diff --git a/app.py b/app.py index 7863e790b14839c1e73a6f72f9485a52e367d414..cd5c2a596517b90ce053f3fdfca8cb5a20b15d3e 100644 --- a/app.py +++ b/app.py @@ -4,6 +4,7 @@ from fastapi.staticfiles import StaticFiles from pydantic import BaseModel, Field from typing import Optional from cold.classifier import ToxicTextClassifier +import torch app = FastAPI() @@ -17,6 +18,8 @@ app.add_middleware( model = ToxicTextClassifier() +model.load_state_dict(torch.load("output/lited_best.pth",map_location="cpu")) + class PredictionInput(BaseModel): text: str = Field(..., title="Text to classify", description="The text to classify for malicious content") diff --git a/cold/__pycache__/classifier.cpython-310.pyc b/cold/__pycache__/classifier.cpython-310.pyc index b4fd119adc6747740bc54e6632dbf6b430c7a782..fdd3de5ae180efe2a75b53ab6f00965218e4c835 100644 Binary files a/cold/__pycache__/classifier.cpython-310.pyc and b/cold/__pycache__/classifier.cpython-310.pyc differ diff --git a/cold/__pycache__/dynamic_conv.cpython-310.pyc b/cold/__pycache__/dynamic_conv.cpython-310.pyc index 4cc1a8a6285fef4d2f147b3e3720b0c5c552da0e..43a54719dd56c419493684a682e7218d10355e36 100644 Binary files a/cold/__pycache__/dynamic_conv.cpython-310.pyc and b/cold/__pycache__/dynamic_conv.cpython-310.pyc differ diff --git a/cold/__pycache__/text_cnn.cpython-310.pyc b/cold/__pycache__/text_cnn.cpython-310.pyc index ba647b3409cd387022976dba5f08d509a0f20f5e..0af4b3044ba635240e0402c042c040e3b06e169f 100644 Binary files a/cold/__pycache__/text_cnn.cpython-310.pyc and b/cold/__pycache__/text_cnn.cpython-310.pyc differ diff --git a/cold/classifier.py b/cold/classifier.py index f3b6acfe506a04fcafa66ace49a3c42e550e840a..12635801d67d55b0e73a444665785b3c42dc9389 100644 --- a/cold/classifier.py +++ b/cold/classifier.py @@ -1,11 +1,14 @@ - +from matplotlib import ticker, transforms +from matplotlib.colors import LinearSegmentedColormap import torch import torch.nn as nn from transformers import BertModel, BertTokenizer from torch.optim import AdamW, lr_scheduler from .text_cnn import DynamicTextCNN from tqdm import tqdm +import io import os +from PIL import Image class ToxicTextClassifier(nn.Module): def __init__(self, @@ -16,7 +19,7 @@ class ToxicTextClassifier(nn.Module): fc_dim=128, num_classes=2, dropout=0.1, - name='lite'): + name='lited_best'): super().__init__() self.tokenizer = BertTokenizer.from_pretrained(bert_name,from_tf=True) self.bert = BertModel.from_pretrained(bert_name) @@ -41,6 +44,15 @@ class ToxicTextClassifier(nn.Module): self.criterion = nn.CrossEntropyLoss() self._rebuild_optimizer() + + self.warmup_scheduler = None + + def _get_warmup_scheduler(self, warmup_steps=1000): + def lr_lambda(current_step): + if current_step < warmup_steps: + return float(current_step) / float(max(1, warmup_steps)) + return 1.0 + return lr_scheduler.LambdaLR(self.optimizer, lr_lambda) def _rebuild_optimizer(self): param_groups = [ @@ -66,14 +78,6 @@ class ToxicTextClassifier(nn.Module): patience=2, ) - self.warmup_scheduler = None - - def _get_warmup_scheduler(self, warmup_steps=1000): - def lr_lambda(current_step): - if current_step < warmup_steps: - return float(current_step) / float(max(1, warmup_steps)) - return 1.0 - return lr_scheduler.LambdaLR(self.optimizer, lr_lambda) def forward(self, input_ids, attention_mask, token_type_ids=None): bert_out = self.bert( @@ -81,7 +85,7 @@ class ToxicTextClassifier(nn.Module): attention_mask=attention_mask, token_type_ids=token_type_ids, output_hidden_states=True, - ) + ) hidden = torch.cat(bert_out.hidden_states[-2:], dim=-1) feat = self.text_cnn(hidden) return self.classifier(feat) @@ -115,7 +119,15 @@ class ToxicTextClassifier(nn.Module): pbar.set_postfix({'loss': f'{loss.item():.4f}'}) + epoch_acc = correct / total if total > 0 else 0 + metrics = { + 'loss': val_loss / len(val_loader), + 'acc': epoch_acc, + 'report': classification_report(all_labels, all_preds, target_names=['non-toxic','toxic']), + 'confusion_matrix': confusion_matrix(all_labels, all_preds) + } torch.cuda.empty_cache() + return metrics def train_model(self, train_loader, val_loader, num_epochs=3, device='cpu', @@ -139,6 +151,7 @@ class ToxicTextClassifier(nn.Module): if logdir is None: logdir = f'runs/{self.name}' + writer = SummaryWriter(logdir) for epoch in range(1, num_epochs + 1): print(f"\nEpoch {epoch}/{num_epochs}") @@ -146,12 +159,12 @@ class ToxicTextClassifier(nn.Module): total_loss = 0 correct = 0 total = 0 - + self.warmup_scheduler = self._get_warmup_scheduler(warmup_steps) if epoch == 2: print("Unfreezing 4 layers of BERT") - self.unfrozen_layers = 4 + self.unfrozen_layers = 2 self._rebuild_optimizer() pbar = tqdm(train_loader, desc='Training') @@ -172,13 +185,22 @@ class ToxicTextClassifier(nn.Module): if global_step < warmup_steps: self.warmup_scheduler.step() - + for i, group in enumerate(self.optimizer.param_groups): + writer.add_scalar(f'LR/group_{i}', group['lr'], global_step) + + for name, param in self.named_parameters(): + if "convs" in name: + grad_norm = param.grad.norm().item() + writer.add_scalar(f'Gradients/{name}', grad_norm, global_step) + total_loss += loss.item() preds = torch.argmax(logits, dim=1) correct += (preds == labels).sum().item() total += labels.size(0) acc = correct / total + writer.add_scalar('Loss/train', loss.item(), global_step) + writer.add_scalar('Acc/train', acc, global_step) pbar.set_postfix({'loss': f'{loss.item():.4f}', 'acc': f'{acc:.4f}'}) global_step += 1 @@ -196,7 +218,10 @@ class ToxicTextClassifier(nn.Module): report_text = metrics['report'] conf_mat = metrics['confusion_matrix'] print(report_text) - + writer.add_text('Classification Report', report_text, global_step) + writer.add_scalar('Loss/vali', val_loss, global_step) + writer.add_scalar('Acc/vali', val_acc, global_step) + if val_loss < best_val_loss: best_val_loss = val_loss best_model_state = self.state_dict() @@ -210,11 +235,51 @@ class ToxicTextClassifier(nn.Module): if epochs_no_improve >= early_stop_patience: print(f"Early stopping triggered at step {global_step}!") self.load_state_dict(best_model_state) + writer.close() return - + flame_colors = ['#ffffcc', '#ffeda0', '#feb24c', '#fd8d3c', '#f03b20', '#bd0026'] + flame_cmap = LinearSegmentedColormap.from_list("flame", flame_colors, N=256) + fig, ax = plt.subplots(figsize=(8, 6)) + sns.set_theme(font_scale=1.4) + sns.heatmap( + conf_mat, + annot=True, + fmt='d', + cmap=flame_cmap, + linewidths=0.5, + linecolor='gray', + square=True, + cbar=True, + xticklabels=['non-toxic', 'toxic'], + yticklabels=['non-toxic', 'toxic'], + annot_kws={"size": 16, "weight": "bold"} + ) + + ax.set_xlabel('Predicted', fontsize=14, labelpad=10) + ax.set_ylabel('True', fontsize=14, labelpad=10) + ax.set_title('Confusion Matrix', fontsize=16, pad=12) + + ax.xaxis.set_tick_params(labelsize=12) + ax.yaxis.set_tick_params(labelsize=12) + ax.xaxis.set_major_locator(ticker.FixedLocator([0.5, 1.5])) + ax.yaxis.set_major_locator(ticker.FixedLocator([0.5, 1.5])) + + buf = io.BytesIO() + plt.tight_layout() + plt.savefig(buf, format='png', dpi=150) + plt.savefig(f'data/{self.name}/conf_matrix_step{global_step}.pdf', format='pdf', bbox_inches='tight') + buf.seek(0) + image = Image.open(buf) + image_tensor = ToTensor()(image) + writer.add_image('Confusion Matrix', image_tensor, global_step) + + buf.close() + plt.close(fig) + self.train() + writer.close() def predict(self, texts, device='cpu'): """Used for inference. Predicts the class of the input text. diff --git a/cold/dynamic_conv.py b/cold/dynamic_conv.py index 620e40932b88d9e61c6ef19668cf0aa079b1d315..73ae0496667a44446639e5b0f97ed54336600b46 100644 --- a/cold/dynamic_conv.py +++ b/cold/dynamic_conv.py @@ -11,14 +11,8 @@ class DynamicConv1d(nn.Module): padding='same') for _ in range(K) ]) - # self.attn = nn.Sequential( - # nn.AdaptiveAvgPool2d(1), - # nn.Conv2d(in_channels, max(in_channels // reduction, 1), 1), - # nn.ReLU(inplace=True), - # nn.Conv2d(max(in_channels // reduction, 1), max(in_channels // reduction, 1), 1), # 添加额外的非线性层 - # nn.ReLU(inplace=True), - # nn.Conv2d(max(in_channels // reduction, 1), max(K,1), 1) - # ) + # self.residual_conv = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=1) + self.attn = nn.Sequential( nn.AdaptiveAvgPool1d(1), nn.Conv1d(in_channels, in_channels // reduction, 1), @@ -35,5 +29,6 @@ class DynamicConv1d(nn.Module): attn_weights = F.softmax(attn_logits, dim=1) conv_outs = [conv(x) for conv in self.convs] out = sum(w * o for w, o in zip(attn_weights.split(1, dim=1), conv_outs)) - + # residual connection return out + x + diff --git a/out/404.html b/out/404.html index 8bb21e0032c4744792963c57321a0d5c6994de2a..9c26d45ad9986d98153fc4cd773c8dfd0e7cdc56 100644 --- a/out/404.html +++ b/out/404.html @@ -1 +1 @@ -404: This page could not be found.Lite Detective

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.Lite Detective

404

This page could not be found.

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diff --git a/out/_next/static/media/KaTeX_Typewriter-Regular.a8709e36.woff2 b/out/_next/static/media/KaTeX_Typewriter-Regular.a8709e36.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..771f1af705f5cef5f578b3a1e7d8eff66f9b76b0 Binary files /dev/null and b/out/_next/static/media/KaTeX_Typewriter-Regular.a8709e36.woff2 differ diff --git a/out/_next/static/media/KaTeX_Typewriter-Regular.d97aaf4a.ttf b/out/_next/static/media/KaTeX_Typewriter-Regular.d97aaf4a.ttf new file mode 100644 index 0000000000000000000000000000000000000000..c83252c5714c71a3e0ec62195884167339a0129b Binary files /dev/null and b/out/_next/static/media/KaTeX_Typewriter-Regular.d97aaf4a.ttf differ diff --git a/out/index.html b/out/index.html index b9e94cd8bea9f4c555b60ad7a91ed4f3f69d7af7..eb4b14a7f0a72e7219b31993b21824330a46028c 100644 --- a/out/index.html +++ b/out/index.html @@ -1,4 +1,4 @@ -Lite Detective

LiteDetective🕵️

Lightweight and Accurate Chinese Toxic Text Detection

Disclaimer: The paper contains content that may be profane, vulgar, or offensive.
Qinjian Zhao *1
Mingcheng Hu *1
{1zhaoq ,1humin } @ kean.edu
* Equal Contribution
CPS 3320 2025
Kean University
PaperCodearXiv

Abstract

Harmful content detection is a critical task for +Lite Detective

LiteDetective🕵️

Lightweight and Accurate Chinese Toxic Text Detection

Disclaimer: The paper contains content that may be profane, vulgar, or offensive.
Qinjian Zhao *1
Mingcheng Hu *1
{1zhaoq ,1humin } @ kean.edu
* Equal Contribution
CPS 3320 2025
Kean University
PaperCodeACL 2025

Abstract

Harmful content detection is a critical task for any social media platform, as the presence of misinformation, age, gender, and racial discrim- ination can lead to a reduction in active users. @@ -15,16 +15,4 @@ guided seed examples. Next, we employ a large LLM (Qwen-3) for context-aware and context- free data augmentation. Finally, we integrate BERT embeddings with a Dynamic TextCNN -classifier on our custom dataset.

Method

Our model combines a lightweight transformer encoder with a multi-scale CNN feature extractor and a context-aware classification head.

Online Demo

草尼玛的一群煞笔
🐶日的哈麻批，该放假不放假，上你妈批的课啊
老哥🀄️啊，因为人领导每天都是放假捏捏 😋
密码的周六能不能放过我一次
\ No newline at end of file +classifier on our custom dataset.

Online Demo

Model Architecture

Model Architecture

BERT Encoder

We utilize hfl/chinese-roberta-wwm-ext as the base transformer, with hidden states concatenation:

Hconcat=[HL−1;HL]∈RL×1536H_{\text{concat}} = [H_{L-1}; H_L] \in \mathbb{R}^{L \times 1536}

Dynamic Multi-Scale Convolution

  • Kernel Adaptation: Each DynamicConv1d layer contains K=4K=4 parallel kernels with attention mechanism:
    α=Softmax(W2σ(W1AvgPool(x)))\alpha = \text{Softmax}(W_2\sigma(W_1\text{AvgPool}(x)))
    Output=∑k=1Kα(k)⊗Convk(x)+x\text{Output} = \sum_{k=1}^K \alpha^{(k)} \otimes \text{Conv}_k(x) + x

Hierarchical Classification

The final prediction head implements dimension reduction with layer normalization:

y^=W3(Dropout(σ(W2(Dropout(σ(W1Fpooled)))))\hat{y} = W_3(\text{Dropout}(\sigma(W_2(\text{Dropout}(\sigma(W_1F_{\text{pooled}})))))
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