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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) | |