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          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
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        },
        {
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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        },
        {
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              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
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            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'NEGATIVE', 'score': 0.8957217335700989}]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "!pip install transformers\n",
        "from transformers import pipeline\n",
        "\n",
        "# 仅指定任务时，使用默认模型（不推荐）\n",
        "pipe = pipeline(\"sentiment-analysis\")\n",
        "pipe(\"今儿上海可真冷啊\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#场景1 情感分类\n",
        "from transformers import pipeline\n",
        "\n",
        "distilled_student_sentiment_classifier = pipeline(\n",
        "    model=\"lxyuan/distilbert-base-multilingual-cased-sentiments-student\",\n",
        "    return_all_scores=True\n",
        ")\n",
        "print(distilled_student_sentiment_classifier (\"I love this movie and i would watch it again and again!\"))\n",
        "print(distilled_student_sentiment_classifier (\"上海天气太冷了\"))\n",
        "\n",
        "#批处理\n",
        "text_list = [\n",
        "    \"Today Shanghai is really cold.\",\n",
        "    \"I think the taste of the garlic mashed pork in this store is average.\",\n",
        "    \"You learn things really quickly. You understand the theory class as soon as it is taught.\"\n",
        "]\n",
        "print(distilled_student_sentiment_classifier(text_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrm1iCA-B5og",
        "outputId": "2114d269-0ad8-4dc2-f29f-1caa45047074"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:105: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[{'label': 'positive', 'score': 0.9731044769287109}, {'label': 'neutral', 'score': 0.01691008359193802}, {'label': 'negative', 'score': 0.00998548325151205}]]\n",
            "[[{'label': 'positive', 'score': 0.060441628098487854}, {'label': 'neutral', 'score': 0.10610411316156387}, {'label': 'negative', 'score': 0.8334542512893677}]]\n",
            "[[{'label': 'positive', 'score': 0.09706168621778488}, {'label': 'neutral', 'score': 0.12048666924238205}, {'label': 'negative', 'score': 0.7824516892433167}], [{'label': 'positive', 'score': 0.37246397137641907}, {'label': 'neutral', 'score': 0.2500365078449249}, {'label': 'negative', 'score': 0.3774994909763336}], [{'label': 'positive', 'score': 0.7639099955558777}, {'label': 'neutral', 'score': 0.1531052589416504}, {'label': 'negative', 'score': 0.0829847976565361}]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#场景2 Token分类\n",
        "from transformers import pipeline\n",
        "nlp=pipeline(\"universal-dependencies\",\"KoichiYasuoka/roberta-base-chinese-ud-goeswith\",trust_remote_code=True,aggregation_strategy=\"simple\")\n",
        "print(nlp(\"我把这本书看完了\"))\n",
        "\n",
        "\n",
        "from transformers import (AutoModelForTokenClassification, AutoTokenizer)\n",
        "from transformers import pipeline\n",
        "\n",
        "hub_model_id = \"9pinus/macbert-base-chinese-medical-collation\"\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(hub_model_id)\n",
        "tokenizer = AutoTokenizer.from_pretrained(hub_model_id)\n",
        "classifier = pipeline('ner', model=model, tokenizer=tokenizer)\n",
        "result = classifier(\"如果病情较重，可适当口服甲肖唑片、环酯红霉素片等药物进行抗感染镇痛。\")\n",
        "\n",
        "for item in result:\n",
        "    if item['entity'] == 1:\n",
        "        print(item)\n"
      ],
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            "645f9a3094eb4dd7966f4d624389221f",
            "373611d438774ef0b1fbc7fe69b16fc9",
            "1ef46919bad74de1a085c1dc0f5b87e1",
            "b31c4f6cd34549d28bbd8588131cdb8e",
            "35794d4e8d9e4f93b95229ef6134d82d",
            "d6741ec22850408dbda8043f2299dbb0",
            "bbbea8cb90d449149fe5aa3c5266b8f3",
            "4f1464f1cd3647f3b195d652b482ae07",
            "6f49b9d8e26c45298b5590017689cb6e",
            "b1a18abb1c344aea942beeef4c43a58d",
            "a2cb213e34c94c33ac5a850530e3150b",
            "802549bd59db493eb2bb3eaf573f38cc",
            "cfa0824444d74728984f7eff4ba32b20",
            "9ee6f59645bc44ffbafadfec7e6abe5d",
            "7c3632d6a7e54b4e9b4d23655f1c352a",
            "002db1b0675441aa890a40314b382e09",
            "32e7a7e9be4548368204d2944c1fc9d4",
            "a3b9d4c0c20146b3affff251eff77cf0",
            "7ede218402ed40a08ee8d55fec867341",
            "638deea79a3f4bb1a047e35381398417",
            "3e5826ffc067462f8a5c864baa2bd2b9",
            "4d6a36b5a4e74bbdb3850542e0e9617f",
            "c0d9702cdad44096b8748fb04dee6e5b",
            "af2fd77f5455430e90ae665fb957085b",
            "6eda79f9c2f7455ca9dc316b6b00a077",
            "423deaef86f14dbdbc9e9ea970106f8e",
            "ae92cbc8406c47569447c47570d92e56"
          ]
        },
        "id": "sN-6YB2EDok0",
        "outputId": "cc2df6bb-c099-41c1-c6b8-39e78bb03ace"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# text = 我把这本书看完了\n",
            "1\t我\t我\tPRON\t_\tPerson=1\t6\tnsubj\t_\tSpaceAfter=No\n",
            "2\t把\t把\tADP\t_\t_\t5\tcase\t_\tSpaceAfter=No\n",
            "3\t这\t这\tDET\t_\t_\t4\tdet\t_\tSpaceAfter=No\n",
            "4\t本\t本\tNOUN\t_\t_\t5\tclf\t_\tSpaceAfter=No\n",
            "5\t书\t书\tNOUN\t_\t_\t6\tobl:patient\t_\tSpaceAfter=No\n",
            "6\t看完\t看完\tVERB\t_\t_\t0\troot\t_\tSpaceAfter=No\n",
            "7\t了\t了\tPART\t_\t_\t6\tdiscourse\t_\tSpaceAfter=No\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "73b2bb28342b413b8b8ee37a7c317f26"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee99f64135d843abb803246d045db4cf"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer_config.json:   0%|          | 0.00/496 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "43f7299934e942b98b2c56c16b081f9e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/110k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1b20a1638c54dec98e015cc581c2123"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/630 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6741ec22850408dbda8043f2299dbb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32e7a7e9be4548368204d2944c1fc9d4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'entity': 1, 'score': 0.9777925, 'index': 14, 'word': '肖', 'start': 13, 'end': 14}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#场景3 问答\n",
        "!pip install torch\n",
        "!pip install transformers\n",
        "!pip install accelerate\n",
        "!pip install bitsandbytes\n",
        "\n",
        "from transformers import BertTokenizerFast, BertForQuestionAnswering, pipeline\n",
        "model_name = \"NchuNLP/Chinese-Question-Answering\"\n",
        "tokenizer = BertTokenizerFast.from_pretrained(model_name)\n",
        "model = BertForQuestionAnswering.from_pretrained(model_name)\n",
        "\n",
        "# a) Get predictions\n",
        "nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)\n",
        "QA_input = {\n",
        "    'question': '中興大學在哪裡？',\n",
        "    'context': '國立中興大學（簡稱興大、NCHU），是位於臺中的一所高等教育機構。中興大學以農業科學、農業經濟學、獸醫、生命科學、轉譯醫學、生醫工程、生物科技、綠色科技等研究領域見長 。近年中興大學與臺中榮民總醫院、彰化師範大學、中國醫藥大學等機構合作，聚焦於癌症醫學、免疫醫學及醫學工程三項領域，將實驗室成果逐步應用到臨床上，未來「衛生福利部南投醫院中興院區」將改為「國立中興大學醫學院附設醫院」。興大也與臺中市政府合作，簽訂合作意向書，共同推動數位文化、智慧城市等面相帶動區域發展。'\n",
        "}\n",
        "res = nlp(QA_input)\n",
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYW3-EhpG2l9",
        "outputId": "ba87f383-551e-477c-9b0b-a762a7cf6845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.25.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.3.post2)\n",
            "{'score': 1.0, 'start': 21, 'end': 23, 'answer': '臺中'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 场景4 Summarization(文本摘要）\n",
        "!pip install sentencepiece\n",
        "!pip install transformers\n",
        "import re\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "WHITESPACE_HANDLER = lambda k: re.sub('\\s+', ' ', re.sub('\\n+', ' ', k.strip()))\n",
        "\n",
        "article_text = \"\"\"Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said.  The policy includes the termination of accounts of anti-vaccine influencers.  Tech giants have been criticised for not doing more to counter false health information on their sites.  In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue.  YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines.  In a blog post, the company said it had seen false claims about Covid jabs \"spill over into misinformation about vaccines in general\". The new policy covers long-approved vaccines, such as those against measles or hepatitis B.  \"We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO,\" the post said, referring to the World Health Organization.\"\"\"\n",
        "\n",
        "model_name = \"csebuetnlp/mT5_multilingual_XLSum\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
        "\n",
        "input_ids = tokenizer(\n",
        "    [WHITESPACE_HANDLER(article_text)],\n",
        "    return_tensors=\"pt\",\n",
        "    padding=\"max_length\",\n",
        "    truncation=True,\n",
        "    max_length=512\n",
        ")[\"input_ids\"]\n",
        "\n",
        "output_ids = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    max_length=84,\n",
        "    no_repeat_ngram_size=2,\n",
        "    num_beams=4\n",
        ")[0]\n",
        "\n",
        "summary = tokenizer.decode(\n",
        "    output_ids,\n",
        "    skip_special_tokens=True,\n",
        "    clean_up_tokenization_spaces=False\n",
        ")\n",
        "\n",
        "print(summary)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752,
          "referenced_widgets": [
            "56128cb1b6cc45e3aa817e0dfe68961d",
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            "7a7d53b7664f48349dcf1f6d5154a517",
            "528380004a434af68cb5baa2e1b187fd",
            "db87ba04870b48c8a22bb6e4beddc160",
            "628c50795a684ef79e87b98c435fa4ca",
            "0bfa8eade7634ef8af4cd4b76c1695f7",
            "2d603e6c1858414288f407f58e3a8a3c",
            "4e426cd255d343838c50d3afecdf1e95",
            "48295569a6d9432ba0282a9dec9d75f4",
            "c58eabe219354716acdca01f01ff8af4",
            "ca6f6140f1354f55acaee1f2db152d8a",
            "9ac4f6683b5740dd87059ed785c68e4e",
            "73592b2b067d44629b416129099965d3",
            "5704ebcb28a2432280e1ec8e22476fd5",
            "324620ea95d148d984dc689174674e21",
            "d7422da78b134b59b3c438ba611f670e",
            "05124e42e5434788af26bc5fe9addc4b",
            "d68865a724d844f5a60e3398af44b4a1",
            "a743f1982414401c940a61787e7d43f1",
            "6bf5ff397158470088a5f30524830bf0",
            "4645e338f0c943b6a3c87bea3d733715",
            "cb2277508af244be84c03a0cc665cfa7",
            "dcc91a4552bf407e9f0e28272cf7cad8",
            "bc61bfddd1bc4462afd6bf542190d70b",
            "b373ee7df8394d81a910c0e61e394409",
            "89076671a4ad43e58b6612c436e53bd0",
            "75cb1e5914d1402b952d73eeea506f14",
            "6e53390747d94adda49028b44cc13dc9",
            "70a39792d2be4391bce7bf3c64880f6e",
            "f03e2928133d442c86b3f8492700c9b6",
            "ca0400911ef34d47a5d295ec7be7e4a5",
            "ca2309078a1041c997fb8cbf4987aa61",
            "3ddf038aa9bf43c288e49c8ae34a3dfd",
            "94b835bf32e645f488593e5b6045d55d",
            "802df6aab4ab4bdbab1e403fcfd30b31",
            "1c48a16d77064493b403d09deb6537b1",
            "4ce1251bfd84443cba0c91773c9fda8f",
            "36d8f80932a44fba90fb96f244539319",
            "3be79053dfeb46229f42d909c962971f",
            "cefe65cc22e9437892ba010b3f39ae59",
            "93d3b8921244418a886ecab383c2ff84",
            "96c256c73c7b4212be3f6e64e8aef39a",
            "b396fbacd8e347228d6f5b9f72de35f2",
            "19b71a1bf7f44fd5acb94eb284f83650",
            "50959a43d9b24e0dab2f87993951abab",
            "f7ed2aac7ea544c784204068a184e2fe",
            "1f6cfe37743445009273f04c62909182",
            "3a2aebf2ca9f491789e0d35d4af0e452",
            "59ad7ecf52fb421e8ac194bb41985d5d",
            "f69938fe33e74692b4ee17beb05f242e",
            "4cbf883f75934552852a98213508b8d7",
            "0ce69d81c0f345d3a71d9ded0f096b3a",
            "e59defd6dea74d24800dddbc8630490c",
            "8f3fc3350f1740b38f4557abf962ec84"
          ]
        },
        "id": "XMsfZuRCutTf",
        "outputId": "f1e19262-b9bd-474e-c216-49c435efe28a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
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              "model_id": "cb2277508af244be84c03a0cc665cfa7"
            }
          },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3ddf038aa9bf43c288e49c8ae34a3dfd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:473: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/2.33G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19b71a1bf7f44fd5acb94eb284f83650"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "YouTube has banned thousands of videos spreading misinformation about Covid vaccines.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 场景6 Audio 音频处理任务\n",
        "\n",
        "\n",
        "#Audio classification(音频分类)\n",
        "!pip install torchaudio\n",
        "!pip install speechbrain\n",
        "import torchaudio\n",
        "from speechbrain.pretrained import EncoderClassifier\n",
        "language_id = EncoderClassifier.from_hparams(source=\"speechbrain/lang-id-voxlingua107-ecapa\", savedir=\"tmp\")\n",
        "# Download Thai language sample from Omniglot and cvert to suitable form\n",
        "signal = language_id.load_audio(\"https://omniglot.com/soundfiles/udhr/udhr_th.mp3\")\n",
        "prediction =  language_id.classify_batch(signal)\n",
        "print(prediction)\n",
        "\n",
        "print(prediction[1].exp())\n",
        "\n",
        "print(prediction[3])\n",
        "\n",
        "emb =  language_id.encode_batch(signal)\n",
        "print(emb.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "68222c597fba42ee8548483aef70649e",
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            "3593cec7f6c942158eb0c16cf22755df",
            "530cdc2013af43869f8b9638f6ffb7b6",
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            "ee0319f457ce4bbc877c593b091ce292",
            "25d654f886704ecf86f0efce0c3f332f",
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            "444a0ff25fc94ba6ab5f79c97c14a325",
            "b825a973265c4f6399a820e38819bb89",
            "8e66ede09bc54eebb8b935e5ddb58dac",
            "86b3674838e94a32bf1d72ce4a6b6981",
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            "90d66ba7bd9e463c84a98df46bd17792",
            "d51b436de30f495cbb1b6380eab99e40",
            "8fc6fcc064464f7d8bc03692f6da07b1",
            "b9dade232afa44769a283f94432a5980",
            "ee7c26fed85a45a28c73c590ffa5cd36",
            "f4e01956be4f4e16934eb3f59007703f",
            "a4b47115bebc4a4ebcc5ce828e000bd3",
            "f6feb8f032b8459680bdfef2d79f8801"
          ]
        },
        "id": "nULFCctIvgxD",
        "outputId": "140b6104-fc4c-40ab-8543-b48456e81346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting speechbrain\n",
            "  Downloading speechbrain-0.5.16-py3-none-any.whl (630 kB)\n",
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            "\u001b[?25hCollecting hyperpyyaml (from speechbrain)\n",
            "  Downloading HyperPyYAML-1.2.2-py3-none-any.whl (16 kB)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from speechbrain) (1.3.2)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->speechbrain) (2.31.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->speechbrain) (6.0.1)\n",
            "Collecting ruamel.yaml>=0.17.28 (from hyperpyyaml->speechbrain)\n",
            "  Downloading ruamel.yaml-0.18.5-py3-none-any.whl (116 kB)\n",
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            "\u001b[?25hCollecting ruamel.yaml.clib>=0.2.7 (from ruamel.yaml>=0.17.28->hyperpyyaml->speechbrain)\n",
            "  Downloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (526 kB)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.9->speechbrain) (1.3.0)\n",
            "Installing collected packages: ruamel.yaml.clib, ruamel.yaml, hyperpyyaml, speechbrain\n",
            "Successfully installed hyperpyyaml-1.2.2 ruamel.yaml-0.18.5 ruamel.yaml.clib-0.2.8 speechbrain-0.5.16\n"
          ]
        },
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            }
          },
          "metadata": {}
        },
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            }
          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25d654f886704ecf86f0efce0c3f332f"
            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e66ede09bc54eebb8b935e5ddb58dac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/speechbrain/dataio/encoder.py:722: UserWarning: CategoricalEncoder.expect_len was never called: assuming category count of 107 to be correct! Sanity check your encoder using `.expect_len`. Ensure that downstream code also uses the correct size. If you are sure this does not apply to you, use `.ignore_len`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(tensor([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01,\n",
            "         -3.2668e+01, -3.6677e+01, -3.3573e+01, -3.2545e+01, -2.4365e+01,\n",
            "         -2.4688e+01, -3.1171e+01, -2.7743e+01, -2.9918e+01, -2.4770e+01,\n",
            "         -3.2250e+01, -2.4727e+01, -2.6087e+01, -2.1870e+01, -3.2821e+01,\n",
            "         -2.2128e+01, -2.2822e+01, -3.0888e+01, -3.3564e+01, -2.9906e+01,\n",
            "         -2.2392e+01, -2.5573e+01, -2.6443e+01, -3.2429e+01, -3.2652e+01,\n",
            "         -3.0030e+01, -2.4607e+01, -2.2967e+01, -2.4396e+01, -2.8578e+01,\n",
            "         -2.5153e+01, -2.8475e+01, -2.6409e+01, -2.5230e+01, -2.7957e+01,\n",
            "         -2.6298e+01, -2.3609e+01, -2.5863e+01, -2.8225e+01, -2.7225e+01,\n",
            "         -3.0486e+01, -2.1185e+01, -2.7938e+01, -3.3155e+01, -1.9076e+01,\n",
            "         -2.9181e+01, -2.2160e+01, -1.8352e+01, -2.5866e+01, -3.3636e+01,\n",
            "         -4.2016e+00, -3.1581e+01, -3.1894e+01, -2.7834e+01, -2.5429e+01,\n",
            "         -3.2235e+01, -3.2280e+01, -2.8786e+01, -2.3366e+01, -2.6047e+01,\n",
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            "         -3.1968e+01, -3.1747e+01, -2.8156e+01, -2.9025e+01, -2.7756e+01,\n",
            "         -2.8052e+01, -2.9341e+01, -2.8806e+01, -2.1636e+01, -2.3992e+01,\n",
            "         -2.3794e+01, -3.3743e+01, -2.8332e+01, -2.7465e+01, -1.5086e-02,\n",
            "         -2.9094e+01, -2.1444e+01, -2.9780e+01, -3.6046e+01, -3.7401e+01,\n",
            "         -3.0888e+01, -3.3172e+01, -1.8931e+01, -2.2679e+01, -3.0225e+01,\n",
            "         -2.4995e+01, -2.1028e+01]]), tensor([-0.0151]), tensor([94]), ['th: Thai'])\n",
            "tensor([0.9850])\n",
            "['th: Thai']\n",
            "torch.Size([1, 1, 256])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 场景7 Automatic speech recognition（自动语音识别）\n",
        "!pip install  datasets\n",
        "!pip install torch\n",
        "\n",
        "from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC\n",
        "from datasets import load_dataset\n",
        "import torch\n",
        "\n",
        "# load model and tokenizer\n",
        "processor = Wav2Vec2Processor.from_pretrained(\"facebook/wav2vec2-base-960h\")\n",
        "model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-base-960h\")\n",
        "\n",
        "# load dummy dataset and read soundfiles\n",
        "ds = load_dataset(\"patrickvonplaten/librispeech_asr_dummy\", \"clean\", split=\"validation\")\n",
        "\n",
        "# tokenize\n",
        "input_values = processor(ds[0][\"audio\"][\"array\"], return_tensors=\"pt\", padding=\"longest\").input_values  # Batch size 1\n",
        "\n",
        "# retrieve logits\n",
        "logits = model(input_values).logits\n",
        "\n",
        "# take argmax and decode\n",
        "predicted_ids = torch.argmax(logits, dim=-1)\n",
        "transcription = processor.batch_decode(predicted_ids)\n"
      ],
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        "id": "D4lVP6weyh-D",
        "outputId": "0e412724-566e-4394-de7e-a6ac245af607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at facebook/wav2vec2-base-960h were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.encoder.pos_conv_embed.conv.weight_g', 'wav2vec2.encoder.pos_conv_embed.conv.weight_v']\n",
            "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'wav2vec2.masked_spec_embed', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:1429: FutureWarning: The repository for patrickvonplaten/librispeech_asr_dummy contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/patrickvonplaten/librispeech_asr_dummy\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
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          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/9.08M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "It is strongly recommended to pass the ``sampling_rate`` argument to this function. Failing to do so can result in silent errors that might be hard to debug.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#场景9 Image Classificaiton(图像分类)\n",
        "!pip install PIL\n",
        "from transformers import ViTImageProcessor, ViTForImageClassification\n",
        "from PIL import Image\n",
        "import requests\n",
        "\n",
        "url = 'http://images.cocodataset.org/val2017/000000039769.jpg'\n",
        "image = Image.open(requests.get(url, stream=True).raw)\n",
        "\n",
        "processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')\n",
        "model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\n",
        "\n",
        "inputs = processor(images=image, return_tensors=\"pt\")\n",
        "outputs = model(**inputs)\n",
        "logits = outputs.logits\n",
        "# model predicts one of the 1000 ImageNet classes\n",
        "predicted_class_idx = logits.argmax(-1).item()\n",
        "print(\"Predicted class:\", model.config.id2label[predicted_class_idx])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
            "93ecf1a1b2084ebba5b3dd11a370cb57",
            "9995248e09c04adf82e60da592b1b312",
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            "57c95fd8ebf84809a6e0ba10ac3ef86e",
            "11dd619e624f40afbd16e6db38d3efb5"
          ]
        },
        "id": "ik2yAWsyzTtw",
        "outputId": "daeaa748-1cd8-49ba-c2f1-0d0adb9808c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not find a version that satisfies the requirement PIL (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for PIL\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93ecf1a1b2084ebba5b3dd11a370cb57"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/69.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce32595dc795412db740b0294615927c"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted class: Egyptian cat\n"
          ]
        }
      ]
    }
  ]
}