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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/GTimothee/cb3551ba6eb14b04f7a06d63ea4616f9/load-and-save-embed-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets\n",
        "!pip install --upgrade sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "pa9wkGU7i21H",
        "outputId": "d2b055b2-70a2-4b79-b57a-7e0e2aad8f1e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\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",
            "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[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\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-3.3.0-py3-none-any.whl.metadata (10 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.7/268.7 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers\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",
            "Successfully installed sentence-transformers-3.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# case where we want to add tokens"
      ],
      "metadata": {
        "id": "vEd-C3T8mwWJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9liyq3bLICqc"
      },
      "outputs": [],
      "source": [
        "# from sentence_transformers import SentenceTransformer\n",
        "# from peft import LoraConfig, TaskType\n",
        "\n",
        "# test_new_tokens = [\"[bla]\", \"[blub]\", \"[bloo]\", \"[blib]\"]\n",
        "\n",
        "# # Create a resized base model\n",
        "# model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
        "# model.tokenizer.add_tokens(test_new_tokens)\n",
        "# model[0].auto_model.resize_token_embeddings(len(model.tokenizer))\n",
        "# model.save_pretrained(\"all-MiniLM-L6-v2-resized\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "7uZFsDi5if_z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "code mostly copied from https://www.philschmid.de/fine-tune-embedding-model-for-rag"
      ],
      "metadata": {
        "id": "gZ_uW1DVnbZW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"philschmid/finanical-rag-embedding-dataset\", split=\"train\")\n",
        "\n",
        "# rename columns\n",
        "dataset = dataset.rename_column(\"question\", \"anchor\")\n",
        "dataset = dataset.rename_column(\"context\", \"positive\")\n",
        "\n",
        "# Add an id column to the dataset\n",
        "dataset = dataset.add_column(\"id\", range(len(dataset)))\n",
        "\n",
        "# split dataset into a 10% test set\n",
        "dataset = dataset.train_test_split(test_size=0.1)\n",
        "\n",
        "# save datasets to disk\n",
        "dataset[\"train\"].to_json(\"train_dataset.json\", orient=\"records\")\n",
        "dataset[\"test\"].to_json(\"test_dataset.json\", orient=\"records\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215,
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            "3faa44446e684f3fbaec3622875c93aa"
          ]
        },
        "id": "KhCNyIWpiKzq",
        "outputId": "47cc5d20-eaaa-45e6-c445-10d3a5063424"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/882 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cbddb365876477396610bafe3114c00"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/1.09M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "916b53848a2e45198d1ea3afdcd4e3c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/7000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62a80de00a2443e2a325f4ec0bb2ac9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/7 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f64e849e8ce742c0a68dfe942f1e8f7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64a4c4af79cf42859d519fffaa86be8a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "242472"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, concatenate_datasets\n",
        "train_dataset = load_dataset(\"json\", data_files=\"train_dataset.json\")['train'].select(range(500))\n",
        "val_dataset = load_dataset(\"json\", data_files=\"test_dataset.json\")['train']\n",
        "corpus_dataset = concatenate_datasets([train_dataset, val_dataset])\n",
        "\n",
        "corpus = dict(\n",
        "    zip(corpus_dataset[\"id\"], corpus_dataset[\"positive\"])\n",
        ")  # Our corpus (cid => document)\n",
        "queries = dict(\n",
        "    zip(val_dataset[\"id\"], val_dataset[\"anchor\"])\n",
        ")  # Our queries (qid => question)\n",
        "relevant_docs = {}  # Query ID to relevant documents (qid => set([relevant_cids])\n",
        "for q_id in queries:\n",
        "    relevant_docs[q_id] = [q_id]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "80e92517c9f145a39a99e33e1e16e2d1",
            "c4ba4c497256469aa2dec03033ddb4a9",
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            "2d90e43a971f4eacab56ce3e494dfcde",
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            "5bd8e1e3b6084ccf8953f207c046c57b"
          ]
        },
        "id": "IocKs1k-iqXe",
        "outputId": "352b3cd7-0566-4bdc-90d6-876fb63350f1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80e92517c9f145a39a99e33e1e16e2d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70b5083ce95a4a04b9f2f3d308af4ad1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers.evaluation import (\n",
        "    InformationRetrievalEvaluator\n",
        ")\n",
        "from sentence_transformers.util import cos_sim\n",
        "ir_evaluator = InformationRetrievalEvaluator(\n",
        "    queries=queries,\n",
        "    corpus=corpus,\n",
        "    relevant_docs=relevant_docs,\n",
        "    score_functions={\"cosine\": cos_sim},\n",
        "    batch_size=32,\n",
        "    corpus_chunk_size=100,\n",
        "    show_progress_bar=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87,
          "referenced_widgets": [
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            "7836a1b191184c55b2f9ca54e37f4a69"
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        },
        "id": "GAluOJ46i_ck",
        "outputId": "edcfaec7-9a3a-4c4f-cffe-97f58616d24e"
      },
      "execution_count": 5,
      "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": "055a26e0b776423da6981e229d86c921"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "from peft import LoraConfig, TaskType\n",
        "import torch\n",
        "\n",
        "# Load the resized model and add an adapter\n",
        "# model = SentenceTransformer(\"all-MiniLM-L6-v2-resized\")\n",
        "model = SentenceTransformer(\"all-MiniLM-L6-v2\", device=\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "peft_config = LoraConfig(\n",
        "    task_type=TaskType.FEATURE_EXTRACTION,\n",
        "    inference_mode=False,\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.1,\n",
        ")\n",
        "model.add_adapter(peft_config)\n",
        "\n",
        "embedding = model.encode(\"[bla] my name is [blub]\")\n",
        "print(embedding[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425,
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            "73abbee8c18143098239916cbb043db8",
            "0122b1b060e943b89ca76876688e0be8",
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            "553d28d720a44487bb95d2bf5829dee8"
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        },
        "id": "qQq-7SDHIhIA",
        "outputId": "2a5ba1d2-6400-409c-d54b-b89767f3a23d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "512c3f6e7f46498ab5baeb3bc600286b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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            ],
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          },
          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e31cc7cb44bf457ebef36c0b2d74b92f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.05346014 -0.02178512 -0.00050324 -0.02896873 -0.09822001  0.00544652\n",
            "  0.19883959  0.00645556 -0.04425739 -0.09591077]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = ir_evaluator(model)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357,
          "referenced_widgets": [
            "01df1dcefc054dedae762cb7f181ddbd",
            "01774c8db33b42ba9198199c9b89e633",
            "626320c80eb84c77bffd20385a2d53e2",
            "21b7126ac4c643e4a971550b432d8e5b",
            "23a4656e337e41b88da26c23c033e05c",
            "b5cc6abce44a476196ac72a82d8e24da",
            "0bc2ace14bb94596b93f374b85c53eab",
            "a58717641b90407492a872bb3e616600",
            "50953885582d495782924e5278512857",
            "445f031729144ee6ac6279ee8e48ab45",
            "5c7c2a39c5584fdcbf6dc6de323f0421"
          ]
        },
        "id": "IF63o9kbljdb",
        "outputId": "9964243e-4e11-4d34-9ed5-1012d72d0bbe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/22 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01df1dcefc054dedae762cb7f181ddbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00,  7.58it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cosine_accuracy@1': 0.7842857142857143,\n",
              " 'cosine_accuracy@3': 0.8914285714285715,\n",
              " 'cosine_accuracy@5': 0.9171428571428571,\n",
              " 'cosine_accuracy@10': 0.9457142857142857,\n",
              " 'cosine_precision@1': 0.7842857142857143,\n",
              " 'cosine_precision@3': 0.29714285714285715,\n",
              " 'cosine_precision@5': 0.18342857142857139,\n",
              " 'cosine_precision@10': 0.09457142857142856,\n",
              " 'cosine_recall@1': 0.7842857142857143,\n",
              " 'cosine_recall@3': 0.8914285714285715,\n",
              " 'cosine_recall@5': 0.9171428571428571,\n",
              " 'cosine_recall@10': 0.9457142857142857,\n",
              " 'cosine_ndcg@10': 0.8678902907115743,\n",
              " 'cosine_mrr@10': 0.842611111111111,\n",
              " 'cosine_map@100': 0.8451141243804458}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformerTrainingArguments\n",
        "from sentence_transformers.training_args import BatchSamplers\n",
        "\n",
        "args = SentenceTransformerTrainingArguments(\n",
        "    output_dir=\"training_trial\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=2,\n",
        "    gradient_accumulation_steps=1,\n",
        "    per_device_eval_batch_size=.5,\n",
        "    warmup_ratio=0.1,\n",
        "\n",
        "    learning_rate=1e-5,\n",
        "    weight_decay=0.001,\n",
        "    fp16=True,\n",
        "    lr_scheduler_type='reduce_lr_on_plateau',\n",
        "    report_to=None,\n",
        "\n",
        "    batch_sampler=BatchSamplers.NO_DUPLICATES,\n",
        "    eval_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_steps=.2,\n",
        "    save_total_limit=3,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_cosine_ndcg@10\",\n",
        ")"
      ],
      "metadata": {
        "id": "y_cs61pEjaZl"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformerTrainer\n",
        "from sentence_transformers.losses import MultipleNegativesRankingLoss\n",
        "\n",
        "trainer = SentenceTransformerTrainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset.select_columns(\n",
        "        [\"anchor\", \"positive\"]\n",
        "    ),\n",
        "    loss=MultipleNegativesRankingLoss(model),\n",
        "    evaluator=ir_evaluator\n",
        ")"
      ],
      "metadata": {
        "id": "c8Tz9Ea5jjqC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_MODE\"] = \"disabled\"\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598,
          "referenced_widgets": [
            "1e3cf7b8d8cb43d1a6e6c4ed3e230c60",
            "9349e5af702d4ec190e0c5b3a72b680a",
            "cf17822f874e4345bf6e545e97a5a3bc",
            "89cf6f27e2d3456a90f82b4f19957270",
            "c6b877db432f44379d98b5deabbe0ac9",
            "f4884675f6f84016af0fadc9817a251c",
            "81d94a9dece2450c9de2a4d64f7be9f1",
            "b43abb72c0654d6a862410bab00672ab",
            "e0a2a1c4c10e4887bd6a27ae466f3651",
            "409af25ed298495aba779d3989b0d52b",
            "9b515ddee3b24540998dc6e2080ec5f1",
            "842604dd1db9498fa7e826f375514479",
            "5aa2596669754c8d81aa6bfedab007a9",
            "1741b8aca8d34f2eafef7e0452efaa3c",
            "b511fcad5c5b496283d7e1e05f8c6db7",
            "b38f8c334d5d4d869d42df75e0a36499",
            "c3e59487742446dca9c607090ef46f77",
            "a365cc7737a84504839836a6b24da465",
            "e676e64e9af445f7b9fbcd59a0c007e9",
            "5c5161841c0c42a295f0ceefaebffa09",
            "ddb20b36016e483f9549fdc1ebdebf26",
            "246566b5287842a29dfde452936afc38"
          ]
        },
        "id": "FRBTKuNXjlND",
        "outputId": "1673e970-4bf1-4ff9-af82-87b2dbb06694"
      },
      "execution_count": 11,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='251' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 00:12, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Cosine Accuracy@1</th>\n",
              "      <th>Cosine Accuracy@3</th>\n",
              "      <th>Cosine Accuracy@5</th>\n",
              "      <th>Cosine Accuracy@10</th>\n",
              "      <th>Cosine Precision@1</th>\n",
              "      <th>Cosine Precision@3</th>\n",
              "      <th>Cosine Precision@5</th>\n",
              "      <th>Cosine Precision@10</th>\n",
              "      <th>Cosine Recall@1</th>\n",
              "      <th>Cosine Recall@3</th>\n",
              "      <th>Cosine Recall@5</th>\n",
              "      <th>Cosine Recall@10</th>\n",
              "      <th>Cosine Ndcg@10</th>\n",
              "      <th>Cosine Mrr@10</th>\n",
              "      <th>Cosine Map@100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.781429</td>\n",
              "      <td>0.894286</td>\n",
              "      <td>0.917143</td>\n",
              "      <td>0.945714</td>\n",
              "      <td>0.781429</td>\n",
              "      <td>0.298095</td>\n",
              "      <td>0.183429</td>\n",
              "      <td>0.094571</td>\n",
              "      <td>0.781429</td>\n",
              "      <td>0.894286</td>\n",
              "      <td>0.917143</td>\n",
              "      <td>0.945714</td>\n",
              "      <td>0.867046</td>\n",
              "      <td>0.841435</td>\n",
              "      <td>0.844011</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:418: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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": "1e3cf7b8d8cb43d1a6e6c4ed3e230c60"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/22 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "842604dd1db9498fa7e826f375514479"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00,  8.50it/s]\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:418: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'training_trial/checkpoint-250/pytorch_model.bin'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-af73f3eb6fa7>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"WANDB_MODE\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"disabled\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   2121\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2122\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2123\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   2124\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2599\u001b[0m                 \u001b[0msmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarrier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2601\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_best_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2603\u001b[0m         \u001b[0;31m# add remaining tr_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sentence_transformers/trainer.py\u001b[0m in \u001b[0;36m_load_best_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    511\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_best_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0mloaded_auto_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_load_best_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2916\u001b[0m                         \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafetensors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_safe_model_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2917\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2918\u001b[0;31m                         state_dict = torch.load(\n\u001b[0m\u001b[1;32m   2919\u001b[0m                             \u001b[0mbest_model_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2920\u001b[0m                             \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m   1317\u001b[0m         \u001b[0mpickle_load_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1320\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_zipfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m             \u001b[0;31m# The zipfile reader is going to advance the current file position.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m    657\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    660\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"w\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m    638\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_opener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 640\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'training_trial/checkpoint-250/pytorch_model.bin'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Save the adapter itself\n",
        "# model.save_pretrained(\"all-MiniLM-L6-v2-adapter\")\n",
        "# # Load the adapter directly\n",
        "# loaded_model = SentenceTransformer(\"all-MiniLM-L6-v2-adapter\")\n",
        "# embedding = loaded_model.encode(\"[bla] my name is [blub]\")\n",
        "# print(embedding[:10])"
      ],
      "metadata": {
        "id": "vuCJvdjIIZOk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "npfoGXz9Iqn6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}