liudev
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roberta-multilabel-28-3-classes

@@ -1,199 +1,177 @@
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 ---
+language:
+  - zh
+tags:
+  - roberta
+  - text-classification
+  - multi-label-classification
+  - emotion-detection
+  - sentiment-analysis
+  - pytorch
+metrics:
+  - f1
+  - precision
+  - recall
 ---
+# RoBERTa Multi-Label Emotion & Tone Classifier (28 Emotions + 3 Tones)
+[![Try it Live](https://img.shields.io/badge/🚀_Try_it_Live-Novel_Emotion_Search_Engine-blue?style=for-the-badge)](https://semo.liudev.com)
+[![Author](https://img.shields.io/badge/Author-liudev-orange?style=for-the-badge)](https://huggingface.co/liudev)
+## 🚀 官方应用展示 (Powered by this model)
+本模型目前已在生产环境中部署。我们基于此模型开发了一款强大的 **“小说情绪起伏搜索引擎”**。
+👉 **[点击这里立即体验在线 Demo：semo.liudev.com](https://semo.liudev.com)**
+在这个应用中，我们展示了该模型的高阶用法：
+1. **情绪轨迹检索 (Emotion Trajectory Search)**：不再是单纯的关键词搜索，你可以拼装一个情绪链条（例如：`喜极而泣 [Joy] -> [Sadness] -> [Relief]` 或 `先抑后扬 [Annoyance] -> [Surprise] -> [Admiration]`），引擎会在海量小说库中找到完美符合该情绪走向的章节。
+2. **序列匹配算法**：底层结合了该模型的 31 维向量输出与 DTW (动态时间规整) 算法，实现长文本情绪子序列的模糊匹配。
+3. **上下文感知高亮**：精准定位并渲染命中情绪的段落。
+4. **AI 链条生成**：支持使用自然语言描述情绪走向，自动转化为模型的查询向量。
+如果你对长文本情感分析、网文数据挖掘感兴趣，强烈建议试用该应用！
+---
+## 模型简介 (Model Description)
+本模型 (`liudev/roberta-multilabel-28-3-classes`) 是一个基于 RoBERTa 架构的多标签文本分类模型。专门用于小说、对话或长文本段落的情感和基调分析。
+模型共支持 **31 个类别**，包括：
+- **28 种细粒度情感**（如 anger, joy, love, surprise 等）
+- **3 种情感基调**（tone_positive, tone_negative, tone_neutral）
+### 特殊的输入格式 (Context-Aware)
+为了更好地理解小说/长文本中的上下文连贯性，**本模型在训练和推理时使用了双句输入（Pair Input）策略**：
+- `text_a`: 历史上下文（如当前段落的前 3 段）
+- `text_b`: 当前需要预测的段落文本
+这种设计使得模型能够结合前文语境，做出更准确的判断。
+## 生产环境推荐阈值 (High-Precision Thresholds)
+在多标签分类中，默认的 `0.5` 阈值往往不是最优的。为了在生产环境中确保**“宁愿漏报，也不误报”（高查准率，Precision >= 80% 为目标）**，我们对每个标签进行了严格的阈值调优（正如我们的官方引擎中所使用的那样）。
+强烈建议在推理时使用以下独立阈值字典：
+```python
+PRODUCTION_THRESHOLDS = {
+    "anger": 0.71, "annoyance": 0.66, "disapproval": 0.65, "disgust": 0.75,
+    "fear": 0.73, "nervousness": 0.74, "embarrassment": 0.82, "disappointment": 0.69,
+    "gratitude": 0.75, "joy": 0.59, "amusement": 0.65, "excitement": 0.61,
+    "optimism": 0.73, "pride": 0.73, "relief": 0.75, "admiration": 0.71,
+    "approval": 0.69, "love": 0.77, "caring": 0.73, "desire": 0.78,
+    "neutral": 0.63, "sadness": 0.68, "grief": 0.80, "remorse": 0.81,
+    "surprise": 0.59, "realization": 0.61, "curiosity": 0.78, "confusion": 0.77,
+    "tone_positive": 0.49, "tone_negative": 0.47, "tone_neutral": 0.62
+}
+```
+## 如何使用 (How to Use)
+以下是一个开箱即用的推理示例，包含了上下文组装和自定义阈值过滤：
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model_id = "liudev/roberta-multilabel-28-3-classes"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+model.eval()
+# 推荐的生产环境阈值 (High Precision)
+THETA_FINAL_TENSOR = torch.tensor([
+    0.71, 0.66, 0.65, 0.75, 0.73, 0.74, 0.82, 0.69, 0.75, 0.59,
+    0.65, 0.61, 0.73, 0.73, 0.75, 0.71, 0.69, 0.77, 0.73, 0.78,
+    0.63, 0.68, 0.80, 0.81, 0.59, 0.61, 0.78, 0.77, 0.49, 0.47, 0.62
+])
+# 标签映射
+id2label = model.config.id2label
+# 构建输入 (Context + Current Paragraph)
+context_paragraphs = [
+    "夜幕低垂，狂风在破败的庙宇外肆虐，吹得半掩的残门嘎吱作响。",
+    "李青死死握紧了手中的长剑，手心满是冷汗，连呼吸都变得极其小心翼翼。"
+]
+current_paragraph = "突然，黑暗中传来一声凄厉的惨叫，紧接着，一双血红色的眼睛在神像背后缓缓睁开！"
+text_a = "\n".join(context_paragraphs) # 前文历史（提供语境）
+text_b = current_paragraph             # 当前需要分析情绪的段落
+inputs = tokenizer(
+    text_a,
+    text_b,
+    padding=True,
+    truncation=True,
+    max_length=256,
+    return_tensors="pt"
+)
+with torch.no_grad():
+    logits = model(**inputs).logits
+    probs = torch.sigmoid(logits).squeeze(0) # 转换为概率
+# 使用自定义阈值进行过滤
+predictions = []
+for idx, prob in enumerate(probs):
+    if prob >= THETA_FINAL_TENSOR[idx]:
+        predictions.append({
+            "label": id2label[idx],
+            "score": round(prob.item(), 4)
+        })
+print(predictions)
+# Expected Output format:[{'label': 'fear', 'score': 0.8855}, {'label': 'nervousness', 'score': 0.7645}, {'label': 'surprise', 'score': 0.8146}, {'label': 'tone_negative', 'score': 0.8112}]
+```
+## 评估指标 (Evaluation Results)
+本模型在验证集上的综合表现如下：
+- **Micro F1:** 0.75
+- **Macro F1:** 0.72
+- **Samples F1:** 0.75
+### 详细分类报告 (基于 Best F1 阈值)
+模型在各个标签上的查准率（Precision）和召回率（Recall）表现：
+| Label | Precision | Recall | F1-Score | Support |
+| :--- | :---: | :---: | :---: | :---: |
+| anger | 0.75 | 0.77 | 0.76 | 887 |
+| annoyance | 0.76 | 0.73 | 0.74 | 1874 |
+| disapproval | 0.70 | 0.74 | 0.72 | 2013 |
+| disgust | 0.75 | 0.67 | 0.71 | 691 |
+| fear | 0.71 | 0.68 | 0.70 | 1165 |
+| nervousness | 0.64 | 0.70 | 0.67 | 1134 |
+| embarrassment| 0.48 | 0.62 | 0.54 | 417 |
+| disappointment| 0.66 | 0.80 | 0.73 | 1805 |
+| gratitude | 0.88 | 0.76 | 0.82 | 683 |
+| joy | 0.85 | 0.88 | 0.87 | 2329 |
+| amusement | 0.72 | 0.68 | 0.70 | 1546 |
+| excitement | 0.81 | 0.81 | 0.81 | 2073 |
+| optimism | 0.68 | 0.68 | 0.68 | 1464 |
+| pride | 0.62 | 0.67 | 0.65 | 1151 |
+| relief | 0.69 | 0.64 | 0.67 | 1023 |
+| admiration | 0.61 | 0.75 | 0.67 | 1529 |
+| approval | 0.67 | 0.74 | 0.70 | 1917 |
+| love | 0.65 | 0.68 | 0.67 | 922 |
+| caring | 0.67 | 0.71 | 0.69 | 1630 |
+| desire | 0.53 | 0.62 | 0.57 | 1132 |
+| neutral | 0.74 | 0.75 | 0.75 | 1810 |
+| sadness | 0.84 | 0.82 | 0.83 | 1585 |
+| grief | 0.64 | 0.77 | 0.70 | 612 |
+| remorse | 0.67 | 0.63 | 0.65 | 323 |
+| surprise | 0.76 | 0.79 | 0.77 | 2355 |
+| realization | 0.65 | 0.79 | 0.71 | 3563 |
+| curiosity | 0.69 | 0.69 | 0.69 | 762 |
+| confusion | 0.64 | 0.72 | 0.68 | 839 |
+| tone_positive | 0.87 | 0.86 | 0.86 | 4306 |
+| tone_negative | 0.87 | 0.90 | 0.89 | 4772 |
+| tone_neutral | 0.75 | 0.77 | 0.76 | 2093 |
+| **Micro Avg** | **0.73** | **0.77** | **0.75** | **50405** |
+*(注意：若采用前文提供的生产环境高精度阈值，Precision 将普遍提升至 0.7~0.85 以上，代价是 Recall 会按预期下降。)*
+以上内容撰写使用LLM生成，以上代码可直接在kaggle上运行，以上数据均为实际运行结果，已测试通过。