docs: add introduction about text-classification using Qwen2. (#56)

* docs: add document about `BertSelfAttention`

* docs: add doc about text-classification

* docs: add code example for text-classification using Qwen2

* docs: add logo for qwen2 in chaper 6

.obsidian/workspace.json

@@ -13,13 +13,13 @@
             "state": {
               "type": "markdown",
               "state": {
-                "file": "docs/chapter8/bert/modeling/modeling.md",
+                "file": "docs/chapter6/financial_report/financial_report.md",
                 "mode": "source",
                 "backlinks": false,
                 "source": false
               },
               "icon": "lucide-file",
-              "title": "modeling"
+              "title": "financial_report"
             }
           }
         ]
@@ -68,10 +68,10 @@
             "state": {
               "type": "outline",
               "state": {
-                "file": "docs/chapter8/bert/modeling/modeling.md"
+                "file": "docs/chapter6/financial_report/financial_report.md"
               },
               "icon": "lucide-list",
-              "title": "modeling 的大纲"
+              "title": "financial_report 的大纲"
             }
           }
         ]
@@ -87,10 +87,10 @@
   },
   "active": "a00f9c294cc735a6",
   "lastOpenFiles": [
+    "docs/chapter8/bert/modeling/modeling.md",
     "docs/chapter8/repositories/repositories.md",
     "docs/chapter8/repositories_index.md",
     "docs/chapter8/bert/tokenization/tokenization.md",
-    "docs/chapter8/bert/modeling/modeling.md",
     "docs/chapter8/bert/configuration/configuration.md",
     "docs/chapter1/dataset_tour/datasets.md",
     "docs/chapter8/bert/tokenization/tokenizer.md",

.vscode/settings.json

@@ -1,5 +1,6 @@
 {
     "markdownlint.config": {
         "MD010": false,
+        "MD033": false
     }
 }

docker-compose/Dockerfile

@@ -10,12 +10,16 @@ RUN apt-get update && apt-get install -y \
     wget \
     curl \
     git \
+    unzip \
+    inetutils-ping \
+    tmux \
     && apt-get clean
 
 COPY --from=miniconda-stage /opt/conda /opt/conda
 ENV PATH="/opt/conda/bin:${PATH}"
 WORKDIR /root
 
+RUN echo "export HF_ENDPOINT=https://hf-mirror.com" >> /root/.bashrc
 COPY condarc /root/.condarc
 COPY Dockerfile /root/dockerfile/Dockerfile
 COPY pip.conf /root/.pip/pip.conf

docs/appendix/env_config/env.md

@@ -15,13 +15,13 @@ title: 环境配置
 
 注意：选择下载的 Miniconda3 版本需要和电脑处理器的架构吻合。为了方便，在此下方直接提供各大操作系统推荐的下载链接。
 
-| 系统 | 下载地址 |
-| :---: | --- |
-| Windows | <https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe> |
-| macOS（Intel） | <https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh> |
-| macOS（M/ARM） | <https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh> |
-| Linux（x64） | <https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh> |
-| Linux（ARM） | <https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh> |
+|      系统      | 下载地址                                                                   |
+| :------------: | -------------------------------------------------------------------------- |
+|    Windows     | <https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe> |
+| macOS（Intel） | <https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh>   |
+| macOS（M/ARM） | <https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh>    |
+|  Linux（x64）  | <https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh>    |
+|  Linux（ARM）  | <https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh>   |
 
 ### 安装 Miniconda
 
@@ -222,7 +222,7 @@ auto_activate_base: false
 ![conda_activate_env](./imgs/conda_activate_env.png){ width="600" }
 
 - 安装包：`conda install package_name`或者`pip install package_name`
-- `pip`在安装包时临时更换镜像源：`pip install package_name -i https://pypi.tuna.tsinghua.edu.cn/simple`
+    - `pip`在安装包时临时更换镜像源：`pip install package_name -i https://pypi.tuna.tsinghua.edu.cn/simple`
 - 卸载包：`conda remove package_name`或者`pip uninstall package_name`
 - 显示所有安装的包：`conda list`
 - 删除指定虚拟环境：`conda remove -n env_name --all`
@@ -235,7 +235,7 @@ auto_activate_base: false
 ## 安装函数库
 
 ???+ warning
-    💯 当你想在虚拟环境安装包的时候，确认你正处在正确的虚拟环境中！！
+    :100:当你想在虚拟环境安装包的时候，确认你正处在正确的虚拟环境中！！
 
 ```bash title='pip/conda'
 pip install numpy pandas matplotlib transformers datasets peft evaluate diffusers gradio torch jupyterlab

docs/chapter1/dataset_tour/datasets.md

@@ -93,7 +93,7 @@ data = load_dataset("hfl/cmrc2018")
 
 通过返回结果可以看出 `data` 的数据类型为 `DatasetDict`，它是 `Datasets` 库中重要的数据类型。
 
-!!! Note "train_test_split"
+!!! note "train_test_split"
 
 	并非所有数据集都包含训练集、验证集和测试集。有些数据集可能只有一个或两个子集。
 	对于数据集 `hfl/cmrc2018` 存在训练集、验证集和测试集。但是对于 `LooksJuicy/ruozhiba` 却只存在训练集。
@@ -180,7 +180,7 @@ Dataset({
 
 ```
 
-!!! Note "配置"
+!!! note "配置"
 
 	### 配置 (Configurations)
 

docs/chapter6/code_index.md

@@ -5,9 +5,12 @@ title: 索引
 
 主页
 
-- 多标签分类:[面对有害言论, 是时候让AI重拳出击了](./mlcoftc/multi-label-classification-of-toxic-comments.md)
-- 抽取式阅读理解:[CMRC2018](./cmrc/cmrc.md)
-- 文本摘要:[LCSTS短文本新闻摘要](./text-summary/text-summary.md)
-- 集装箱编号位置检测:[DETR目标检测](./container-detr/container-detr.md)
-- 文本翻译:[中英文本翻译](./translation/translation.md)
-- 简单去噪:[ddpm-unet](./ddpm-unet-mnist/ddpm-unet-mnist.md)
+| 模块名称           | 链接                                                                                            |
+| ------------------ | ----------------------------------------------------------------------------------------------- |
+| 多标签分类         | [面对有害言论，是时候让AI重拳出击了](./mlcoftc/multi-label-classification-of-toxic-comments.md) |
+| 抽取式阅读理解     | [CMRC2018](./cmrc/cmrc.md)                                                                      |
+| 文本摘要           | [LCSTS短文本新闻摘要](./text-summary/text-summary.md)                                           |
+| 集装箱编号位置检测 | [目标检测](./container-detr/container-detr.md)                                                  |
+| 文本翻译           | [中英文本翻译](./translation/translation.md)                                                    |
+| 扩散去噪           | [ddpm-unet简单去噪](./ddpm-unet-mnist/ddpm-unet-mnist.md)                                       |
+| 文本分类           | [基金年报问答意图识别](./financial_report/financial_report.md)                                  |

docs/chapter6/ddpm-unet-mnist/ddpm-unet-mnist.md

@@ -25,7 +25,7 @@ import pandas as pd
 from matplotlib import pyplot as plt
 ```
 
-### 数据集的加载
+### 加载数据集
 
 ```python
 class MnistDataset: