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moderation_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb5f1cbf7c576b2d1ea0ee801d90178b2d392ba37256573bc8454c38ae521854
+size 204952

test.py

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+import json
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+
+from transformers import AutoTokenizer, AutoModel
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer_embeddings = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+model_embeddings = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2').to(device)
+
+class ModerationModel(nn.Module):
+    def __init__(self):
+        input_size = 384
+        hidden_size = 128
+        output_size = 11
+        super(ModerationModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+    
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+def getEmbeddings(sentences):
+    encoded_input = tokenizer_embeddings(sentences, padding=True, truncation=True, return_tensors='pt').to(device)
+    with torch.no_grad():
+        model_output = model_embeddings(**encoded_input)
+    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+    return sentence_embeddings.cpu()
+
+def getEmb(text):
+    sentences = [text]
+    sentence_embeddings = getEmbeddings(sentences)
+    return sentence_embeddings.tolist()[0]
+
+def predict(model, embeddings):
+    model.eval()
+    with torch.no_grad():
+        embeddings_tensor = torch.tensor(embeddings, dtype=torch.float)
+        outputs = model(embeddings_tensor.unsqueeze(0))
+        predicted_scores = torch.sigmoid(outputs)
+        predicted_scores = predicted_scores.squeeze(0).tolist()
+        category_names = ["harassment", "harassment-threatening", "hate", "hate-threatening", "self-harm", "self-harm-instructions", "self-harm-intent", "sexual", "sexual-minors", "violence", "violence-graphic"]
+        
+        result = {category: score for category, score in zip(category_names, predicted_scores)}
+        detected = {category: score > 0.5 for category, score in zip(category_names, predicted_scores)}
+        detect_value = any(detected.values())
+        
+        return {"category_scores": result, 'detect': detected, 'detected': detect_value}
+
+
+print('Load model')
+moderation = ModerationModel()
+moderation.load_state_dict(torch.load('moderation_model.pth'))
+
+text = "I want to kill them."
+
+embeddings_for_prediction = getEmb(text)
+prediction = predict(moderation, embeddings_for_prediction)
+print(json.dumps(prediction,indent=4))