Chapter10_Creating_Text_Embedding_Model.ipynb

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            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Zohreh6384NKH/LLM_Project/blob/main/Chapter10_Creating_Text_Embedding_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Creating an Embedding Model**\n"
      ],
      "metadata": {
        "id": "VIaAuFEX8a2V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Data**"
      ],
      "metadata": {
        "id": "Ki7Fd6dR8lKD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# %%capture\n",
        "!pip install -q accelerate>=0.27.2 peft>=0.9.0 bitsandbytes>=0.43.0 transformers>=4.38.2 trl>=0.7.11 sentencepiece>=0.1.99\n",
        "!pip install -q sentence-transformers>=3.0.0 mteb>=1.1.2 datasets>=2.18.0\n",
        "!pip install --upgrade torch datasets sentence-transformers\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5bSApGQ8g_M",
        "outputId": "8cb239fa-311d-4e4a-f191-156650c43dd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.6.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.1+cu121)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.21.0)\n",
            "Collecting datasets\n",
            "  Using cached datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.3.1-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
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            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
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            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.2)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.26.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.46.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.5.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (11.0.0)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.3)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2024.9.11)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.21,>=0.20 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.20.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Using cached datasets-3.1.0-py3-none-any.whl (480 kB)\n",
            "Downloading sentence_transformers-3.3.1-py3-none-any.whl (268 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers, datasets\n",
            "  Attempting uninstall: sentence-transformers\n",
            "    Found existing installation: sentence-transformers 3.2.1\n",
            "    Uninstalling sentence-transformers-3.2.1:\n",
            "      Successfully uninstalled sentence-transformers-3.2.1\n",
            "  Attempting uninstall: datasets\n",
            "    Found existing installation: datasets 2.21.0\n",
            "    Uninstalling datasets-2.21.0:\n",
            "      Successfully uninstalled datasets-2.21.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "mteb 1.20.0 requires datasets<3.0.0,>=2.19.0, but you have datasets 3.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.1.0 sentence-transformers-3.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load MNLI dataset from GLUE\n",
        "# 0 = entailment, 1 = neutral, 2 = contradiction\n",
        "train_dataset = load_dataset(\"glue\", \"mnli\", split=\"train\").select(range(50_000))\n",
        "train_dataset = train_dataset.remove_columns(\"idx\")"
      ],
      "metadata": {
        "id": "5vEDUUjo_Sm8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
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        },
        "outputId": "80e99d7f-2a9e-4dc1-8536-80075e1d2e46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: 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",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "train-00000-of-00001.parquet:   0%|          | 0.00/52.2M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
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          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "2933bbb8ae3e4a03abbfd57959190419"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test_mismatched-00000-of-00001.parquet:   0%|          | 0.00/1.26M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70498e24ce1f4a47930eb694d609a4d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/392702 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a39d5009b7f0468f83a0a72569d1a7eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation_matched split:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fcec7c04062e4965beddf2c87a68f451"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation_mismatched split:   0%|          | 0/9832 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "871f8fe03c4b4d64876378f7faf20236"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_matched split:   0%|          | 0/9796 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c3c696297104391abaaab6e421aeb3c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test_mismatched split:   0%|          | 0/9847 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9751cc5514d74a30b4bd1ed194a3d1e5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uG0V1bQghQ90",
        "outputId": "6b4f31e8-0a16-426a-9d34-383459cccda3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'premise': 'One of our number will carry out your instructions minutely.',\n",
              " 'hypothesis': 'A member of my team will execute your orders with immense precision.',\n",
              " 'label': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Model**"
      ],
      "metadata": {
        "id": "_oKxI2nHO-WL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "embedding_model = SentenceTransformer('bert-base-uncased')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244,
          "referenced_widgets": [
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          ]
        },
        "id": "6ZZlfN6YO_-d",
        "outputId": "36146aa5-5c47-4162-92c9-faa9e8ea30cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78a9211c9025452092b182d5719bda3e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name bert-base-uncased. Creating a new one with mean pooling.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0671caa832746eba2dcb9f3caa56982"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7d908366bee49a8ba30a26608366bf6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7421bb432e9477ba229b0a8d1169c5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66d6c098b3b64517abfd67e73114012b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a2121d3fd664130b4aecfb4a16b0b6d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Loss Funnction**"
      ],
      "metadata": {
        "id": "fuZz8mj8QC15"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import losses\n",
        "#define the loss function we also explicitly in softmax loss we mention the number of labels\n",
        "train_loss = losses.SoftmaxLoss(\n",
        "    model=embedding_model,\n",
        "    sentence_embedding_dimension=embedding_model.get_sentence_embedding_dimension(),\n",
        "    num_labels=3\n",
        ")"
      ],
      "metadata": {
        "id": "rf72L_KFQDBZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Evaluation**"
      ],
      "metadata": {
        "id": "ngT9-SoiY1kd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "\n",
        "# Create an embedding similarity evaluator for stsb\n",
        "val_sts = load_dataset('glue', 'stsb', split='validation')\n",
        "evaluator = EmbeddingSimilarityEvaluator(\n",
        "    sentences1=val_sts[\"sentence1\"],\n",
        "    sentences2=val_sts[\"sentence2\"],\n",
        "    scores=[score/5 for score in val_sts[\"label\"]],\n",
        "    main_similarity=\"cosine\",\n",
        ")"
      ],
      "metadata": {
        "id": "2RYJL4CzY1sH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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          ]
        },
        "outputId": "8f98b364-19e4-46f5-e1a0-5955655728d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/502k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "624c31834be14b00833c99fd8313947e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "validation-00000-of-00001.parquet:   0%|          | 0.00/151k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3522b7d8f2c14e6e840012ecb5a58ef2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test-00000-of-00001.parquet:   0%|          | 0.00/114k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3e76f157dd042c1ac9fdb8cf6380573"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/5749 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de2841b6a90e49e08caabac3673fc5ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e6eb28104c1449192a26b543fde308d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1379 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "784e81c881df48b99fd2412306689d96"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\""
      ],
      "metadata": {
        "id": "z_IJusl705WL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Training**"
      ],
      "metadata": {
        "id": "4nJhD5MCZIii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.training_args import SentenceTransformerTrainingArguments\n",
        "\n",
        "# Define the training arguments\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"base_embedding_model\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    warmup_steps=100,\n",
        "    fp16=True,\n",
        "    eval_steps=100,\n",
        "    logging_steps=100,\n",
        ")"
      ],
      "metadata": {
        "id": "ntVVEr-iZLVG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "VPNxJRqaknzs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.trainer import SentenceTransformerTrainer\n",
        "\n",
        "# Train embedding model\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=embedding_model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    loss=train_loss,\n",
        "    evaluator=evaluator\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "dnyu_gaRkn9i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635,
          "referenced_widgets": [
            "c1842709d04f4270b90a374738502e25",
            "d43a330a684f410bb839b159ae47c193",
            "3a377f64c5cf4ee0a5ef0ab3edf61597",
            "2614ccee43ea4a97a5455cc049d31800",
            "1733865a153c4ae4865f102ad43082f3",
            "217144eb08bc4a3daced9181b2c846bb",
            "93c2f20156b14fa1876889d6ba7de4b9",
            "02e2a3ed5fcb41d283fa6aaf4b109a24",
            "361607483a2c41c48da4a8ce74635c7e",
            "d6dbd21496c247c09b6498b1dd0e9379",
            "63039bb276814ae4bd68ea7ddb9916c7"
          ]
        },
        "outputId": "116af988-d144-4900-fc16-736d7c7d5fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "WARNING:sentence_transformers.data_collator:Column 'hypothesis' is at index 1, whereas a column with this name is usually expected at index 0. Note that the column order can be important for some losses, e.g. MultipleNegativesRankingLoss will always consider the first column as the anchor and the second as the positive, regardless of the dataset column names. Consider renaming the columns to match the expected order, e.g.:\n",
            "dataset = dataset.select_columns(['hypothesis', 'entailment', 'contradiction'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1563' max='1563' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1563/1563 06:28, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.078500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.947300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.882500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.844900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.828600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.826100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.818500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.790400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.775900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.768900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.759800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.730500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.747900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.717300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.758000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1842709d04f4270b90a374738502e25"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1563, training_loss=0.8153652720777789, metrics={'train_runtime': 391.6865, 'train_samples_per_second': 127.653, 'train_steps_per_second': 3.99, 'total_flos': 0.0, 'train_loss': 0.8153652720777789, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**In Depth Evaluation**"
      ],
      "metadata": {
        "id": "fjXfKrf_mGpR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Massive Text Embedding Benchmark**"
      ],
      "metadata": {
        "id": "-K19y1usnID7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mteb import MTEB\n",
        "\n",
        "# Choose evaluation task\n",
        "evaluation = MTEB(tasks=[\"Banking77Classification\"])\n",
        "\n",
        "# Calculate results\n",
        "results = evaluation.run(embedding_model)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181,
          "referenced_widgets": [
            "bdaf9e744d7545e78bb60e1b94cc8021",
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            "6cc7bb2afa694be4953c31ec825e1721",
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            "b0bb0aac198341a4877660824c05175e",
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            "e81754560724447fb0d617c2b3821b93",
            "0e5daa8007c04389a356e7d89f84a4eb",
            "b9bdaa20b0fd44af836824723f84b203",
            "51b4961cbdd44967b755b70e0ba20e65",
            "5bb80128fe5d423792cacd5d13b918ec",
            "1eb97d83ecc343829a67d84fae7bcf18"
          ]
        },
        "id": "vAtYUGTAmMqh",
        "outputId": "f3b9b210-3ecb-4c05-e687-8965d4110d70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:mteb.evaluation.MTEB:Passing task names as strings is deprecated and will be removed in the next release. Please use `tasks = mteb.get_tasks(tasks=[...])` method to get tasks instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[38;5;235m───────────────────────────────────────────────── \u001b[0m\u001b[1mSelected tasks \u001b[0m\u001b[38;5;235m ─────────────────────────────────────────────────\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #262626; text-decoration-color: #262626\">───────────────────────────────────────────────── </span><span style=\"font-weight: bold\">Selected tasks </span><span style=\"color: #262626; text-decoration-color: #262626\"> ─────────────────────────────────────────────────</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mClassification\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Classification</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    - Banking77Classification, \u001b[3;38;5;241ms2s\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    - Banking77Classification, <span style=\"color: #626262; text-decoration-color: #626262; font-style: italic\">s2s</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train.jsonl:   0%|          | 0.00/1.25M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bdaf9e744d7545e78bb60e1b94cc8021"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test.jsonl:   0%|          | 0.00/365k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f0619d2d5b04145b796fe8271006ae6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[TaskResult(task_name=Banking77Classification, scores=...)]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "8si1-jHGv-eZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xLu88Kphv-mI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Empty and delete trainer/model\n",
        "trainer.accelerator.clear()\n",
        "del trainer, embedding_model\n",
        "\n",
        "# # Garbage collection and empty cache\n",
        "import gc\n",
        "import torch\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "SfaRv_phnVAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "LjnF9wSuwL9-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Loss Functions**"
      ],
      "metadata": {
        "id": "woABvd6pwVBn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Cosine Similarity Loss**"
      ],
      "metadata": {
        "id": "ItSr2pY8we5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, load_dataset\n",
        "# Load MNLI dataset from GLUE\n",
        "# 0 = entailment, 1 = neutral, 2 = contradiction\n",
        "\n",
        "train_dataset = load_dataset(\"glue\", \"mnli\", split=\"train\").select(range(50_000))\n",
        "train_dataset = train_dataset.remove_columns(\"idx\")\n",
        "\n",
        "# (neutral/contradiction)=0 and (entailment)=1\n",
        "mapping = {2: 0, 1: 0, 0:1}\n",
        "train_dataset = Dataset.from_dict({\n",
        "    \"sentence1\": train_dataset[\"premise\"],\n",
        "    \"sentence2\": train_dataset[\"hypothesis\"],\n",
        "    \"label\": [float(mapping[label]) for label in train_dataset[\"label\"]]\n",
        "})\n"
      ],
      "metadata": {
        "id": "vjHXbJ9wwXxq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "\n",
        "# Create an embedding similarity evaluator for stsb\n",
        "val_sts = load_dataset('glue', 'stsb', split='validation')\n",
        "evaluator = EmbeddingSimilarityEvaluator(\n",
        "    sentences1=val_sts[\"sentence1\"],\n",
        "    sentences2=val_sts[\"sentence2\"],\n",
        "    scores=[score/5 for score in val_sts[\"label\"]],\n",
        "    main_similarity=\"cosine\"\n",
        ")"
      ],
      "metadata": {
        "id": "AvJfOprny1Bl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\""
      ],
      "metadata": {
        "id": "1FORvmrX0tAP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import losses, SentenceTransformer\n",
        "from sentence_transformers.trainer import SentenceTransformerTrainer\n",
        "from sentence_transformers.training_args import SentenceTransformerTrainingArguments\n",
        "\n",
        "# Define model\n",
        "embedding_model = SentenceTransformer('bert-base-uncased')\n",
        "\n",
        "# Loss function\n",
        "train_loss = losses.CosineSimilarityLoss(model=embedding_model)\n",
        "\n",
        "# Define the training arguments\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"cosineloss_embedding_model\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    warmup_steps=100,\n",
        "    fp16=True,\n",
        "    eval_steps=100,\n",
        "    logging_steps=100,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=embedding_model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    loss=train_loss,\n",
        "    evaluator=evaluator\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581,
          "referenced_widgets": [
            "45486bd4ad7d4d4cb7d15a626e2d5838",
            "2e983e32e5244bf09eb2a9ebfad6c7a3",
            "7baadcf864c8445fa64b3dae73864796",
            "77711056d20f4845be17934bab53c116",
            "cc88a9139ee741c493326c76d753cc71",
            "722b1189a50a4b5b8dba8f97c90b0d09",
            "8a74ab2c172e4eb18e5751802f7a0a18",
            "b39070c8a9e44f91a4375a9f82592e87",
            "48a9759cccfa49fc8bc9de978ccdb1ba",
            "c24d36a7fc47441aaa87bc05eb507270",
            "f224ff78728841848e892aea486d5193"
          ]
        },
        "id": "qBa1Ph5o0cLz",
        "outputId": "8e276dd7-7305-4ee5-fe47-ae90aa4356d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name bert-base-uncased. Creating a new one with mean pooling.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1563' max='1563' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1563/1563 05:53, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.232500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.170600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.172300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.159700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.151900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.158300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.151000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.156100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.147600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.146700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.146900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.147500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.146100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.140100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.140200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45486bd4ad7d4d4cb7d15a626e2d5838"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1563, training_loss=0.15731500892858816, metrics={'train_runtime': 353.7615, 'train_samples_per_second': 141.338, 'train_steps_per_second': 4.418, 'total_flos': 0.0, 'train_loss': 0.15731500892858816, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our trained model\n",
        "evaluator(embedding_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BFzgu-7az7xf",
        "outputId": "825578f1-fcf9-4fb7-84e7-83a6cb813338"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pearson_cosine': 0.7285063873604648, 'spearman_cosine': 0.7304035538813521}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Multiple Negative Ranking Loss**"
      ],
      "metadata": {
        "id": "d1gnaJNN0dkK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "from tqdm import tqdm\n",
        "from datasets import Dataset, load_dataset\n",
        "\n",
        "# # Load MNLI dataset from GLUE\n",
        "mnli = load_dataset(\"glue\", \"mnli\", split=\"train\").select(range(50_000))\n",
        "mnli = mnli.remove_columns(\"idx\")\n",
        "mnli = mnli.filter(lambda x: True if x['label'] == 0 else False)\n",
        "\n",
        "# Prepare data and add a soft negative\n",
        "train_dataset = {\"anchor\": [], \"positive\": [], \"negative\": []}\n",
        "soft_negatives = mnli[\"hypothesis\"]\n",
        "random.shuffle(soft_negatives)\n",
        "for row, soft_negative in tqdm(zip(mnli, soft_negatives)):\n",
        "\n",
        "\n",
        "  train_dataset[\"anchor\"].append(row[\"premise\"])\n",
        "  train_dataset[\"positive\"].append(row[\"hypothesis\"])\n",
        "  train_dataset[\"negative\"].append(soft_negative)\n",
        "train_dataset = Dataset.from_dict(train_dataset)\n",
        "len(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vd2aojzH0d1T",
        "outputId": "36e43843-6bf2-4ad9-8f3a-964182376250"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "16875it [00:01, 14761.62it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16875"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "\n",
        "# Create an embedding similarity evaluator for stsb\n",
        "val_sts = load_dataset('glue', 'stsb', split='validation')\n",
        "evaluator = EmbeddingSimilarityEvaluator(\n",
        "    sentences1=val_sts[\"sentence1\"],\n",
        "    sentences2=val_sts[\"sentence2\"],\n",
        "    scores=[score/5 for score in val_sts[\"label\"]],\n",
        "    main_similarity=\"cosine\"\n",
        ")"
      ],
      "metadata": {
        "id": "wFWOEr7138BI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import losses, SentenceTransformer\n",
        "from sentence_transformers.trainer import SentenceTransformerTrainer\n",
        "from sentence_transformers.training_args import SentenceTransformerTrainingArguments\n",
        "\n",
        "# Define model\n",
        "embedding_model = SentenceTransformer('bert-base-uncased')\n",
        "\n",
        "# Loss function\n",
        "train_loss = losses.MultipleNegativesRankingLoss(model=embedding_model)\n",
        "\n",
        "# Define the training arguments\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"mnrloss_embedding_model\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    warmup_steps=100,\n",
        "    fp16=True,\n",
        "    eval_steps=100,\n",
        "    logging_steps=100,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=embedding_model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    loss=train_loss,\n",
        "    evaluator=evaluator\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267,
          "referenced_widgets": [
            "afd7ec39074d428dbd90487d4ced5be5",
            "6c035b1d6f384968b518b8ab45145ebc",
            "110113d6196b43d0adc4a5460e44bde9",
            "44d7ca0b83a645a2a4a744f22bad936d",
            "2538792697974741b998be4db345c91e",
            "439f2014a82d489a97619f290ed59df0",
            "b0d0c054e9f64868bf2333f92a294291",
            "6fad67b0659245f3aa57fbda5e9e4c98",
            "d92fef238c454ba99f3064910b5816f9",
            "5d826f1a120e43eca0cc727a4ddda415",
            "dd9939701dc14ef1a9f3b7a5b651c100"
          ]
        },
        "id": "upW2oW1V3_41",
        "outputId": "778c3d72-f765-4023-c78e-334040c17153"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name bert-base-uncased. Creating a new one with mean pooling.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='528' max='528' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [528/528 03:07, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.329200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.106700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.076500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.064700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.068600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afd7ec39074d428dbd90487d4ced5be5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=528, training_loss=0.12530518458648163, metrics={'train_runtime': 187.5763, 'train_samples_per_second': 89.963, 'train_steps_per_second': 2.815, 'total_flos': 0.0, 'train_loss': 0.12530518458648163, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our trained model\n",
        "evaluator(embedding_model)"
      ],
      "metadata": {
        "id": "K7F5GT-r4EeJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1dc5471-230b-4a83-ef69-a5bab92ea0de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pearson_cosine': 0.808734981867691, 'spearman_cosine': 0.8105461928185358}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Fine Tuning**"
      ],
      "metadata": {
        "id": "SoDtKjET745m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n"
      ],
      "metadata": {
        "id": "D_s5LAhQQrcU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Supervised**"
      ],
      "metadata": {
        "id": "AvHXMHh9S9Oj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "from datasets import Dataset, load_dataset\n",
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "\n",
        "#load mnli dataset from glue\n",
        "#entailment=0 neutral=1, contradiction=2\n",
        "\n",
        "train_dataset = load_dataset(\"glue\", \"mnli\", split=\"train\").select(range(50_000))\n",
        "train_dataset = train_dataset.remove_columns(\"idx\")\n",
        "\n",
        "# Create an embedding similarity evaluator for stsb\n",
        "val_sts = load_dataset('glue', 'stsb', split='validation')\n",
        "print(val_sts[0])\n",
        "evaluator = EmbeddingSimilarityEvaluator(\n",
        "    sentences1=val_sts[\"sentence1\"],\n",
        "    sentences2=val_sts[\"sentence2\"],\n",
        "    scores=[score/5 for score in val_sts[\"label\"]],\n",
        "    main_similarity=\"cosine\"\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIyikRLC77R8",
        "outputId": "23925166-6457-4d83-c6f8-c97b4996e01c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'sentence1': 'A man with a hard hat is dancing.', 'sentence2': 'A man wearing a hard hat is dancing.', 'label': 5.0, 'idx': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\""
      ],
      "metadata": {
        "id": "Mh4dD8LRTxpU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import losses, SentenceTransformer\n",
        "from sentence_transformers.trainer import SentenceTransformerTrainer\n",
        "from sentence_transformers.training_args import SentenceTransformerTrainingArguments\n",
        "\n",
        "# Define model\n",
        "embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n",
        "\n",
        "# Loss function\n",
        "train_loss = losses.MultipleNegativesRankingLoss(model=embedding_model)\n",
        "\n",
        "# Define the training arguments\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"finetuned_embedding_model\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    warmup_steps=100,\n",
        "    fp16=True,\n",
        "    eval_steps=100,\n",
        "    logging_steps=100,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=embedding_model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    loss=train_loss,\n",
        "    evaluator=evaluator\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 970,
          "referenced_widgets": [
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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        {
          "output_type": "display_data",
          "data": {
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            ],
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.data_collator:Column 'hypothesis' is at index 1, whereas a column with this name is usually expected at index 0. Note that the column order can be important for some losses, e.g. MultipleNegativesRankingLoss will always consider the first column as the anchor and the second as the positive, regardless of the dataset column names. Consider renaming the columns to match the expected order, e.g.:\n",
            "dataset = dataset.select_columns(['hypothesis', 'entailment', 'contradiction'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1563' max='1563' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1563/1563 01:52, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.157300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.110500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.119900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.118800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.108300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.101100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.119600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.098600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.104100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.105200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.093700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.105600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.101400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.104800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.104700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b1f8bceab014b1da9f12e61b85698c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1563, training_loss=0.10938238807770967, metrics={'train_runtime': 112.725, 'train_samples_per_second': 443.558, 'train_steps_per_second': 13.866, 'total_flos': 0.0, 'train_loss': 0.10938238807770967, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our trained model\n",
        "evaluator(embedding_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cS-jkoPuTaaN",
        "outputId": "0dec5335-d526-468e-c2dc-f4590d07795e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pearson_cosine': 0.849506947439533, 'spearman_cosine': 0.8488999043402223}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the pre-trained model\n",
        "original_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n",
        "evaluator(original_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnKqqxW8EWuL",
        "outputId": "831c911b-ad89-4b73-c707-2e6a7d78e372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pearson_cosine': 0.8696194518832261, 'spearman_cosine': 0.8671631197908374}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**VRAM clean up**"
      ],
      "metadata": {
        "id": "ijzYsNw8T5y4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "VqkrXU68T87y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Augmented SBERT**"
      ],
      "metadata": {
        "id": "Cl2Qg71c75-B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Finetuning a Cross encoder**"
      ],
      "metadata": {
        "id": "0CaXWK1g8Czf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from datasets import load_dataset, Dataset\n",
        "from sentence_transformers import InputExample\n",
        "from sentence_transformers.datasets import NoDuplicatesDataLoader\n",
        "\n",
        "# Prepare a small set of 10000 documents for the cross-encoder\n",
        "dataset = load_dataset(\"glue\", \"mnli\", split=\"train\").select(range(10_000))\n",
        "mapping = {2: 0, 1: 0, 0:1}\n",
        "\n",
        "# Data Loader\n",
        "gold_examples = [\n",
        "    InputExample(texts=[row[\"premise\"], row[\"hypothesis\"]], label=mapping[row[\"label\"]])\n",
        "    for row in tqdm(dataset)\n",
        "]\n",
        "gold_dataloader = NoDuplicatesDataLoader(gold_examples, batch_size=32)\n",
        "\n",
        "# Pandas DataFrame for easier data handling\n",
        "gold = pd.DataFrame(\n",
        "    {\n",
        "    'sentence1': dataset['premise'],\n",
        "    'sentence2': dataset['hypothesis'],\n",
        "    'label': [mapping[label] for label in dataset['label']]\n",
        "    }\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKSL40PW76JP",
        "outputId": "c4d21fe2-8b3a-4938-befe-88e9348e2d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10000/10000 [00:00<00:00, 25188.67it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.cross_encoder import CrossEncoder\n",
        "\n",
        "# Train a cross-encoder on the gold dataset\n",
        "cross_encoder = CrossEncoder('bert-base-uncased', num_labels=2)\n",
        "cross_encoder.fit(\n",
        "    train_dataloader=gold_dataloader,\n",
        "    epochs=1,\n",
        "    show_progress_bar=True,\n",
        "    warmup_steps=100,\n",
        "    use_amp=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "8dafdc4d698d4720af715f31265e6151",
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            "f789848b2cec4f72abfa86806c89a304",
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            "d315875b46c844adb50253ca167208bb"
          ]
        },
        "id": "uwrYn5OvG1x6",
        "outputId": "6c62a897-4e4a-45ed-c840-1637487857b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/312 [00:00<?, ?it/s]"
            ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create new sentence pairs\n",
        "#prepare the silver dataset by predicting labels with the cross encoder\n",
        "# Prepare the silver dataset by predicting labels with the cross-encoder\n",
        "silver = load_dataset(\"glue\", \"mnli\", split=\"train\").select(range(10_000, 50_000))\n",
        "pairs = list(zip(silver['premise'], silver['hypothesis']))"
      ],
      "metadata": {
        "id": "QW7kqOpnZ4X4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Label the sentence pairs using our fine-tuned cross-encoder\n",
        "output = cross_encoder.predict(pairs, apply_softmax=True, show_progress_bar=True)\n",
        "silver = pd.DataFrame(\n",
        "    {\n",
        "        \"sentence1\": silver[\"premise\"],\n",
        "        \"sentence2\": silver[\"hypothesis\"],\n",
        "        \"label\": np.argmax(output, axis=1)\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "id": "U1jjFy9SckGZ",
        "outputId": "e934c2e2-9fd0-4684-d071-073664f013bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1250 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine gold + silver\n",
        "data = pd.concat([gold, silver], ignore_index=True, axis=0)\n",
        "data = data.drop_duplicates(subset=['sentence1', 'sentence2'], keep=\"first\")\n",
        "train_dataset = Dataset.from_pandas(data, preserve_index=False)"
      ],
      "metadata": {
        "id": "jfOYai6MckTg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "\n",
        "# Create an embedding similarity evaluator for stsb\n",
        "val_sts = load_dataset('glue', 'stsb', split='validation')\n",
        "evaluator = EmbeddingSimilarityEvaluator(\n",
        "    sentences1=val_sts[\"sentence1\"],\n",
        "    sentences2=val_sts[\"sentence2\"],\n",
        "    scores=[score/5 for score in val_sts[\"label\"]],\n",
        "    main_similarity=\"cosine\"\n",
        ")"
      ],
      "metadata": {
        "id": "Zakf67wGcsJd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\""
      ],
      "metadata": {
        "id": "2srfP1CVe5aj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import losses, SentenceTransformer\n",
        "from sentence_transformers.trainer import SentenceTransformerTrainer\n",
        "from sentence_transformers.training_args import SentenceTransformerTrainingArguments\n",
        "\n",
        "# Define model\n",
        "embedding_model = SentenceTransformer('bert-base-uncased')\n",
        "\n",
        "# Loss function\n",
        "train_loss = losses.CosineSimilarityLoss(model=embedding_model)\n",
        "\n",
        "# Define the training arguments\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"augmented_embedding_model\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    warmup_steps=100,\n",
        "    fp16=True,\n",
        "    eval_steps=100,\n",
        "    logging_steps=100,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=embedding_model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    loss=train_loss,\n",
        "    evaluator=evaluator\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581,
          "referenced_widgets": [
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        "id": "XZIXRmVwcxP8",
        "outputId": "23dbd5ce-29f3-4b40-84e7-33e590b53b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name bert-base-uncased. Creating a new one with mean pooling.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1563' max='1563' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1563/1563 06:24, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.215800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.156500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.142600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.140200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.138500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.134500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.131700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.131300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.130500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.127900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.129600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.129900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.128200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.126500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.126600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b76c9ed5df6c4943b061dd8c5048f4be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1563, training_loss=0.13873551643893875, metrics={'train_runtime': 385.1185, 'train_samples_per_second': 129.825, 'train_steps_per_second': 4.058, 'total_flos': 0.0, 'train_loss': 0.13873551643893875, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our trained model\n",
        "evaluator(embedding_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5eZUFZaCc0La",
        "outputId": "48bf0053-7871-4150-bd12-aae7e658df53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pearson_cosine': 0.6967466004965847, 'spearman_cosine': 0.7074230860649906}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.accelerator.clear()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4h6alUTgc31y",
        "outputId": "8b78dd83-dd4b-4988-e703-920ebd24d142"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine gold + silver\n",
        "data = pd.concat([gold], ignore_index=True, axis=0)\n",
        "data = data.drop_duplicates(subset=['sentence1', 'sentence2'], keep=\"first\")\n",
        "train_dataset = Dataset.from_pandas(data, preserve_index=False)\n",
        "\n",
        "# Define model\n",
        "embedding_model = SentenceTransformer('bert-base-uncased')\n",
        "\n",
        "# Loss function\n",
        "train_loss = losses.CosineSimilarityLoss(model=embedding_model)\n",
        "\n",
        "# Define the training arguments\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"gold_only_embedding_model\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    warmup_steps=100,\n",
        "    fp16=True,\n",
        "    eval_steps=100,\n",
        "    logging_steps=100,\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=embedding_model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    loss=train_loss,\n",
        "    evaluator=evaluator\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204,
          "referenced_widgets": [
            "7e358f3ebfd046a8af57d1e3d485198d",
            "cbac5b94543241e481bfc374659c9b75",
            "b6fb2f9eb0bd40c4adf624c9e55efbb9",
            "6dd41b86756c4e1794207c608945ec0b",
            "267531145e2a49a9aacf5bf2d4a4d701",
            "3cf115ee49b74f3ea681df5ec3aaec9f",
            "2f7db0b5808e41649b6af8c056f1c778",
            "c701dbed33524fc08ed4a19631078ddc",
            "a746805cad9e4a338798d8054a910a9b",
            "6ec63d7670de4dc887171a81bb17f940",
            "2d640b1d4eca4f33a2d19d6008c6e494"
          ]
        },
        "id": "YLGo-md6c7K0",
        "outputId": "d56d6583-d705-4ee6-8313-04d0c365d140"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name bert-base-uncased. Creating a new one with mean pooling.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='313' max='313' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [313/313 01:32, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.226800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.171400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.160000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e358f3ebfd046a8af57d1e3d485198d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=313, training_loss=0.18524515438384523, metrics={'train_runtime': 92.3904, 'train_samples_per_second': 108.236, 'train_steps_per_second': 3.388, 'total_flos': 0.0, 'train_loss': 0.18524515438384523, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our trained model\n",
        "evaluator(embedding_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVmqoh1Nc99h",
        "outputId": "3854e274-9253-47dc-c6c9-a876a281a1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'pearson_cosine': 0.6209046334997653, 'spearman_cosine': 0.6475824789320501}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "import torch\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n"
      ],
      "metadata": {
        "id": "VAU0V_SndDBh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}