| | ---
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| | language: zh
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| | license: apache-2.0
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| | tags:
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| | - sentiment-analysis
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| | - chinese
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| | - finance
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| | - finbert
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| | - crypto
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| | - text-classification
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| | - news
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| | datasets:
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| | - custom
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| | metrics:
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| | - accuracy
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| | - f1
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| | - precision
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| | - recall
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| | model-index:
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| | - name: Chinese Financial Sentiment Analysis (Crypto)
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| | results:
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| | - task:
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| | type: text-classification
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| | name: Sentiment Analysis
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| | metrics:
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| | - type: accuracy
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| | value: 0.8484
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| | name: Accuracy
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| | - type: f1
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| | value: 0.8488
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| | name: F1 Score
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| | - type: precision
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| | value: 0.8536
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| | name: Precision
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| | - type: recall
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| | value: 0.8484
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| | name: Recall
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| | ---
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| |
|
| | # Chinese Financial Sentiment Analysis Model (Crypto Focus)
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| |
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| | 中文金融情感分析模型（加密货币领域）
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| |
|
| | ## 模型描述 | Model Description
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| |
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| | 本模型基于 `yiyanghkust/finbert-tone-chinese` 经过多轮迭代微调，专门用于分析中文加密货币相关新闻和社交媒体内容的情感倾向。模型可以识别三种情感类别：正面（Positive）、中性（Neutral）和负面（Negative）。
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| |
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| | 训练数据经过 Claude AI 逐条人工审阅、纠正标注错误，确保数据质量。
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| |
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| | This model is iteratively fine-tuned from `yiyanghkust/finbert-tone-chinese`, specifically designed for sentiment analysis of Chinese cryptocurrency-related news and social media content. It classifies text into three sentiment categories: Positive, Neutral, and Negative.
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| |
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| | Training data is manually reviewed and corrected entry-by-entry by Claude AI to ensure annotation quality.
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| |
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| | ## 训练数据 | Training Data
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| |
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| | - **数据量 | Size**: 2208条人工审阅标注的中文金融新闻 | 2208 manually reviewed Chinese financial news articles
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| | - **数据来源 | Source**: 加密货币相关新闻和推文 | Cryptocurrency-related news and tweets
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| | - **标注方式 | Annotation**: 模型预测 + Claude AI 逐条审阅纠正 | Model prediction + Claude AI manual review & correction
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| | - **数据分布 | Distribution**:
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| | - Positive（正面）: 734条 (33.2%)
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| | - Neutral（中性）: 899条 (40.7%)
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| | - Negative（负面）: 575条 (26.0%)
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| |
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| | ## 性能指标 | Performance Metrics
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| |
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| | 在442条测试集上的表现（80/20分层划分） | Performance on 442 test samples (80/20 stratified split):
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| |
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| | | 指标 Metric | 数值 Value |
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| | |-------------|-----------|
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| | | 准确率 Accuracy | 84.84% |
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| | | F1分数 F1 Score | 84.88% |
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| | | 精确率 Precision | 85.36% |
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| | | 召回率 Recall | 84.84% |
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| |
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| | ### 各类别详细指标 | Per-class Metrics
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| |
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| | | 类别 Class | Precision | Recall | F1 |
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| | |-----------|-----------|--------|----|
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| | | negative | 0.938 | 0.791 | 0.858 |
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| | | neutral | 0.806 | 0.878 | 0.840 |
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| | | positive | 0.846 | 0.857 | 0.851 |
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| | | **weighted avg** | **0.854** | **0.848** | **0.849** |
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| |
|
| | ### 性能迭代历史 | Performance History
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| |
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| | | 版本 Version | 训练数据 Data | F1 Score | Accuracy |
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| | |------|----------|----------|----------|
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| | | v1.0 | 500条 | 61.65% | — |
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| | | v2.0 | 1000条 | 63.65% | 64.50% |
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| | | v3.5 | 1500条 | 67.16% | 68.33% |
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| | | v4.0 | 1700条 | 70.91% | 72.06% |
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| | | v5.0 | 2008条 | 76.88% | 77.36% |
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| | | **v6.0** | **2208条** | **84.88%** | **84.84%** |
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| |
|
| | ## 使用方法 | Usage
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| |
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| | ### 快速开始 | Quick Start
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| |
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| | ```python
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| | from transformers import AutoTokenizer, AutoModelForSequenceClassification
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| | import torch
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| |
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| | # 加载模型和分词器 | Load model and tokenizer
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| | model_name = "LocalOptimum/chinese-crypto-sentiment"
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| | tokenizer = AutoTokenizer.from_pretrained(model_name)
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| | model = AutoModelForSequenceClassification.from_pretrained(model_name)
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| |
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| | # 分析文本 | Analyze text
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| | text = "比特币突破10万美元创历史新高"
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| | inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
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| |
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| | # 预测 | Predict
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| | with torch.no_grad():
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| | outputs = model(**inputs)
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| | predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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| | predicted_class = torch.argmax(predictions, dim=-1).item()
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| |
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| | # 结果映射 | Result mapping
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| | labels = ['positive', 'neutral', 'negative']
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| | sentiment = labels[predicted_class]
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| | confidence = predictions[0][predicted_class].item()
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| |
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| | print(f"情感: {sentiment}")
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| | print(f"置信度: {confidence:.4f}")
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| | ```
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| |
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| | ### 批量处理 | Batch Processing
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| |
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| | ```python
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| | texts = [
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| | "币安获得阿布扎比监管授权",
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| | "以太坊完成Fusaka升级",
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| | "某交易所遭攻击损失100万美元"
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| | ]
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| |
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| | inputs = tokenizer(texts, return_tensors="pt", truncation=True,
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| | max_length=128, padding=True)
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| |
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| | with torch.no_grad():
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| | outputs = model(**inputs)
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| | predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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| | predicted_classes = torch.argmax(predictions, dim=-1)
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| |
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| | labels = ['positive', 'neutral', 'negative']
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| | for text, pred in zip(texts, predicted_classes):
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| | print(f"{text} -> {labels[pred]}")
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| | ```
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| |
|
| | ## 训练参数 | Training Configuration
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| |
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| | - **基础模型 | Base Model**: yiyanghkust/finbert-tone-chinese（经多轮迭代微调）
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| | - **训练轮数 | Epochs**: 5（Early Stopping patience=3，Epoch 5 达到最佳）
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| | - **批次大小 | Batch Size**: 16
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| | - **学习率 | Learning Rate**: 2e-5
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| | - **最大序列长度 | Max Length**: 128
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| | - **训练设备 | Device**: NVIDIA GeForce RTX 5080 Laptop GPU (16GB)
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| | - **混合精度 | Mixed Precision**: FP16
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| | - **最佳模型选择 | Best Model**: metric_for_best_model='f1'
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| |
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| | ## 适用场景 | Use Cases
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| |
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| | - ✅ 加密货币新闻情感分析
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| | - ✅ 社交媒体舆情监控
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| | - ✅ 金融市场情绪指标
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| | - ✅ 实时新闻情感跟踪
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| | - ✅ 投资决策辅助参考
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| |
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| | ## 核心标注原则 | Annotation Principles
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| |
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| | - 加密货币是**风险资产**（类似美股），不是避险资产（类似黄金）
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| | - 战争、地缘冲突、关税 → **negative**（利空风险资产）
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| | - 平台上线新币种/功能 → **neutral**（常规运营，非利好）
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| | - 个人观点/分析师预测 → **neutral**（主观意见）
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| | - 明确利好（ETF通过、大额买入、政策支持）→ **positive**
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| | - 明确利空（清算、暴跌、诈骗、监管打压）→ **negative**
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| |
|
| | ## 局限性 | Limitations
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| |
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| | - ⚠️ 主要针对加密货币领域的金融新闻，其他金融领域可能表现不佳
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| | - ⚠️ 短文本（少于10字）的分析准确率可能下降
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| | - ⚠️ 仅支持简体中文
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| | - ⚠️ 模型不能替代人工判断，仅供参考
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| |
|
| | ## 许可证 | License
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| |
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| | Apache-2.0
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| |
|
| | ## 引用 | Citation
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| |
|
| | 如果使用本模型，请引用：
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| |
|
| | ```bibtex
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| | @misc{watchtower-sentiment-2026,
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| | title={Chinese Financial Sentiment Analysis Model (Crypto Focus)},
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| | author={Onefly},
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| | year={2026},
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| | howpublished={\url{https://huggingface.co/LocalOptimum/chinese-crypto-sentiment}},
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| | note={Fine-tuned from yiyanghkust/finbert-tone-chinese, 2208 samples, F1=84.88\%}
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| | }
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| | ```
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| |
|
| | ## 基础模型 | Base Model
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| |
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| | 本模型基于以下模型微调：
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| | - [yiyanghkust/finbert-tone-chinese](https://huggingface.co/yiyanghkust/finbert-tone-chinese)
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| |
|
| | 感谢原作者的贡献！
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| |
|
| | ## 更新日志 | Changelog
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| |
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| | ### v6.0 (2026-02-28)
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| | - ✅ 扩充训练数据至2208条（+200条Claude人工审阅数据）
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| | - ✅ F1分数大幅提升（76.88% → 84.88%，+8.00%）
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| | - ✅ 大规模纠正地缘政治/战争新闻标注（97条 positive→negative，修复"美以打击伊朗"系统性错误）
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| | - ✅ negative recall 显著提升（67.0% → 79.1%，+12.1pp）
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| | - ✅ 地缘政治专项验证：14条测试全部几乎正确（92.9%），8条战争新闻置信度1.00判为negative
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| |
|
| | ### v5.0 (2026-02-28)
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| | - ✅ 扩充训练数据至2008条（+308条Claude人工审阅数据）
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| | - ✅ F1分数大幅提升（70.91% → 76.88%，+5.97%）
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| | - ✅ 纠正模型系统性错误（positive→neutral 过度预测等）
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| | - ✅ 数据分布优化：negative从362增至431条
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| |
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| | ### v4.0 (2026-02-28)
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| | - ✅ 扩充训练数据至1700条
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| | - ✅ F1分数提升（67.16% → 70.91%，+3.75%）
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| | - ✅ 引入Claude AI逐条审阅标注流程
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| |
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| | ### v3.5 (2026-02-27)
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| | - ✅ 扩充训练数据至1500条
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| | - ✅ F1分数提升（63.65% → 67.16%，+3.51%）
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| | - ✅ 大幅修正战争/地缘冲突→positive的系统性错误
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| |
|
| | ### v2.0 (2025-12-09)
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| | - ✅ 扩充训练数据至1000条
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| | - ✅ 修正标注错误，提升数据质量
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| | - ✅ F1分数提升（61.65% → 63.65%，+2.01%）
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| |
|
| | ### v1.0 (Initial Release)
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| | - 基于500条标注数据的初始版本
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| |
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| | ## 联系方式 | Contact
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| |
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| | 如有问题或建议，欢迎提 issue 或 PR。
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| |
|
| | ---
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| |
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| | **维护者 | Maintainer**: Onefly
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| | **最后更新 | Last Updated**: 2026-02-28
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| | |
