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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
import torch
# Hate Speech
tokenizer = AutoTokenizer.from_pretrained(
"mrm8488/distilroberta-finetuned-tweets-hate-speech")
model = AutoModelForSequenceClassification.from_pretrained(
"mrm8488/distilroberta-finetuned-tweets-hate-speech")
def classify_hatespeech(sentence):
preprocessed_text = sentence.strip().replace("\n", "")
inputs = tokenizer(preprocessed_text, return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)
outputs = model(**inputs, labels=labels)
logits = outputs.logits
probs = torch.softmax(logits, dim=1)
nice = torch.flatten(probs).detach().numpy()[0]
return "{:.2f}".format(nice)
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