William Jeynes
25 lines
757 B
Python
25 lines
757 B
Python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
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import torch
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MODEL_PATH = "./roberta_classifier"
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tokenizer = RobertaTokenizer.from_pretrained(MODEL_PATH)
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model = RobertaForSequenceClassification.from_pretrained(MODEL_PATH)
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text2 = "High-profile political downplaying of COVID-19 (examples: President Trump saying 'it will go away' in March–August 2020)"
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text = "Multiple mirrored reuploads (2020–2023) put the clip on other channels with titles implying it was a genuine 1970s public information film."
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inputs = tokenizer(
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text,
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return_tensors="pt",
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truncation=True,
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padding=True
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)
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model.eval()
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with torch.no_grad():
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logits = model(**inputs).logits
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probs = torch.softmax(logits, dim=1)
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print(probs) |