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          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\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) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m37.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->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,>=0.14.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0\n"
          ]
        }
      ],
      "source": [
        "pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import libraries"
      ],
      "metadata": {
        "id": "k3VRcrfUGpNq"
      }
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      "source": [
        "from transformers import pipeline\n",
        "\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
      ],
      "metadata": {
        "id": "1tSbCn9OGiaW"
      },
      "execution_count": 15,
      "outputs": []
    },
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      "source": [
        "## Sentiment Analysis"
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        "id": "bXHOqQVxaJG-"
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      "source": [
        "classifier = pipeline(\"sentiment-analysis\")"
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            "a69237ccdf1041aa8468422163d5a362",
            "1461c8e45975480b9761d4d209c5687a",
            "1b418300cfd14b5da83be98f97382bd0"
          ]
        },
        "id": "lMzThAsEaMhH",
        "outputId": "657e2ba5-1484-4e81-f59d-59580df74b75"
      },
      "execution_count": 3,
      "outputs": [
        {
          "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": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26d74678f307423898806b599c0beb11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0853eaffb9545e6814bc44de9e46600"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9753448fcf8e4146bf7e9f7481413c6c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a5f0834a17849a19f3c520ae971edae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n",
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = classifier([\"We are very happy to show you the Hugging Face transformers library\", \"We hope you don't hate it\"])"
      ],
      "metadata": {
        "id": "yTgLCh9gfe4F"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YNoL7MAfp9j",
        "outputId": "21caa8be-3bb3-432b-abc1-751c9daf9538"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.999786913394928},\n",
              " {'label': 'POSITIVE', 'score': 0.8319616913795471}]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\""
      ],
      "metadata": {
        "id": "ghHbrhFngQ_Q"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier1 = pipeline(\"sentiment-analysis\", model = model_name)"
      ],
      "metadata": {
        "id": "W0aXQDXIgps1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = classifier1([\"We are very happy to show you the Hugging Face transformers library\", \"We hope you don't hate it\"])"
      ],
      "metadata": {
        "id": "DUFpQZ1kgxEq"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtI7gmg9gywn",
        "outputId": "f9f2bf5c-c0e1-4969-fc42-d701d36f7201"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.999786913394928},\n",
              " {'label': 'POSITIVE', 'score': 0.8319616913795471}]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "mHjsvYpFhLDU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier2 = pipeline(\"sentiment-analysis\", model = model_name)"
      ],
      "metadata": {
        "id": "Pqq3TVxjhbF4"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.tokenize(\"We are very happy to show you the Hugging Face transformers library\")\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "token_ids_direct = tokenizer(\"We are very happy to show you the Hugging Face transformers library\")"
      ],
      "metadata": {
        "id": "LXmfFQcOs0l-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\" tokens: {tokens}\")\n",
        "print(f\" token ids: {token_ids}\")\n",
        "print(f\" token ids (direct): {token_ids_direct}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rCCAQj5tZkw",
        "outputId": "aeeabc86-ba0d-4a55-8d81-92e359d3bcf3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " tokens: ['we', 'are', 'very', 'happy', 'to', 'show', 'you', 'the', 'hugging', 'face', 'transformers', 'library']\n",
            " token ids: [2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 17662, 2227, 19081, 3075]\n",
            " token ids (direct): {'input_ids': [101, 2057, 2024, 2200, 3407, 2000, 2265, 2017, 1996, 17662, 2227, 19081, 3075, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = [\"We are very happy to show you the Hugging Face transformers library\", \"I hate it\"]"
      ],
      "metadata": {
        "id": "WJehV4xI0t4S"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(X_train, padding = True, truncation = True, max_length = 512, return_tensors = \"pt\")"
      ],
      "metadata": {
        "id": "muHmvyYB02Mh"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYh78ZAI1DqK",
        "outputId": "c777988f-0510-4aa1-8fd4-e88024067e2f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,  2057,  2024,  2200,  3407,  2000,  2265,  2017,  1996, 17662,\n",
            "          2227, 19081,  3075,   102],\n",
            "        [  101,  1045,  5223,  2009,   102,     0,     0,     0,     0,     0,\n",
            "             0,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "        [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  outputs = model(**batch, labels = torch.tensor([1, 0]))\n",
        "  print(outputs)\n",
        "  predictions = F.softmax(outputs.logits, dim = 1)\n",
        "  print(predictions)\n",
        "  labels = torch.argmax(predictions, dim = 1)\n",
        "  print(labels)\n",
        "  labels = [model.config.id2label[label_id] for label_id in labels.tolist()]\n",
        "  print(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pgb9tGrUD5yk",
        "outputId": "a8bd08ab-a9d9-4caf-fb7e-a4be4cc0bd75"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=tensor(0.0003), logits=tensor([[-4.1038,  4.3497],\n",
            "        [ 4.3940, -3.5347]]), hidden_states=None, attentions=None)\n",
            "tensor([[2.1309e-04, 9.9979e-01],\n",
            "        [9.9964e-01, 3.6016e-04]])\n",
            "tensor([1, 0])\n",
            "['POSITIVE', 'NEGATIVE']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "save_directory = \"saved\"\n",
        "tokenizer.save_pretrained(save_directory)\n",
        "model.save_pretrained(save_directory)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(save_directory)"
      ],
      "metadata": {
        "id": "xCtSq3FrOPgV"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(save_directory)"
      ],
      "metadata": {
        "id": "x1jY2hxQSXUK"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Another model"
      ],
      "metadata": {
        "id": "GrjO_l9VVoI9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1_name = \"oliverguhr/german-sentiment-bert\""
      ],
      "metadata": {
        "id": "-SgnNrflVrbM"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_german = AutoModelForSequenceClassification.from_pretrained(model1_name)\n",
        "tokenizer_german = AutoTokenizer.from_pretrained(model1_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "a8f8e764425d438fa2c72bad225c2ad2",
            "bc0b3b0fb58f420b91668ac8686e0bf5",
            "edd41576eb094529b3f6d66fcb840714",
            "4eca208881bf40d98ced131e4edf4ef7",
            "323c28dd9bf04a59bbee01d6bf2ffbf1",
            "b375e697d8a540418ad3806bc9368c1d",
            "06d52eedea3644fabaf3b0f69831a1db",
            "546c897d41f3492aa64b119229cd78ed",
            "9fad45c725404a8f94297fc54c2f6b59",
            "bf34b2f7e2214067821205f0b8018b79",
            "c1d431f35d914ab29264b920a0018139",
            "8efc860b60bd41dd9f146e022f9fc730",
            "d1ab1f180e814f51bfe66704ddc62685",
            "72c0efdce6784e7ba48e13d465930d20",
            "f13eb7f6ecf3403bb56e278be00b548d",
            "05d4036506c14a8486e30ca8f9ab0bb4",
            "763420f974e04b0fb451b45982dc8abc",
            "068e7ec325a842c0a162bf146fa2e633",
            "b37a1da4a6e94ff5ad5f4f0acd850628",
            "063e1f010628479097033c2792e7f0fb",
            "7f701ed0ec1644f88fdb208c65c619c9",
            "2aa5ca5d411d4b65959b443758358507",
            "43bc1e958e434cd3aca3d083732e8c03",
            "9432b403abcb49b6bc7450db6975e771",
            "8ac82615443d4e5fb68f3556ae1e217c",
            "556ed0c96a34433d97cadf908c458250",
            "ba1fb8ae4a3748198a6a11a0b208ba95",
            "27471037a4fb4e9890510c1e504cc545",
            "f95c7b3a876c4a90bc472091099a2288",
            "95f6df15468b4c2c9cc1e0bcd0b7bfae",
            "ec6f3d8f52e941c2a66f56b9b7e9028c",
            "443e48cda834447cb2c28d10ce8da499",
            "6ab7ab3fa043479fb9a5536fb44c82ca",
            "4d6fafb45b384d75a29ff9252a4d45cf",
            "1cfc2f0d8b634344af6920bb93edf249",
            "9cdfe520e1984450a7684053c7c2a326",
            "4391c50ab44c4010b818ab37bb757546",
            "2d28d6cb63db4e4baef18d9bbb95920b",
            "44dcb1bb9c7a4d948df4e145784f4d7d",
            "9c4219d87d8e479abf4ba57c26d99d22",
            "f53c83efc9ab47caa62b516bc7d0abee",
            "940228bf0d574b10bb4048e5cd61883a",
            "a45309d21a6c402d89d3204045567b45",
            "543a4911bd884ee2809840f055fd55fe",
            "9126751fa0b6493e94fed25c45fc96ca",
            "fe7470eb4c1d474495d4f29043926395",
            "ef77153343c24494827e4397e5ff0f44",
            "2abbdc2d12ef4b35871a729e20b640db",
            "6836ee3468864c51a72077c545317a25",
            "88e9e909300c404689d92a0f2f78be54",
            "cbe4d31a24334dc7bc47bdb1c52ac549",
            "99cb5eb9c2ca417bbe0b8c3419750c6e",
            "fc9e8efb486143519f24787a636fac6c",
            "0050d488b63b48409fc23eddb486f5f4",
            "00fdaa88b3f149f1869a2b1721f6429f"
          ]
        },
        "id": "i6AN-kcBVzYP",
        "outputId": "1aaab657-c8b8-4b00-ffa1-8f953c592bd2"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8f8e764425d438fa2c72bad225c2ad2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8efc860b60bd41dd9f146e022f9fc730"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/161 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43bc1e958e434cd3aca3d083732e8c03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/255k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d6fafb45b384d75a29ff9252a4d45cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_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": "9126751fa0b6493e94fed25c45fc96ca"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_german = [\"Guten Abend\", \"Wie heißt du?\", \"Wiederholen Sie bitte.\", \"Keine sorge.\"]"
      ],
      "metadata": {
        "id": "lcp5Z9loYhLW"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(X_train_german, padding = True, truncation = True, max_length = 512, return_tensors = \"pt\")\n",
        "print(batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuJ4sRmlZrER",
        "outputId": "580d50cc-391c-430c-a4b5-0a4c6e8e6460"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,  9535,  2368, 14863,  4859,   102,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101, 15536,  2063,  2002,  2072, 19310,  2102,  4241,  1029,   102,\n",
            "             0,     0],\n",
            "        [  101, 15536, 14728, 25032,  9890,  2078,  9033,  2063,  2978,  2618,\n",
            "          1012,   102],\n",
            "        [  101, 26679,  2638,  2061, 20800,  1012,   102,     0,     0,     0,\n",
            "             0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#batch = tokenizer(X_train_german, padding = True, truncation = True, max_length = 512)\n",
        "#batch = torch.tensor(batch[\"input_ids\"])\n",
        "#print(batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5TO9LbG0eGfN",
        "outputId": "5d3091a5-26fa-48b8-a738-d01faa777319"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[  101,  9535,  2368, 14863,  4859,   102,     0,     0,     0,     0,\n",
            "             0,     0],\n",
            "        [  101, 15536,  2063,  2002,  2072, 19310,  2102,  4241,  1029,   102,\n",
            "             0,     0],\n",
            "        [  101, 15536, 14728, 25032,  9890,  2078,  9033,  2063,  2978,  2618,\n",
            "          1012,   102],\n",
            "        [  101, 26679,  2638,  2061, 20800,  1012,   102,     0,     0,     0,\n",
            "             0,     0]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "  outputs_german = model_german(**batch)\n",
        "  print(outputs)\n",
        "  predictions_german = F.softmax(outputs_german.logits, dim = 1)\n",
        "  print(predictions_german)\n",
        "  labels_german = torch.argmax(predictions_german, dim = 1)\n",
        "  print(labels_german)\n",
        "  labels_german = [model_german.config.id2label[label_id] for label_id in labels_german.tolist()]\n",
        "  print(labels_german)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15yNJSdhZGIY",
        "outputId": "5ba856d3-fe1e-4644-dd38-1dc4651de995"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=tensor(0.0003), logits=tensor([[-4.1038,  4.3497],\n",
            "        [ 4.3940, -3.5347]]), hidden_states=None, attentions=None)\n",
            "tensor([[2.0787e-02, 1.2513e-01, 8.5408e-01],\n",
            "        [2.4477e-02, 8.2572e-01, 1.4981e-01],\n",
            "        [5.3153e-05, 1.4655e-04, 9.9980e-01],\n",
            "        [2.0299e-05, 6.2296e-05, 9.9992e-01]])\n",
            "tensor([2, 1, 2, 2])\n",
            "['neutral', 'negative', 'neutral', 'neutral']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "References:\n",
        "\n",
        "https://huggingface.co/docs/transformers/main_classes/pipelines"
      ],
      "metadata": {
        "id": "tz2x4B1bbCO2"
      }
    }
  ]
}