||1 year ago|
|model||1 year ago|
|.gitignore||1 year ago|
|LICENSE||1 year ago|
|README.md||1 year ago|
|convert_raw_model.py||1 year ago|
|convert_text.py||1 year ago|
|gptc_scratch.sb3||1 year ago|
gptc_scratch uses the same algorithm as GPTC, in a Scratch project. However, it only supports two categories, and they are numbered (1 and 2) rather than named.
Models are formatted differently than in the Python version of GPTC. A model consists of two files, each containing the text for a category. gptc_scratch does not have a tokenizer, so each line must consist of one lowercase word with no other characters. To load the model, right-click "Category 1 Words" in the project, click "import," then choose one of the files. Do the same thing again with "Category 2 Words" and the other file.
The text to classify must be stored in the same format as the model files. Follow the same procedure that you used for loading the model files, but with "Words in text to classify" and the file containing the text to classify. Then, click "Compile." If the button changes to a lighter color, wait for it to change back. Next, click "classify." This should update the "Cat1" and "Cat2" variables with the confidence valued for the given text and categories.