fix-percentage

nlp/toxycity_detector.py

@@ -1,13 +1,30 @@
 from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+import torch.nn.functional as F
 
-# load tokenizer and model weights
+# Load tokenizer and model weights
 tokenizer = BertTokenizer.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier')
 model = BertForSequenceClassification.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier')
 
-
 def detector(text):
-    # prepare the input
+    # Prepare the input
     batch = tokenizer.encode(text, return_tensors='pt')
 
-    # inference
+    # Inference
-    model(batch)
+    with torch.no_grad():
+        result = model(batch)
+    
+    # Get logits
+    logits = result.logits
+
+    # Convert logits to probabilities using softmax
+    probabilities = F.softmax(logits, dim=1)
+
+    return probabilities[0][1].item()
+
+if __name__ == "__main__":
+    import sys
+    if len(sys.argv) > 1:
+        p = detector(sys.argv[1])
+        toxicity_percentage = p * 100  # Assuming index 1 is for toxic class
+        print(f"Toxicity Probability: {toxicity_percentage:.2f}%")

requirements.txt

@@ -1,4 +1,4 @@
 aiohttp
 redis[hiredis]
-tensorflow
+torch
 transformers