41 lines
1.6 KiB
Python
41 lines
1.6 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# 1. Load the fine-tuned model and tokenizer
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model_path = "T5Autocorrection_Book_Typos" # Path where your model and tokenizer are saved
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tokenizer_path = "T5Autocorrection_Book_Typos"
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
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# Set device (use GPU if available)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# 2. Define the function for sentence correction (inference)
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def correct_sentence(sentence):
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# Tokenize the input sentence
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inputs = tokenizer(sentence, return_tensors="pt", max_length=128, truncation=True, padding="max_length").to(device)
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# Generate prediction (corrected sentence)
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with torch.no_grad():
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outputs = model.generate(inputs["input_ids"], max_length=128, num_beams=5, early_stopping=True)
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# Decode the output ids to a string
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corrected_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return corrected_sentence
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# 3. Test the model on some example sentences
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incorrect_sentence = "mam rsd slovenske ludove piesne."
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corrected_sentence = correct_sentence(incorrect_sentence)
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print(f"Incorrect sentence: {incorrect_sentence}")
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print(f"Corrected sentence: {corrected_sentence}")
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# You can test more sentences like this:
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# incorrect_sentence2 = "The cat are playing with the dog."
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# corrected_sentence2 = correct_sentence(incorrect_sentence2)
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# print(f"Incorrect sentence: {incorrect_sentence2}")
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# print(f"Corrected sentence: {corrected_sentence2}")
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