| import transformers | |
| import datasets | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| from datasets import load_dataset # if loading a dataset | |
| model_name = 'logicreasoning/LogiT5' | |
| tokenize = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| device = 'cuda:0' if torch.cuda.is_available() else 'cpu' | |
| input_text = '' #your input text here must be a string | |
| input = tokenize(input_text, return_tensors='pt', padding=True).to(device) | |
| model = model.to(device) | |
| output = model.generate(*input, max_length=1024) | |
| prediction = tokenize.decode(output[0],skip_special_tokens=True) |