Custom, without any pretraining, training with PyTorch
Hey!The tokenizer options in HuggingFace are extremely useful and easy to work with.I’d like to build a custom pipeline that:inputs custom data ,not something in Datasets (I know how to do this in HF).tokenizes it with a HF tokenizer pipeline (this I know how to do, too).uses the above to feed into a custom (no pretraining, not a specific architecture) PyTorch model,optimizes with any viable PyTorch loss function.So basically, I want to work within the HF framework for the text’s preprocessing and tokenizing, while still doing whatever I wish in PyTorch afterwards…Is this feasible?Thanks!