generate more interesting text, while other frameworks work better between 0.2 - 0.5. You may want to reduce length appropriately.) (workaround: pass the truncate parameter to a generate function to only collect text until a specified end token.
#FINETUNE IDIOM FULL#
The method GPT-2 uses to generate text is slightly different than those like other packages like textgenrnn (specifically, generating the full text sequence purely in the GPU and decoding it later), which cannot easily be fixed without hacking the underlying model code. Differences Between gpt-2-simple And Other Text Generation Utilities NB: Restart the Python session first if you want to finetune on another dataset or load another model. See below to see what some of the CLI arguments do. Gpt_2_simple generate -temperature 1.0 -nsamples 20 -batch_size 20 -length 50 -prefix " " -truncate " " -include_prefix False -nfiles 5 Checkpoints trained using gpt-2-simple can be loaded using aitextgen as well.
![finetune idiom finetune idiom](https://www.idioms4you.com/img2/kettle-of-fish-scen01.png)
If you do not require using TensorFlow, I recommend using aitextgen instead. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time and resource usage.
#FINETUNE IDIOM FOR FREE#
You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended.
#FINETUNE IDIOM LICENSE#
Text generation output management from textgenrnn (MIT License / also created by me)įor finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly).Model finetuning from Neil Shepperd's fork of GPT-2 (MIT License).Model management from OpenAI's official GPT-2 repo (MIT License).This package incorporates and makes minimal low-level changes to:
![finetune idiom finetune idiom](https://i1.wp.com/www.designideas.pics/wp-content/uploads/formidable/13/idiom28-1.jpg)
Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifically the "small" 124M and "medium" 355M hyperparameter versions).