ilk_goruste_ogrenme.ipynb

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      "source": [
        "# This is a Hugging Face Zero shot learning tutorial for Turkish learners.\n",
        "# So, all the credits must go to Hugging face.\n",
        "\n",
        "\n",
        "# Bu yalnızca Hugging face'in muhteşem ZSL tutorialının Türkçe halidir. Lütfen detaylı bilgi için\n",
        "# Hugging Face'e başvurun. \n",
        "\n",
        "# Links:\n",
        "# https://discuss.huggingface.co/t/new-pipeline-for-zero-shot-text-classification/681\n",
        "# https://joeddav.github.io/blog/2020/05/29/ZSL.html\n",
        "# https://huggingface.co/joeddav/xlm-roberta-large-xnli\n",
        "# https://arxiv.org/abs/1911.02116"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "metadata": {
        "id": "t8lRTrHoEPZ1",
        "outputId": "435459c8-ddd8-4a18-fc7c-cb5e4220747d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        }
      },
      "source": [
        "# Hugging face'i yükleyelim\n",
        "!pip install transformers==3.1.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers==3.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/05/c8c55b600308dc04e95100dc8ad8a244dd800fe75dfafcf1d6348c6f6209/transformers-3.1.0-py3-none-any.whl (884kB)\n",
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            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==3.1.0) (4.41.1)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
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            "Collecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/83/8b9fccb9e48eeb575ee19179e2bdde0ee9a1904f97de5f02d19016b8804f/tokenizers-0.8.1rc2-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
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            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=e5aa17a46c81af1c8ab817680e1afc611de4b21f92df9f9fb27b1d3ae365c427\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, sentencepiece, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.1rc2 transformers-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1--g4RuWEfY2"
      },
      "source": [
        "# Pipeline'lar Hugging face modellerinin detaylarına inmeden kullanmanın en kolay yolu.\n",
        "# Veriyi sizin yerinize hazırlıyor ve modeli çalıştırıyor.\n",
        "from transformers import pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYQd87aMEnHE",
        "outputId": "59e7e2fe-946f-4976-f2fa-45b68d3e1262",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262,
          "referenced_widgets": [
            "6f67ba765d3549509885df7f208b0856",
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            "038af0c37e12460c8f303c12286a43bc",
            "f2165ff7df4143e0aed136b39ffa82e0",
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            "7d31874cb1b14e199e2b8de111b2172e",
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            "f225b6f603184d339899f20eecb8fc54",
            "c8319c239b114c4ab1ff363825f55287",
            "aba9fc220d8244178993d2f7ba956c94",
            "e238dbdde10d4a24a8c3aa454eb40709",
            "7cc262c7969e49318544515527cc5301",
            "89567b91d47247fa9436f8d1b6b8357c",
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            "6fce862cfae1415fa44e9e92451e64e5",
            "ab7c4cd54fc640668eabd7f267025c67",
            "7780517b9821457b88c0d6c4cc24b318"
          ]
        }
      },
      "source": [
        "# Model pipeline'ını alalım\n",
        "model_pipeline = pipeline(\"zero-shot-classification\", model='joeddav/xlm-roberta-large-xnli')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f67ba765d3549509885df7f208b0856",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=734.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2165ff7df4143e0aed136b39ffa82e0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=5069051.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d31874cb1b14e199e2b8de111b2172e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=150.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16ce1a9ab6aa4cc2bf57bc29ff721817",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=25.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aba9fc220d8244178993d2f7ba956c94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2243942751.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBE_Thq2J4X-",
        "outputId": "4bab5159-9788-41b0-f2a8-bdddcddf80d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# Duygu analizi örneği\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "konu = [\n",
        "    \"Burada oturuyorum.\",\n",
        "]\n",
        "\n",
        "ihtimaller = [\"olumlu\", \"olumsuz\"]\n",
        "\n",
        "soru_kalibi = \"Bu cümle {}.\"\n",
        "\n",
        "tahminler = model_pipeline(konu, \n",
        "                           ihtimaller, \n",
        "                           hypothesis_template=soru_kalibi, \n",
        "                           multi_class=False)\n",
        "\n",
        "if not isinstance(tahminler, list):\n",
        "  tahminler = [tahminler]\n",
        "\n",
        "for tahmin in tahminler: \n",
        "  print(\"------------\")\n",
        "  idx = np.argmax(tahmin[\"scores\"])\n",
        "  print(\"Bizim sorumuz -> \", tahmin[\"sequence\"])\n",
        "  print(\"Modelin cevabi -> \" + soru_kalibi.format(tahmin[\"labels\"][idx]))\n",
        "  print(list(zip(tahmin['labels'], tahmin['scores'])))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------\n",
            "Bizim sorumuz ->  Burada oturuyorum.\n",
            "Modelin cevabi -> Ben maça gidiyorum ne olumlu ne olumsuz.\n",
            "[('ne olumlu ne olumsuz', 0.6598588228225708), ('olumsuz', 0.19010098278522491), ('olumlu', 0.1500401496887207)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cenpvkiSGIKn",
        "outputId": "545378e5-1321-4459-d172-10dcfbd2d833",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# Çeviri örneği\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "konu = [\n",
        "    \"Bir kamera aldım.\",\n",
        "]\n",
        "\n",
        "ihtimaller = [\n",
        "              \"I am watching robotikçi YouTube channel right now\",\n",
        "              \"I don't like robotikçi YouTube channel at all\",\n",
        "              \"I have no idea about Robotikçi YouTube channel.\",\n",
        "              \"What is robotikçi?\",\n",
        "              \"I bought a webcam.\"\n",
        "              ]\n",
        "\n",
        "soru_kalibi = \"Bu cümlenin çevirisi {}.\"\n",
        "\n",
        "# Kendime not değil koyarak yanılt\n",
        "\n",
        "tahminler = model_pipeline(konu, \n",
        "                           ihtimaller, \n",
        "                           hypothesis_template=soru_kalibi, \n",
        "                           multi_class=False)\n",
        "\n",
        "if not isinstance(tahminler, list):\n",
        "  tahminler = [tahminler]\n",
        "\n",
        "for tahmin in tahminler: \n",
        "  print(\"------------\")\n",
        "  idx = np.argmax(tahmin[\"scores\"])\n",
        "  print(\"Cümle -> \", tahmin[\"sequence\"])\n",
        "  print(\"Çeviri -> \" + soru_kalibi.format(tahmin[\"labels\"][idx]))\n",
        "  print(list(zip(tahmin['labels'], tahmin['scores'])))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------\n",
            "Cümle ->  Bir kamera aldım.\n",
            "Çeviri -> Bu cümlenin çevirisi I bought a webcam..\n",
            "[('I bought a webcam.', 0.9863730072975159), ('What is robotikçi?', 0.00957981962710619), (\"I don't like robotikçi YouTube channel at all\", 0.0017924205167219043), ('I have no idea about Robotikçi YouTube channel.', 0.001536509022116661), ('I am watching robotikçi YouTube channel right now', 0.0007182254921644926)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPmmZMhfUBkH",
        "outputId": "7a7e3b78-4688-4adb-e496-b79a7ecbc4c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------\n",
            "Cümle ->  2 ile 7 toplamı.\n",
            "Çeviri -> Cevap 7.\n",
            "[('7', 0.5438330769538879), ('2', 0.1404133290052414), ('1', 0.08615794032812119), ('8', 0.07243703305721283), ('3', 0.0387323834002018), ('4', 0.03388495370745659), ('9', 0.028184620663523674), ('6', 0.025162532925605774), ('5', 0.020339589565992355), ('0', 0.010854518972337246)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6f1GgnZnIyQ",
        "outputId": "d3ed9247-d105-4f8f-b754-6ba473fbe9f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "# Konu örneği\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "konu = [\n",
        "    \"Türkiye'nin 3 tarafı denizlerle çevrilidir.\",\n",
        "]\n",
        "\n",
        "ihtimaller = [\n",
        "              \"coğrafya\",\n",
        "              \"matematik\",\n",
        "              \"teknoloji\",\n",
        "              \"biyoloji\"\n",
        "              ]\n",
        "\n",
        "soru_kalibi = \"Bu cümlenin konusu {}.\"\n",
        "\n",
        "# Kendime not değil koyarak yanılt\n",
        "\n",
        "tahminler = model_pipeline(konu, \n",
        "                           ihtimaller, \n",
        "                           hypothesis_template=soru_kalibi, \n",
        "                           multi_class=False)\n",
        "\n",
        "if not isinstance(tahminler, list):\n",
        "  tahminler = [tahminler]\n",
        "\n",
        "for tahmin in tahminler: \n",
        "  print(\"------------\")\n",
        "  idx = np.argmax(tahmin[\"scores\"])\n",
        "  print(\"Cümle -> \", tahmin[\"sequence\"])\n",
        "  print(\"Çeviri -> \" + soru_kalibi.format(tahmin[\"labels\"][idx]))\n",
        "  print(list(zip(tahmin['labels'], tahmin['scores'])))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------\n",
            "Cümle ->  Türkiye'nin 3 tarafı denizlerle çevrilidir.\n",
            "Çeviri -> Bu cümlenin konusu coğrafya.\n",
            "[('coğrafya', 0.908661425113678), ('biyoloji', 0.037452325224876404), ('matematik', 0.027655743062496185), ('teknoloji', 0.02623051032423973)]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}