File size: 1,411 Bytes
9bcdf02 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
# Simple Inference Example
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import re
# Load model
tokenizer = AutoTokenizer.from_pretrained("junaid1993/distilroberta-bot-detection")
model = AutoModelForSequenceClassification.from_pretrained("junaid1993/distilroberta-bot-detection")
def preprocess_text(text):
if not isinstance(text, str):
return ""
text = re.sub(r'http\S+|www\.\S+', '', text)
text = re.sub(r'[@#]', '', text)
text = re.sub(r'[^\w\s]', '', text)
text = re.sub(r'\d+', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text.lower()
def predict_bot(text):
clean_text = preprocess_text(text)
inputs = tokenizer(clean_text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
bot_prob = probabilities[0][1].item()
prediction = "Bot" if bot_prob > 0.5 else "Human"
return {"prediction": prediction, "bot_probability": bot_prob}
# Example usage
examples = [
"🔥 AMAZING DEAL! Get 90% OFF now!",
"Just finished reading a great book about AI."
]
for text in examples:
result = predict_bot(text)
print(f"Text: {text}")
print(f"Prediction: {result['prediction']} ({result['bot_probability']:.3f})")
print("-" * 50)
|