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| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("davebulaval/MeaningBERT") | |
| scorer = AutoModelForSequenceClassification.from_pretrained("davebulaval/MeaningBERT") | |
| scorer.eval() | |
| documents = [ | |
| "He wanted to make them pay.", | |
| "This sandwich looks delicious.", | |
| "He wants to eat.", | |
| ] | |
| simplifications = [ | |
| "He wanted to make them pay.", | |
| "This sandwich looks delicious.", | |
| "Whatever, whenever, this is a sentence.", | |
| ] | |
| # We tokenize the text as a pair and return Pytorch Tensors | |
| tokenize_text = tokenizer( | |
| documents, simplifications, truncation=True, padding=True, return_tensors="pt" | |
| ) | |
| with torch.no_grad(): | |
| # We process the text | |
| scores = scorer(**tokenize_text) | |
| print(scores.logits.tolist()) | |