Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+emotion[[:space:]]summary/tokenizer.json filter=lfs diff=lfs merge=lfs -text

emotion summary/.gitattributes

@@ -0,0 +1,5 @@
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text
+*.md filter=lfs diff=lfs merge=lfs -text
+

emotion summary/README.md

@@ -0,0 +1,92 @@
+# Emotion Summary Model (mT5-small)
+
+## 模型描述
+
+这是一个基于 mT5-small 微调的情感总结模型，用于从心理咨询案例中提取和总结关键信息。
+
+## 模型信息
+
+- **基础模型**: google/mt5-small
+- **任务**: 长文本情感信息提取与总结
+- **训练数据**: 8000条心理咨询对话
+- **验证数据**: 800条
+- **输出字段**: 
+  - predicted_cause: 病因分析
+  - predicted_symptoms: 症状描述
+  - predicted_treatment_process: 治疗过程
+  - predicted_illness_Characteristics: 疾病特征
+  - predicted_treatment_effect: 治疗效果
+
+## 使用方法
+
+```python
+from transformers import MT5ForConditionalGeneration, MT5Tokenizer
+
+# 加载模型和tokenizer
+model = MT5ForConditionalGeneration.from_pretrained("./emotion_summary")
+tokenizer = MT5Tokenizer.from_pretrained("./emotion_summary")
+
+# 准备输入
+case_text = "..."  # 输入的案例文本
+input_text = f"Summarize case: {case_text}"
+
+# 编码
+input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+
+# 生成
+output_ids = model.generate(
+    input_ids,
+    max_length=256,
+    num_beams=4,
+    early_stopping=True
+)
+
+# 解码
+output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(output_text)
+```
+
+## 训练参数
+
+- **Epochs**: 1
+- **Batch Size**: 4
+- **Learning Rate**: 1e-4
+- **Max Input Length**: 128 tokens
+- **Max Output Length**: 128 tokens
+- **Gradient Accumulation Steps**: 2
+
+## 性能
+
+- 训练损失: ~2.5
+- 验证损失: ~2.8
+- 推理速度: ~2-3秒/样本
+
+## 注意事项
+
+1. 输入文本需要包含完整的案例描述、咨询过程和反思内容
+2. 模型针对心理咨询领域文本优化
+3. 建议输入长度控制在512 tokens以内以获得最佳效果
+
+## 文件清单
+
+- `config.json`: 模型配置
+- `generation_config.json`: 生成配置
+- `model.safetensors`: 模型权重
+- `tokenizer配置文件`: 用于文本编码/解码
+- `spiece.model`: SentencePiece词表
+
+## 许可证
+
+MIT License
+
+## 引用
+
+如果使用本模型，请引用：
+
+```
+@model{emotion_summary_mt5,
+  title={Emotion Summary Model based on mT5-small},
+  year={2025},
+  author={Your Team}
+}
+```

emotion summary/UPLOAD_GUIDE.md

@@ -0,0 +1,259 @@
+# Emotion Summary 模型上传到 Hugging Face 指南
+
+## 📦 文件清单
+
+此文件夹包含上传到 Hugging Face 所需的所有文件：
+
+### 必需文件（9个）：
+
+1. **config.json** (1.93 KB)
+   - 模型配置文件
+   - 定义模型架构和参数
+
+2. **generation_config.json** (188 Bytes)
+   - 生成配置文件
+   - 定义文本生成参数（beam search等）
+
+3. **model.safetensors** (1,145.1 MB = 1.12 GB)
+   - 模型权重文件
+   - 使用SafeTensors格式存储
+   - ⚠️ 需要Git LFS上传
+
+4. **special_tokens_map.json** (280 Bytes)
+   - 特殊token映射
+   - 定义[PAD], [CLS], [SEP]等特殊标记
+
+5. **spiece.model** (4.11 MB)
+   - SentencePiece词表模型
+   - mT5使用的分词器
+
+6. **tokenizer.json** (15.59 MB)
+   - Tokenizer配置
+   - 包含词表和分词规则
+
+7. **tokenizer_config.json** (20 KB)
+   - Tokenizer配置参数
+   - 定义分词器的行为
+
+8. **README.md**
+   - 模型卡片（Model Card）
+   - 描述模型用途、性能、使用方法
+
+9. **.gitattributes**
+   - Git LFS配置
+   - 标记大文件使用LFS上传
+
+10. **inference_example.py**
+    - 推理示例代码
+    - 展示如何使用模型
+
+---
+
+## 📊 文件大小统计
+
+| 文件名 | 大小 | 类型 |
+|--------|------|------|
+| model.safetensors | 1,145.1 MB | 模型权重 |
+| tokenizer.json | 15.59 MB | Tokenizer |
+| spiece.model | 4.11 MB | 词表 |
+| tokenizer_config.json | 20 KB | 配置 |
+| config.json | ~2 KB | 配置 |
+| README.md | ~3 KB | 文档 |
+| 其他JSON文件 | < 1 KB | 配置 |
+| **总计** | **~1.16 GB** | - |
+
+---
+
+## 🚀 上传到 Hugging Face 的步骤
+
+### 方法1：使用 Web 界面上传（推荐）
+
+1. **登录 Hugging Face**
+   - 访问：https://huggingface.co
+   - 登录您的账号
+
+2. **创建新模型仓库**
+   - 点击右上角头像 → "New Model"
+   - 填写信息：
+     - Model name: `emotion-summary-mt5`
+     - License: MIT
+     - 勾选 "Public" 或 "Private"
+
+3. **上传文件**
+   - 进入模型页面
+   - 点击 "Files and versions" 标签
+   - 点击 "Add file" → "Upload files"
+   - 拖拽或选择本文件夹中的所有文件
+   - ⚠️ **重要**：确保上传 `.gitattributes` 文件在上传其他文件之前！
+
+4. **等待处理**
+   - 大文件会自动使用 Git LFS
+   - model.safetensors (1.1GB) 需要较长时间
+
+### 方法2：使用命令行 + Git
+
+```bash
+# 1. 安装 Git LFS（如果还没安装）
+git lfs install
+
+# 2. 克隆你的模型仓库
+git clone https://huggingface.co/<your-username>/emotion-summary-mt5
+cd emotion-summary-mt5
+
+# 3. 复制所有文件到仓库
+cp ../emotion_summary_huggingface/* .
+
+# 4. 添加所有文件
+git add .
+
+# 5. 提交
+git commit -m "Upload emotion summary model"
+
+# 6. 推送（需要 Hugging Face token）
+git push
+```
+
+### 方法3：使用 huggingface_hub 库
+
+```python
+from huggingface_hub import HfApi, create_repo
+
+# 创建API对象
+api = HfApi()
+
+# 创建仓库
+repo_id = "your-username/emotion-summary-mt5"
+create_repo(repo_id, repo_type="model", exist_ok=True)
+
+# 上传整个文件夹
+api.upload_folder(
+    folder_path="./emotion_summary_huggingface",
+    repo_id=repo_id,
+    repo_type="model"
+)
+
+print(f"✓ Model uploaded to https://huggingface.co/{repo_id}")
+```
+
+---
+
+## 🔑 获取 Hugging Face Token
+
+如果使用命令行或Python上传，需要访问token：
+
+1. 访问：https://huggingface.co/settings/tokens
+2. 点击 "New token"
+3. 给token命名（如 "upload-models"）
+4. 选择权限：**Write** 
+5. 复制token并保存
+
+**设置token：**
+```bash
+# 方式1：使用 huggingface-cli
+huggingface-cli login
+
+# 方式2：设置环境变量
+export HF_TOKEN="your_token_here"
+```
+
+---
+
+## ⚠️ 注意事项
+
+1. **Git LFS 必需**
+   - model.safetensors 文件很大 (1.1GB)
+   - 必须使用 Git LFS 上传
+   - `.gitattributes` 文件已配置好
+
+2. **上传顺序**
+   - 先上传 `.gitattributes`
+   - 再上传其他所有文件
+
+3. **网络要求**
+   - 上传 1.1GB 文件需要稳定网络
+   - 建议使用有线连接
+   - 预计时间：10-30分钟（取决于网速）
+
+4. **仓库设置**
+   - 建议设置为 Public 以便团队使用
+   - 添加适当的 tags（如：mt5, emotion-analysis, chinese）
+
+---
+
+## 📝 模型信息
+
+- **模型名称**: Emotion Summary Model
+- **基础模型**: google/mt5-small
+- **任务类型**: Text2Text Generation
+- **语言**: Chinese (中文)
+- **用途**: 从心理咨询案例中提取情感信息
+- **训练数据**: 8,000条心理咨询对话
+- **模型大小**: ~1.2 GB
+
+---
+
+## ✅ 验证上传成功
+
+上传完成后，访问您的模型页面：
+```
+https://huggingface.co/<your-username>/emotion-summary-mt5
+```
+
+检查：
+- ✓ 所有10个文件都存��
+- ✓ model.safetensors 显示为 LFS 文件
+- ✓ README.md 正确显示
+- ✓ 可以在 "Files and versions" 中看到所有文件
+
+---
+
+## 🎯 测试模型
+
+上传成功后，可以使用以下代码测试：
+
+```python
+from transformers import MT5ForConditionalGeneration, MT5Tokenizer
+
+# 从 Hugging Face 加载
+model_name = "your-username/emotion-summary-mt5"
+model = MT5ForConditionalGeneration.from_pretrained(model_name)
+tokenizer = MT5Tokenizer.from_pretrained(model_name)
+
+# 测试推理
+input_text = "Extract cause from: ..."
+inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+outputs = model.generate(**inputs, max_length=256, num_beams=4)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+---
+
+## 📞 问题排查
+
+### 问题1：上传失败 "file too large"
+**解决方案**：确保安装并启用了 Git LFS
+```bash
+git lfs install
+git lfs track "*.safetensors"
+```
+
+### 问题2：权限错误
+**解决方案**：检查 Hugging Face token 是否有 Write 权限
+
+### 问题3：上传中断
+**解决方案**：Git 会自动续传，只需重新运行 `git push`
+
+---
+
+## 📚 相关文档
+
+- Hugging Face 上传指南：https://huggingface.co/docs/hub/models-uploading
+- Git LFS 文档：https://git-lfs.github.com/
+- mT5 模型：https://huggingface.co/docs/transformers/model_doc/mt5
+
+---
+
+创建日期：2025-10-31
+模型版本：v1.0
+

emotion summary/config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": 8,
+  "num_heads": 6,
+  "num_layers": 8,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 250112
+}

emotion summary/generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": [
+    1
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "4.57.1"
+}

emotion summary/inference_example.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Emotion Summary Model - 推理示例
+"""
+
+from transformers import MT5ForConditionalGeneration, MT5Tokenizer
+import json
+import torch
+
+def load_model(model_path="./emotion_summary"):
+    """加载模型和tokenizer"""
+    print(f"Loading model from {model_path}...")
+    model = MT5ForConditionalGeneration.from_pretrained(model_path)
+    tokenizer = MT5Tokenizer.from_pretrained(model_path)
+    
+    # 如果有GPU就使用GPU
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = model.to(device)
+    model.eval()
+    
+    print(f"Model loaded on {device}")
+    return model, tokenizer, device
+
+def summarize_case(model, tokenizer, device, case_data, field="cause"):
+    """
+    对案例进行总结
+    
+    Args:
+        model: 模型
+        tokenizer: tokenizer
+        device: 设备
+        case_data: 案例数据（字典）
+        field: 要生成的字段 (cause/symptoms/treatment_process/illness_characteristics/treatment_effect)
+    
+    Returns:
+        生成的总结文本
+    """
+    
+    # 构建输入
+    case_desc = " ".join(case_data.get("case_description", []))
+    consultation = " ".join(case_data.get("consultation_process", []))
+    reflection = case_data.get("experience_and_reflection", "")
+    
+    full_text = f"Case: {case_desc}\nConsultation: {consultation}\nReflection: {reflection}"
+    
+    # 根据字段构建不同的prompt
+    prompts = {
+        "cause": f"Extract cause from: {full_text}",
+        "symptoms": f"Extract symptoms from: {full_text}",
+        "treatment_process": f"Extract treatment process from: {full_text}",
+        "illness_characteristics": f"Extract illness characteristics from: {full_text}",
+        "treatment_effect": f"Extract treatment effect from: {full_text}"
+    }
+    
+    input_text = prompts.get(field, full_text)
+    
+    # 编码
+    input_ids = tokenizer.encode(
+        input_text,
+        return_tensors="pt",
+        max_length=512,
+        truncation=True
+    ).to(device)
+    
+    # 生成
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            max_length=256,
+            num_beams=4,
+            early_stopping=True,
+            no_repeat_ngram_size=3
+        )
+    
+    # 解码
+    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return output_text
+
+def process_test_file(input_file, output_file, model_path="./emotion_summary"):
+    """处理测试文件"""
+    
+    # 加载模型
+    model, tokenizer, device = load_model(model_path)
+    
+    # 读取测试数据
+    test_data = []
+    with open(input_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            if line.strip():
+                test_data.append(json.loads(line))
+    
+    print(f"\nProcessing {len(test_data)} samples...")
+    
+    results = []
+    for i, sample in enumerate(test_data, 1):
+        print(f"  [{i}/{len(test_data)}] Processing ID: {sample['id']}...")
+        
+        result = {
+            "id": sample["id"],
+            "predicted_cause": summarize_case(model, tokenizer, device, sample, "cause"),
+            "predicted_symptoms": summarize_case(model, tokenizer, device, sample, "symptoms"),
+            "predicted_treatment_process": summarize_case(model, tokenizer, device, sample, "treatment_process"),
+            "predicted_illness_Characteristics": summarize_case(model, tokenizer, device, sample, "illness_characteristics"),
+            "predicted_treatment_effect": summarize_case(model, tokenizer, device, sample, "treatment_effect")
+        }
+        
+        results.append(result)
+    
+    # 保存结果
+    with open(output_file, 'w', encoding='utf-8') as f:
+        for result in results:
+            json.dump(result, f, ensure_ascii=False)
+            f.write('\n')
+    
+    print(f"\n✓ Results saved to {output_file}")
+
+if __name__ == "__main__":
+    # 示例：处理单个案例
+    sample_case = {
+        "id": 1,
+        "case_description": ["A 34-year-old male with health anxiety..."],
+        "consultation_process": ["The consultation began with..."],
+        "experience_and_reflection": "This case demonstrates..."
+    }
+    
+    model, tokenizer, device = load_model()
+    
+    print("\nGenerating summaries...")
+    cause = summarize_case(model, tokenizer, device, sample_case, "cause")
+    print(f"\nCause: {cause}")
+    
+    # 如果要处理整个测试文件，取消下面的注释：
+    # process_test_file("data/test/Emotion_Summary.jsonl", "results/predictions.jsonl")

emotion summary/model.safetensors

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emotion summary/special_tokens_map.json

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+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  },
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

emotion summary/spiece.model

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+size 4309802

emotion summary/tokenizer.json

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+size 16350028

emotion summary/tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
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+}

emotion summary/文件说明.md

@@ -0,0 +1,160 @@
+# Emotion Summary 任务 - Hugging Face 提交文件说明
+
+## 📁 文件夹位置
+`C:\Users\xsz20\project(self)\LongEmotion\emotion_summary_huggingface\`
+
+---
+
+## 📋 文件清单（共11个文件）
+
+### 1. 模型文件
+- **model.safetensors** (1,145.1 MB)
+  - mT5-small 微调后的模型权重
+  - SafeTensors 格式（更安全、更快）
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/model.safetensors`
+
+### 2. 配置文件
+- **config.json** (~2 KB)
+  - 模型架构配置
+  - 定义层数、隐藏层大小等参数
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/config.json`
+
+- **generation_config.json** (~200 Bytes)
+  - 文本生成配置
+  - 定义 beam_size、max_length 等生成参数
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/generation_config.json`
+
+### 3. Tokenizer 文件
+- **spiece.model** (4.11 MB)
+  - SentencePiece 分词模型
+  - mT5 使用的多语言分词器
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/spiece.model`
+
+- **tokenizer.json** (15.59 MB)
+  - 完整的 tokenizer 配置
+  - 包含词表、特殊token等
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/tokenizer.json`
+
+- **tokenizer_config.json** (20 KB)
+  - Tokenizer 参数配置
+  - 定义特殊token、最大长度等
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/tokenizer_config.json`
+
+- **special_tokens_map.json** (280 Bytes)
+  - 特殊 token 映射
+  - 定义 [PAD], [UNK], [CLS], [SEP], [MASK] 等
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/special_tokens_map.json`
+
+### 4. 文档文件
+- **README.md** (~3 KB)
+  - 模型卡片（Model Card）
+  - 包含模型描述、使用方法、性能指标
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/README.md`
+
+- **UPLOAD_GUIDE.md** (~6 KB)
+  - 上传指南（新创建）
+  - 详细的 Hugging Face 上传步骤
+  - 包含三种上传方法
+
+- **文件说明.md** (本文件)
+  - 文件清单和说明
+  - 帮助理解每个文件的用途
+
+### 5. 代码文件
+- **inference_example.py** (~4 KB)
+  - 推理示例代码
+  - 展示如何加载和使用模型
+  - 包含单样本和批量处理示例
+  - 来源：`Emotion Summary (ES)/model/emotion_summary/inference_example.py`
+
+### 6. Git LFS 配置
+- **.gitattributes**
+  - Git LFS 配置文件
+  - 标记大文件使用 LFS 上传
+  - 新创建，用于 Hugging Face 上传
+
+---
+
+## 📊 文件大小统计
+
+| 类型 | 文件数 | 总大小 |
+|------|--------|--------|
+| 模型权重 | 1 | 1,145.1 MB |
+| Tokenizer | 3 | 19.7 MB |
+| 配置文件 | 4 | ~25 KB |
+| 文档 | 3 | ~10 KB |
+| 代码 | 1 | ~4 KB |
+| **总计** | **11** | **~1.16 GB** |
+
+---
+
+## 🎯 对照 Classification 任务结构
+
+根据您提供的 classification 任务的 Hugging Face 截图，两个任务的文件对应关系：
+
+| Classification 文件 | Emotion Summary 对应文件 | 说明 |
+|---------------------|-------------------------|------|
+| config.json | ✅ config.json | 模型配置 |
+| model.safetensors | ✅ model.safetensors | 模型权重 |
+| special_tokens_map.json | ✅ special_tokens_map.json | 特殊token |
+| tokenizer.json | ✅ tokenizer.json | Tokenizer配置 |
+| tokenizer_config.json | ✅ tokenizer_config.json | Tokenizer参数 |
+| vocab.json | ✅ spiece.model | 词表（不同格式） |
+| merges.txt | ❌ 无需此文件 | mT5使用SentencePiece，不需要merges |
+| .gitattributes | ✅ .gitattributes | Git LFS配置 |
+
+**额外文件（ES任务特有）：**
+- ✅ generation_config.json - 生成任务配置
+- ✅ README.md - 模型文档
+- ✅ inference_example.py - 使用示例
+- ✅ UPLOAD_GUIDE.md - 上传指南
+
+---
+
+## ✅ 完整性检查
+
+所有必需文件均已包含：
+- ✅ 模型权重文件
+- ✅ 模型配置文件
+- ✅ Tokenizer 全套文件
+- ✅ 文档和示例代码
+- ✅ Git LFS 配置
+
+**可以安全上传到 Hugging Face！**
+
+---
+
+## 📍 原始文件位置
+
+所有文件来源于：
+```
+Emotion Summary (ES)/model/emotion_summary/
+```
+
+该文件夹包含训练完成的 mT5-small 模型，已在8000条心理咨询案例上微调。
+
+---
+
+## 🚀 下一步操作
+
+1. ✅ **文件已准备完毕** - 所有文件已复制到 `emotion_summary_huggingface` 文件夹
+2. ⏭️ **上传到 Hugging Face** - 参考 `UPLOAD_GUIDE.md` 中的详细步骤
+3. ⏭️ **测试模型** - 上传后使用 `inference_example.py` 测试
+
+---
+
+## 📞 技术支持
+
+如有问题，请查看：
+- `UPLOAD_GUIDE.md` - 详细上传指南
+- `README.md` - 模型使用说明
+- `inference_example.py` - 代码示例
+
+---
+
+创建时间：2025-10-31 20:10
+任务：Emotion Summary (ES)
+基础模型：google/mt5-small
+总文件数：11个
+总大小：~1.16 GB
+