Upload bert-traditional-chinese-classifier with accuracy 87.71%

README.md

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+---
+language:
+- zh
+tags:
+- text-classification
+- chinese
+- traditional-chinese
+- bert
+- pytorch
+license: apache-2.0
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+model-index:
+- name: bert-traditional-chinese-classifier
+  results:
+  - task:
+      type: text-classification
+      name: Traditional Chinese Classification
+    metrics:
+    - type: accuracy
+      value: 0.8771
+      name: Accuracy
+    - type: f1
+      value: 0.8771
+      name: F1 Score
+---
+
+# BERT Traditional Chinese Classifier v7
+
+這是一個用於區分大陸繁體和台灣繁體的 BERT 分類模型。
+
+## 模型描述
+
+- **基礎模型**: ckiplab/bert-base-chinese
+- **任務**: 繁體中文文本分類（大陸繁體 vs 台灣繁體）
+- **準確率**: 87.71%
+- **訓練數據量**: 156824 樣本
+
+## 特點
+
+- ✅ 支持長文本處理（最大長度 384 tokens）
+- ✅ 使用 Focal Loss 處理類別不平衡
+- ✅ Multi-Sample Dropout 提高泛化能力
+- ✅ 分層學習率優化
+- ✅ 漸進解凍訓練策略
+
+## 使用方法
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# 載入模型和 tokenizer
+tokenizer = AutoTokenizer.from_pretrained("renhehuang/bert-traditional-chinese-classifier")
+model = torch.load("pytorch_model.bin")  # 需要自定義模型類
+
+# 預測
+text = "您的繁體中文文本"
+inputs = tokenizer(text, return_tensors="pt", max_length=384, truncation=True)
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(-1).item()
+
+# 0: 大陸繁體, 1: 台灣繁體
+label = "大陸繁體" if prediction == 0 else "台灣繁體"
+print(f"預測: {label}")
+```
+
+## 訓練配置
+
+- **Batch Size**: 16
+- **Learning Rate**: 2e-05 (base), 4e-05 (head)
+- **Epochs**: 4
+- **Max Length**: 384
+- **Loss Function**: Focal Loss (gamma=2.0)
+
+## 性能指標
+
+### 整體性能
+- 準確率: 87.71%
+
+### 分層性能（按文本長度）
+詳見評估報告
+
+## 引用
+
+如果您使用此模型，請引用：
+
+```
+@misc{bert-traditional-chinese-classifier,
+  author = {renhehuang},
+  title = {BERT Traditional Chinese Classifier},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/renhehuang/bert-traditional-chinese-classifier}}
+}
+```
+
+## 授權
+
+Apache 2.0
+
+## 聯繫方式
+
+如有問題，請在 Hugging Face 模型頁面或 GitHub 上提出 issue。

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizerFast",
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d572859e01ace15886178219e2e460a45169cb651e6fc72c11cc6b0a483e6ca7
+size 409201943

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_config.json

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+{
+  "model_name": "ckiplab/bert-base-chinese",
+  "max_length": 384,
+  "batch_size": 16,
+  "learning_rate_base": 2e-05,
+  "learning_rate_head": 4e-05,
+  "epochs": 4,
+  "use_focal_loss": true,
+  "focal_gamma": 2.0,
+  "focal_alpha": 0.4,
+  "label_smoothing": 0.05,
+  "num_train_samples": 156824,
+  "num_eval_samples": 39207,
+  "accuracy": 0.877139286351927
+}