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      }
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Spanish QA asymmetric\n",
        "\n",
        "Training a model to create embeddings for asymmetric semantic search using Spanish language:\n",
        "\n",
        "### References:\n",
        "\n",
        "* https://www.sbert.net/docs/training/overview.html\n",
        "* https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py\n",
        "* https://www.pinecone.io/learn/nlp/\n",
        "* https://www.pinecone.io/learn/fine-tune-sentence-transformers-mnr/\n",
        "* https://huggingface.co/datasets/unicamp-dl/mmarco/viewer/spanish/train\n",
        "* https://huggingface.co/datasets/unicamp-dl/mrobust/viewer/queries-spanish"
      ],
      "metadata": {
        "id": "_MPGiOSMS9YL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Config"
      ],
      "metadata": {
        "id": "Z5-11A81UHId"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CONFIG = {\n",
        "    # Base model\n",
        "    'model_name': 'PlanTL-GOB-ES/roberta-base-bne',  # TODO: check large?\n",
        "    'max_seq_length': 512,\n",
        "\n",
        "    # Train model\n",
        "    'epochs': 10,  # 4, 10, 30\n",
        "    'warmup_steps': 1,  # 100, 1000\n",
        "    'batch_size': 16,  # 32\n",
        "    'optimizer_params': {'lr': 2e-5},\n",
        "    'loss': 'tl',  # {'mnrl', 'mse', 'tl'}\n",
        "\n",
        "    # Dataset\n",
        "    'dataset_train_size': 512,  # 481_335 # 500_000 # bottleneck: GPU memory limits\n",
        "    # 'multiple_negatives': False,\n",
        "    'dataset_name': \"dariolopez/ms-marco-es-500k\",  # {\"dariolopez/ms-marco-es-500k\", \"IIC/ms_marco_es\"}\n",
        "\n",
        "    # General\n",
        "    'seed': 42\n",
        "}"
      ],
      "metadata": {
        "id": "lkCACpCeUFvQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(CONFIG)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyezdTlHzpib",
        "outputId": "2f61336f-d5c1-4d7f-d272-d2a833391693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'model_name': 'PlanTL-GOB-ES/roberta-base-bne', 'max_seq_length': 512, 'epochs': 10, 'warmup_steps': 1, 'batch_size': 16, 'optimizer_params': {'lr': 2e-05}, 'loss': 'tl', 'dataset_train_size': 512, 'dataset_name': 'dariolopez/ms-marco-es-500k', 'seed': 42}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install libraries"
      ],
      "metadata": {
        "id": "XY6oU3N7kjiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIUo5-ofYfLa",
        "outputId": "74cd8af4-3a89-483a-95d4-7f25d097b42b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.10.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.22.4)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (3.8.1)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.15.1+cu118)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.0.0+cu118)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.65.0)\n",
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            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.2.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.1.99)\n",
            "Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.14.1)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
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            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
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            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (4.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (3.1)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (2.0.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (3.1.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers) (1.11.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence-transformers) (16.0.2)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->sentence-transformers) (3.25.2)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.13.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2022.10.31)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (1.2.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (8.1.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 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) (2022.7.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.1.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers) (8.4.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "e_zEojCVkvOh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from datetime import datetime\n",
        "\n",
        "from sentence_transformers import InputExample, SentenceTransformer, models, losses\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader"
      ],
      "metadata": {
        "id": "mNA0jukC7X0Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check GPU"
      ],
      "metadata": {
        "id": "m6IfC5P0CbFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0n3Ht_yHCCmU",
        "outputId": "b0e32662-84d9-41de-d223-3f4bfe00cd0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed May  3 07:18:25 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   73C    P8    11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ],
      "metadata": {
        "id": "IUCimEyOCd4X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIZWWLyKCeib",
        "outputId": "87a4da54-c50f-4e37-d3de-0c4557f58e7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Seed"
      ],
      "metadata": {
        "id": "XZX1EpVQCISV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def set_seed(seed):\n",
        "    '''Sets the seed of the entire notebook so results are the same every time we run.\n",
        "    This is for REPRODUCIBILITY.'''\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    # When running on the CuDNN backend, two further options must be set\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    # Set a fixed value for the hash seed\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    \n",
        "set_seed(CONFIG['seed'])"
      ],
      "metadata": {
        "id": "IFmBNNrPCFXf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model"
      ],
      "metadata": {
        "id": "LO5ariHN4ThL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word_embedding_model = models.Transformer(\n",
        "    model_name_or_path=CONFIG['model_name'],\n",
        "    max_seq_length=CONFIG['max_seq_length'],\n",
        "    tokenizer_name_or_path=CONFIG['model_name']\n",
        ")\n",
        "pooling_model = models.Pooling(\n",
        "    word_embedding_model.get_word_embedding_dimension()\n",
        ")\n",
        "model = SentenceTransformer(\n",
        "    modules=[word_embedding_model, pooling_model],\n",
        "    device=device\n",
        ")"
      ],
      "metadata": {
        "id": "_RcE5ZYGOIAS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "810be85e-cca3-45ec-aebd-90f101cc41d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at PlanTL-GOB-ES/roberta-base-bne were not used when initializing RobertaModel: ['lm_head.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CONFIG['length_embedding'] = word_embedding_model.get_word_embedding_dimension()"
      ],
      "metadata": {
        "id": "7cyllR59W7Mb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(CONFIG)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94ziigYoXDvL",
        "outputId": "a7a745ae-0d69-4c4b-fe01-9685fb85ed95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'model_name': 'PlanTL-GOB-ES/roberta-base-bne', 'max_seq_length': 512, 'epochs': 10, 'warmup_steps': 1, 'batch_size': 16, 'optimizer_params': {'lr': 2e-05}, 'loss': 'tl', 'dataset_train_size': 512, 'dataset_name': 'dariolopez/ms-marco-es-500k', 'seed': 42, 'length_embedding': 768}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DstzKVCBUm0K",
        "outputId": "a9e4e758-5124-4d1a-fb1f-61d2436e35c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SentenceTransformer(\n",
            "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel \n",
            "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dataset"
      ],
      "metadata": {
        "id": "WNj0tGxk4wwB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "import datasets\n",
        "\n",
        "marco_es = datasets.load_dataset(CONFIG['dataset_name'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "eb6ca7a72a1b43a8bd692a4855d94246",
            "6b6376e092884752a4beb57379f39367",
            "34367e6d21664ec9be04646b246ee771",
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        },
        "id": "LLIUJ1QTSYMu",
        "outputId": "a99eb4b9-d9d8-4677-8b8e-e57c9697fdd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/dariolopez___parquet/dariolopez--ms-marco-es-500k-dc62cf045b9171b5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb6ca7a72a1b43a8bd692a4855d94246"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 922 ms, sys: 172 ms, total: 1.09 s\n",
            "Wall time: 4.78 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "if CONFIG['dataset_name'] == 'dariolopez/ms-marco-es-500k':\n",
        "    marco_es = marco_es['train']\n",
        "else:  # \"IIC/ms_marco_es\"\n",
        "    marco_es = concatenate_datasets([marco_es['train'], marco_es['validation'], marco_es['test']])"
      ],
      "metadata": {
        "id": "pwZYfq3KyRUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(marco_es)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dr-UPqgYa4v",
        "outputId": "787a5d6b-b233-458c-9379-0419578f61da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['query', 'positive', 'negative'],\n",
            "    num_rows: 500000\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare for training"
      ],
      "metadata": {
        "id": "9h3AF5TT8QJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if CONFIG['dataset_name'] == 'dariolopez/ms-marco-es-500k':  # query - positive - negative  https://huggingface.co/datasets/dariolopez/ms-marco-es\n",
        "    train_samples = [\n",
        "        InputExample(texts=[row['query'], row['positive'], row['negative']])\n",
        "        for row in marco_es.select(range(CONFIG['dataset_train_size']))\n",
        "    ]\n",
        "else:  # query - passage - label https://huggingface.co/datasets/IIC/ms_marco_es\n",
        "    train_samples = [\n",
        "        InputExample(texts=[row['query'], row['passages']], label=row['labels'])\n",
        "        for row in marco_es.select(range(CONFIG['dataset_train_size']))\n",
        "    ]"
      ],
      "metadata": {
        "id": "8tVRpWRhZAqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"length train samples: {len(train_samples)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eubh6qqfXkwL",
        "outputId": "ccac7047-e84f-475f-e520-36e49e4ca925"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "length train samples: 512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=CONFIG['batch_size'])"
      ],
      "metadata": {
        "id": "dtQp6gWpOICu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "now = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
        "model_save_path = os.path.join('output', now)\n",
        "os.makedirs(model_save_path, exist_ok=True)\n",
        "with open(os.path.join(model_save_path, 'train_config.json'), 'w') as file:\n",
        "    file.write(json.dumps(CONFIG, indent=4))"
      ],
      "metadata": {
        "id": "dR3Ae_Wq7v0e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "\n",
        "def free_memory(score, epoch, steps):\n",
        "    torch.cuda.empty_cache()\n",
        "    gc.collect()"
      ],
      "metadata": {
        "id": "E7hUNFLlzbU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if CONFIG['loss'] == 'mnrl':\n",
        "    train_loss = losses.MultipleNegativesRankingLoss(model=model)\n",
        "elif CONFIG['loss'] == 'mse':\n",
        "    train_loss = losses.MarginMSELoss(model=model)\n",
        "elif CONFIG['loss'] == 'tl':\n",
        "    train_loss = losses.TripletLoss(model=model)\n",
        "else:\n",
        "    train_loss = losses.CosineSimilarityLoss(model=model)"
      ],
      "metadata": {
        "id": "7aYSEX8r5-De"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAheIw8zYfhL",
        "outputId": "58b94048-2008-4a05-d1fb-93c75364e895"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TripletLoss(\n",
            "  (model): SentenceTransformer(\n",
            "    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel \n",
            "    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train"
      ],
      "metadata": {
        "id": "A-Go4ufF71PM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(\n",
        "    train_objectives=[(train_dataloader, train_loss)],\n",
        "    epochs=CONFIG['epochs'],\n",
        "    warmup_steps=CONFIG['warmup_steps'],\n",
        "    save_best_model=True,\n",
        "    show_progress_bar=True,\n",
        "    use_amp=True,  # If your GPU does not have FP16 cores, set use_amp=False\n",
        "    callback=free_memory,\n",
        "    checkpoint_save_steps=len(train_dataloader),\n",
        "    checkpoint_path=model_save_path,\n",
        ")"
      ],
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        "id": "Mv3C96clEMty",
        "outputId": "9e01b275-6e80-44bd-f35a-196ef38f80c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
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              "model_id": "2248b7b890bc4e58a38451b90eedff51"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "efe2ef5edc2a4af7b1114f7b11653efd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c983d88dcea84ab9bf906875b1d84b40"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0966f0dcc5dc4e4f8afa8971c07b64f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13492610aeab44e0870703d5fc555ef1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "631041a608ab42f4a8e5bf3e8917719e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Iteration:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8662bb95c60b49c78559a0e4055985f7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save and push to HuggingFace"
      ],
      "metadata": {
        "id": "YJrJ5gb65RpU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train latest model\n",
        "model.save(model_save_path)"
      ],
      "metadata": {
        "id": "hnywDRqUO2S3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_save_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvD-23ek5Udp",
        "outputId": "aded16a3-74b6-4801-fb85-4c00cd19a486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "output/2023-05-03_07-18-35\n"
          ]
        }
      ]
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    {
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      "source": [],
      "metadata": {
        "id": "ENfvM3Po9m9U"
      },
      "execution_count": null,
      "outputs": []
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}