feat: 添加展示HuggingFace支持任务的脚本和测试笔记本

hgface.py

@@ -0,0 +1,8 @@
+from transformers.pipelines import SUPPORTED_TASKS
+
+print(f"{'任务名称':<30} | {'模型类型':<15} | {'Pipeline实现类'}")
+print("-" * 70)
+for task_name, task_info in SUPPORTED_TASKS.items():
+    task_type = task_info['type']
+    impl_class = task_info['impl'].__name__
+    print(f"{task_name:<30} | {task_type:<15} | {impl_class}")

test.ipynb

@@ -0,0 +1,186 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "81db1252",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.5.0\n",
+      "1.9.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import transformers\n",
+    "import huggingface_hub\n",
+    "\n",
+    "print(transformers.__version__)\n",
+    "print(huggingface_hub.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "26843392",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading weights: 100%|██████████| 104/104 [00:00<00:00, 6078.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'label': 'POSITIVE', 'score': 0.9998855590820312}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 从transformers库导入pipeline模块\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "# 修改镜像站\n",
+    "import os\n",
+    "os.environ[\"HF_ENDPOINT\"] = \"https://hf-mirror.com\"\n",
+    "\n",
+    "\n",
+    "# 创建情感分析分类器\n",
+    "classifier = pipeline(\"text-classification\", model=\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\")\n",
+    "\n",
+    "# 对文本进行情感分析\n",
+    "result = classifier(\"I love this product!\")\n",
+    "\n",
+    "# 打印分析结果\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "abdfb345",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "所有支持的任务列表（共 24 个）：\n",
+      "- audio-classification\n",
+      "- automatic-speech-recognition\n",
+      "- text-to-audio\n",
+      "- feature-extraction\n",
+      "- text-classification\n",
+      "- token-classification\n",
+      "- table-question-answering\n",
+      "- document-question-answering\n",
+      "- fill-mask\n",
+      "- text-generation\n",
+      "- zero-shot-classification\n",
+      "- zero-shot-image-classification\n",
+      "- zero-shot-audio-classification\n",
+      "- image-classification\n",
+      "- image-feature-extraction\n",
+      "- image-segmentation\n",
+      "- image-text-to-text\n",
+      "- object-detection\n",
+      "- zero-shot-object-detection\n",
+      "- depth-estimation\n",
+      "- video-classification\n",
+      "- mask-generation\n",
+      "- keypoint-matching\n",
+      "- any-to-any\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers.pipelines import SUPPORTED_TASKS\n",
+    "\n",
+    "# 打印所有任务的名称，统计总个数\n",
+    "total_tasks = len(SUPPORTED_TASKS)\n",
+    "print(f\"所有支持的任务列表（共 {total_tasks} 个）：\")\n",
+    "for task_name in SUPPORTED_TASKS.keys():\n",
+    "    print(f\"- {task_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "57f0cee3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "任务名称                           | 模型类型            | Pipeline实现类\n",
+      "----------------------------------------------------------------------\n",
+      "audio-classification           | audio           | AudioClassificationPipeline\n",
+      "automatic-speech-recognition   | multimodal      | AutomaticSpeechRecognitionPipeline\n",
+      "text-to-audio                  | text            | TextToAudioPipeline\n",
+      "feature-extraction             | text            | FeatureExtractionPipeline\n",
+      "text-classification            | text            | TextClassificationPipeline\n",
+      "token-classification           | text            | TokenClassificationPipeline\n",
+      "table-question-answering       | text            | TableQuestionAnsweringPipeline\n",
+      "document-question-answering    | multimodal      | DocumentQuestionAnsweringPipeline\n",
+      "fill-mask                      | text            | FillMaskPipeline\n",
+      "text-generation                | text            | TextGenerationPipeline\n",
+      "zero-shot-classification       | text            | ZeroShotClassificationPipeline\n",
+      "zero-shot-image-classification | multimodal      | ZeroShotImageClassificationPipeline\n",
+      "zero-shot-audio-classification | multimodal      | ZeroShotAudioClassificationPipeline\n",
+      "image-classification           | image           | ImageClassificationPipeline\n",
+      "image-feature-extraction       | image           | ImageFeatureExtractionPipeline\n",
+      "image-segmentation             | multimodal      | ImageSegmentationPipeline\n",
+      "image-text-to-text             | multimodal      | ImageTextToTextPipeline\n",
+      "object-detection               | multimodal      | ObjectDetectionPipeline\n",
+      "zero-shot-object-detection     | multimodal      | ZeroShotObjectDetectionPipeline\n",
+      "depth-estimation               | image           | DepthEstimationPipeline\n",
+      "video-classification           | video           | VideoClassificationPipeline\n",
+      "mask-generation                | multimodal      | MaskGenerationPipeline\n",
+      "keypoint-matching              | image           | KeypointMatchingPipeline\n",
+      "any-to-any                     | multimodal      | AnyToAnyPipeline\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers.pipelines import SUPPORTED_TASKS\n",
+    "\n",
+    "print(f\"{'任务名称':<30} | {'模型类型':<15} | {'Pipeline实现类'}\")\n",
+    "print(\"-\" * 70)\n",
+    "for task_name, task_info in SUPPORTED_TASKS.items():\n",
+    "    task_type = task_info['type']\n",
+    "    impl_class = task_info['impl'].__name__\n",
+    "    print(f\"{task_name:<30} | {task_type:<15} | {impl_class}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hgface",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}