test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 19,
   "id": "26843392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3950ca2f944412ba421836867810a02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading weights:   0%|          | 0/104 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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(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": 13,
   "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": 2,
   "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b5ffa08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                   | distilbert/distilbert-base-uncased-finetuned-sst-2-english\n",
      "pipeline_tag         | text-classification\n",
      "tags                 | ['transformers', 'pytorch', 'tf', 'rust', 'onnx', 'safetensors', 'distilbert', 'text-classification', 'en', 'dataset:sst2', 'dataset:glue', 'arxiv:1910.01108', 'doi:10.57967/hf/0181', 'license:apache-2.0', 'model-index', 'endpoints_compatible', 'deploy:azure', 'region:us']\n",
      "library_name         | transformers\n",
      "model_type           | N/A\n",
      "downloads            | 2966288\n",
      "likes                | 887\n",
      "created_at           | 2022-03-02 23:29:04+00:00\n",
      "last_modified        | 2023-12-19 16:29:37+00:00\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import model_info\n",
    "\n",
    "info = model_info(\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\")\n",
    "\n",
    "fields = [\"id\", \"pipeline_tag\", \"tags\", \"library_name\", \"model_type\",\n",
    "          \"downloads\", \"likes\", \"created_at\", \"last_modified\"]\n",
    "\n",
    "for field in fields:\n",
    "    value = getattr(info, field, \"N/A\")\n",
    "    print(f\"{field:<20} | {value}\")\n"
   ]
  }
 ],
 "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": "3.13.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}