{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ay9HJhbH03Kx",
        "outputId": "4170bd29-744e-42f9-b9ae-7b3a68a987f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: MarkupSafe>=2.0 in ./.venv/lib/python3.10/site-packages (from jinja2->torch!=1.12.0,>=1.10->transformers[torch]) (2.1.3)\n",
            "Requirement already satisfied: executing>=1.2.0 in ./.venv/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (1.2.0)\n",
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            "Requirement already satisfied: pure-eval in ./.venv/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets ipywidgets transformers[torch] evaluate scikit-learn ray[tune] wandb hyperopt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
            "b79e9c0aa7f442a5956eeb03f684a357",
            "97b0c3827a2040a3b8c81cad7e2f9d78",
            "291adaefb7fb4122b8101076d3412d95",
            "549a797c9e584a49b82f95448513e742",
            "9aa90b4b549345fcafe4b6344532ce7a",
            "f2a14fe11c1f4e34af19958071f9c0cb",
            "92ae36322f5f41b1ba0c3d012eaa274d",
            "cccfa6f765a74fe89d777bab68332fca",
            "0d89dabdf49146d19c5ec6170c5b8efb",
            "b81730ef186548e0924c5add62503e93",
            "169801107d4c426faddda35cdc784e1d",
            "75820af3aa214f3eb1e85c937fccd189",
            "a00fcad40d6e45cf9717d6203676d646",
            "337dfd3562d743f79487582f03f3f396",
            "1e4e36eb6f004f9c9d6101fc81a53983",
            "606d9c327bc04b74ac676b9340acb3df",
            "df03b594bba94ca781b2f68f10d8f928"
          ]
        },
        "id": "hpFXbjdecnzZ",
        "outputId": "b62c1bc5-76cf-4c58-c896-e2068781df1f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab399e5a1fb845fba2388706b9185da4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import huggingface_hub\n",
        "huggingface_hub.login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "env: WANDB_PROJECT=OO_Fine_Tune\n",
            "env: WANDB_NOTEBOOK_NAME=/home/ed/Downloads/OO-Fine-Tune/OO_Fine_Tune.ipynb\n",
            "env: WANDB_LOG_MODEL=off # this is slow for sweeps\n"
          ]
        }
      ],
      "source": [
        "%env WANDB_PROJECT=OO_Fine_Tune\n",
        "%env WANDB_NOTEBOOK_NAME=/home/ed/Downloads/OO-Fine-Tune/OO_Fine_Tune.ipynb\n",
        "%env WANDB_LOG_MODEL=off # this is slow for sweeps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685,
          "referenced_widgets": [
            "69ab6bc4b3634a749f3a3ba96b7c92c0",
            "2535bae27f3c458b93f9ea061e380673",
            "a6c6968592e34df3b00a9ab597e5c534",
            "751dc58b716f4094b4d4db1a747a3993",
            "d5f77daac15d43068201bd328b300f81",
            "14e5855f9b724428b11704cb81381d7d",
            "76cc0ba59652426d99b126b64f037b1d",
            "b5e3f87798b348f2880662f26e142d97",
            "7aadb1e580574c39912111bdcfcba73b",
            "af219f1766194792b25aa97c1cef16a0",
            "f7964bff97fe4435b7979b5a23b657ff",
            "59aa5a6b279144eb81ccd72108fbbc7b",
            "7dd39ac58f5e4ca6aa5642265e700aea",
            "28204b58380641e28fce27738bb43dae",
            "339d32dcf6284c57a549cf23b9ab1ec0",
            "7dce9186c34c472597f809630b4ddb37",
            "74cd6e31936f4f7eb87b42fa7718fcc9",
            "6dfc0604eef9436fb3a4697c9d632f76",
            "ac1d4f17e58c45d5b4eb12bd9caef7a1",
            "1fc18bbc5a7d46e7844d249f28d8a858",
            "2bc86922f36d48b4946ab2735fc2ce46",
            "bcb655bb340c42a2ab56c79a87c929a0",
            "9e9331b15b6641f485e26d25ae23064c",
            "e1f9678665b2478fa9088799b01dc532",
            "6f17309eb1464767a37b9ef2b390bf34",
            "cc0cf98c97ac4dac848e5adc6d7509a9",
            "5e4df184e5834b77a46f9a6b673723b1",
            "e1d2813ad72542a581969589c6bff59b",
            "220f1f005cca4f4592e23dc56a151741",
            "dc08d9d22db341a29e5a2e27444ed061",
            "5e8f0d332ab44319a6170c4c1d84a2ec",
            "0f191700a7e94c1fa497991071441748",
            "e19a7112910e44739c0b7851e155ef32",
            "3b7f7733617a4695b0fdeff681448f92",
            "3c0e40c13e184f899090477df45c5e50",
            "ee7b0c3693c84e9eba2053ad8ad16cb3",
            "4e21b0aa25224fe6a91038e06f420215",
            "5b9477e96a1b489481c3e8016b6985ff",
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        "id": "9kfAgynu09Do",
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "import datasets\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "# Configs\n",
        "model_name = \"huggingface/CodeBERTa-small-v1\"\n",
        "exp_name = \"oo-method-test-model-bylibrary\"\n",
        "#train_size = \"[:1%]\"\n",
        "quick_test_percent = 1.0\n",
        "eval_steps = 0.2\n",
        "save_steps = 0.2\n",
        "push = False\n",
        "hyperopt = True\n",
        "if hyperopt: push = False\n",
        "early_patience = 10 if hyperopt else 5\n",
        "resume = False\n",
        "\n",
        "train_split = f\"train\"\n",
        "\n",
        "id2label = {0: \"func\", 1: \"method\"}\n",
        "label2id = {\"func\": 0, \"method\": 1}\n",
        "\n",
        "def model_init():\n",
        "    global best_accuracy\n",
        "    best_accuracy = 0.0\n",
        "    return AutoModelForSequenceClassification.from_pretrained(model_name,\n",
        "                                                             id2label=id2label,\n",
        "                                                             label2id=label2id,\n",
        "                                                             num_labels=2)\n",
        "\n",
        "model = model_init()\n",
        "\n",
        "small_ds_train = datasets.load_dataset(\"ejschwartz/oo-method-test-split\", \"bylibrary\", split=train_split)\n",
        "ds_dev = datasets.load_dataset(\"ejschwartz/oo-method-test-split\", \"bylibrary\", split=\"test\")\n",
        "small_ds_dev = ds_dev.shuffle(seed=42).select(range(round(len(ds_dev) * quick_test_percent)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "sobBO6U_2J2X"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90128fa600014c0b8b76855b171c8f46",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/2836 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"Disassembly\"], padding=\"max_length\", truncation=True)\n",
        "\n",
        "small_ds_train = small_ds_train.map(tokenize_function, batched=True, num_proc=8).rename_column(\"Disassembly\", \"text\").rename_column(\"Type\", \"label\")\n",
        "small_ds_dev = small_ds_dev.map(tokenize_function, batched=True, num_proc=8).rename_column(\"Disassembly\", \"text\").rename_column(\"Type\", \"label\")\n",
        "ds_dev = ds_dev.map(tokenize_function, batched=True, num_proc=8).rename_column(\"Disassembly\", \"text\").rename_column(\"Type\", \"label\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sXujBOAP2imZ"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "import transformers\n",
        "import numpy as np\n",
        "\n",
        "data_collator = transformers.DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "\n",
        "training_args = transformers.TrainingArguments(output_dir=exp_name,\n",
        "                                  #auto_find_batch_size=True,\n",
        "                                  per_device_train_batch_size=8,\n",
        "                                  per_device_eval_batch_size=8,\n",
        "                                  learning_rate=0.00000245415537008783,\n",
        "                                  max_steps=887,\n",
        "                                  logging_first_step=True,\n",
        "                                  evaluation_strategy=\"steps\",\n",
        "                                  #eval_steps=eval_steps,\n",
        "                                  eval_steps=eval_steps,\n",
        "                                  logging_steps=eval_steps,\n",
        "                                  save_steps=save_steps,\n",
        "                                  push_to_hub=push,\n",
        "                                  report_to=\"wandb\",\n",
        "                                  run_name=exp_name,\n",
        "                                  save_total_limit=0,\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  metric_for_best_model=\"eval_accuracy\",\n",
        "                                  lr_scheduler_type=\"cosine\",\n",
        "                                  warmup_ratio=0.05,\n",
        "                                  num_train_epochs=0.2,\n",
        "                                  )\n",
        "\n",
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "  global best_accuracy\n",
        "  logits, labels = eval_pred\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "  metrics = metric.compute(predictions=predictions, references=labels)\n",
        "  metrics['best_accuracy'] = max(best_accuracy, metrics['accuracy'])\n",
        "  best_accuracy = metrics['best_accuracy']\n",
        "  return metrics\n",
        "\n",
        "total_steps = len(small_ds_train) // training_args.per_device_train_batch_size * training_args.num_train_epochs\n",
        "#optimizer = transformer\n",
        "#scheduler = transformers.get_cosine_schedule_with_warmup(num_warmup_steps=total_steps*0.05, num_training_steps=total_steps)\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model_init=model_init,\n",
        "    #model=model,\n",
        "    args=training_args,\n",
        "    tokenizer=tokenizer,\n",
        "    train_dataset=small_ds_train,\n",
        "    eval_dataset=small_ds_dev,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks = [transformers.EarlyStoppingCallback(early_stopping_patience=early_patience)],\n",
        "    data_collator=data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "gO57fykO862s"
      },
      "outputs": [
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          "text": [
            "Create sweep with ID: 1wpa6h8x\n",
            "Sweep URL: https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x\n"
          ]
        },
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 97sjboe5 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3.662189175786302e-06\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 116.0448051231694\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/97sjboe5' target=\"_blank\">blooming-sweep-1</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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              " View sweep at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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              " View run at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/97sjboe5' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/97sjboe5</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▁▁█</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▁█</td></tr><tr><td>eval/loss</td><td>█▇▇▁</td></tr><tr><td>eval/runtime</td><td>▁▄▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▅▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▅▂▁</td></tr><tr><td>train/epoch</td><td>▁▁▁▁▁█████</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▂█▆▃▁</td></tr><tr><td>train/loss</td><td>█▃▁▁▃</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.30395</td></tr><tr><td>eval/best_accuracy</td><td>0.30395</td></tr><tr><td>eval/loss</td><td>0.98659</td></tr><tr><td>eval/runtime</td><td>45.5047</td></tr><tr><td>eval/samples_per_second</td><td>62.323</td></tr><tr><td>eval/steps_per_second</td><td>7.801</td></tr><tr><td>train/epoch</td><td>0.01</td></tr><tr><td>train/global_step</td><td>116</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.5725</td></tr><tr><td>train/total_flos</td><td>122929745952768.0</td></tr><tr><td>train/train_loss</td><td>0.55012</td></tr><tr><td>train/train_runtime</td><td>235.2118</td></tr><tr><td>train/train_samples_per_second</td><td>3.945</td></tr><tr><td>train/train_steps_per_second</td><td>0.493</td></tr></table><br/></div></div>"
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            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▆▁▆█</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▃█</td></tr><tr><td>eval/loss</td><td>▁▄██</td></tr><tr><td>eval/runtime</td><td>▁▇█▆</td></tr><tr><td>eval/samples_per_second</td><td>█▂▁▃</td></tr><tr><td>eval/steps_per_second</td><td>█▂▁▃</td></tr><tr><td>train/epoch</td><td>▁▃▃▅▅▅▅▆▆█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▁</td></tr><tr><td>train/loss</td><td>█▅▂▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87236</td></tr><tr><td>eval/best_accuracy</td><td>0.87236</td></tr><tr><td>eval/loss</td><td>0.4741</td></tr><tr><td>eval/runtime</td><td>45.9606</td></tr><tr><td>eval/samples_per_second</td><td>61.705</td></tr><tr><td>eval/steps_per_second</td><td>7.724</td></tr><tr><td>train/epoch</td><td>0.04</td></tr><tr><td>train/global_step</td><td>412</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1601</td></tr><tr><td>train/total_flos</td><td>436612545970176.0</td></tr><tr><td>train/train_loss</td><td>0.24878</td></tr><tr><td>train/train_runtime</td><td>355.9155</td></tr><tr><td>train/train_samples_per_second</td><td>9.261</td></tr><tr><td>train/train_steps_per_second</td><td>1.158</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">toasty-sweep-2</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ezujfbw7' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ezujfbw7</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 1snmzbiz with config:\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 366.1063867116238\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              " View run <strong style=\"color:#cdcd00\">dulcet-sweep-3</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/1snmzbiz' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/1snmzbiz</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "{'eval_loss': 0.45402804017066956, 'eval_accuracy': 0.8758815232722144, 'eval_best_accuracy': 0.8758815232722144, 'eval_runtime': 45.927, 'eval_samples_per_second': 61.75, 'eval_steps_per_second': 7.73, 'epoch': 0.03}\n",
            "{'train_runtime': 336.8267, 'train_samples_per_second': 8.693, 'train_steps_per_second': 1.087, 'train_loss': 0.22382783889770508, 'epoch': 0.04}\n"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▃▁▇█</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▇█</td></tr><tr><td>eval/loss</td><td>▁█▃▃</td></tr><tr><td>eval/runtime</td><td>▁▅▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▄▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▂▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▃▃▅▅▆▆█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▃▁</td></tr><tr><td>train/loss</td><td>█▅▃▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87588</td></tr><tr><td>eval/best_accuracy</td><td>0.87588</td></tr><tr><td>eval/loss</td><td>0.45403</td></tr><tr><td>eval/runtime</td><td>45.927</td></tr><tr><td>eval/samples_per_second</td><td>61.75</td></tr><tr><td>eval/steps_per_second</td><td>7.73</td></tr><tr><td>train/epoch</td><td>0.04</td></tr><tr><td>train/global_step</td><td>366</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.0991</td></tr><tr><td>train/total_flos</td><td>387864543264768.0</td></tr><tr><td>train/train_loss</td><td>0.22383</td></tr><tr><td>train/train_runtime</td><td>336.8267</td></tr><tr><td>train/train_samples_per_second</td><td>8.693</td></tr><tr><td>train/train_steps_per_second</td><td>1.087</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">dulcet-sweep-3</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/1snmzbiz' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/1snmzbiz</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: zislhxig with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.7061350795742976e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 492.6120506548938\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/zislhxig' target=\"_blank\">rich-sweep-4</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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              " View sweep at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'learning_rate' was locked by 'sweep' (ignored update).\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "<style>\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▅█▅</td></tr><tr><td>eval/best_accuracy</td><td>▁▅██</td></tr><tr><td>eval/loss</td><td>▁▄▂█</td></tr><tr><td>eval/runtime</td><td>▁▇▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▂▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▂▃▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▁</td></tr><tr><td>train/loss</td><td>█▅▂▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.86495</td></tr><tr><td>eval/best_accuracy</td><td>0.89492</td></tr><tr><td>eval/loss</td><td>0.56404</td></tr><tr><td>eval/runtime</td><td>46.0188</td></tr><tr><td>eval/samples_per_second</td><td>61.627</td></tr><tr><td>eval/steps_per_second</td><td>7.714</td></tr><tr><td>train/epoch</td><td>0.05</td></tr><tr><td>train/global_step</td><td>492</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1083</td></tr><tr><td>train/total_flos</td><td>521391681110016.0</td></tr><tr><td>train/train_loss</td><td>0.2081</td></tr><tr><td>train/train_runtime</td><td>386.4211</td></tr><tr><td>train/train_samples_per_second</td><td>10.186</td></tr><tr><td>train/train_steps_per_second</td><td>1.273</td></tr></table><br/></div></div>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
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            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "<style>\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▇██</td></tr><tr><td>eval/best_accuracy</td><td>▁▇██</td></tr><tr><td>eval/loss</td><td>█▄▁▁</td></tr><tr><td>eval/runtime</td><td>▁▅▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▄▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▃▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▅▅▆▆▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▅▂▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87094</td></tr><tr><td>eval/best_accuracy</td><td>0.87094</td></tr><tr><td>eval/loss</td><td>0.47905</td></tr><tr><td>eval/runtime</td><td>46.0672</td></tr><tr><td>eval/samples_per_second</td><td>61.562</td></tr><tr><td>eval/steps_per_second</td><td>7.706</td></tr><tr><td>train/epoch</td><td>0.06</td></tr><tr><td>train/global_step</td><td>656</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.2063</td></tr><tr><td>train/total_flos</td><td>695188908146688.0</td></tr><tr><td>train/train_loss</td><td>0.268</td></tr><tr><td>train/train_runtime</td><td>451.0846</td></tr><tr><td>train/train_samples_per_second</td><td>11.634</td></tr><tr><td>train/train_steps_per_second</td><td>1.454</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: uakeqy2o with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 2.386135927313411e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 887.5577347480429\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/uakeqy2o' target=\"_blank\">northern-sweep-6</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              "<style>\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▁▄█</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▄█</td></tr><tr><td>eval/loss</td><td>▇█▄▁</td></tr><tr><td>eval/runtime</td><td>▁▆▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▂▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▄▄▅▅▆▆█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▁▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.91608</td></tr><tr><td>eval/best_accuracy</td><td>0.91608</td></tr><tr><td>eval/loss</td><td>0.33027</td></tr><tr><td>eval/runtime</td><td>46.0755</td></tr><tr><td>eval/samples_per_second</td><td>61.551</td></tr><tr><td>eval/steps_per_second</td><td>7.705</td></tr><tr><td>train/epoch</td><td>0.09</td></tr><tr><td>train/global_step</td><td>887</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1162</td></tr><tr><td>train/total_flos</td><td>939988660862976.0</td></tr><tr><td>train/train_loss</td><td>0.18107</td></tr><tr><td>train/train_runtime</td><td>540.5231</td></tr><tr><td>train/train_samples_per_second</td><td>13.128</td></tr><tr><td>train/train_steps_per_second</td><td>1.641</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: vp9ymyaw with config:\n",
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
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            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▇█</td></tr><tr><td>eval/best_accuracy</td><td>▁▇█</td></tr><tr><td>eval/loss</td><td>▁▇█</td></tr><tr><td>eval/runtime</td><td>▁██</td></tr><tr><td>eval/samples_per_second</td><td>█▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▁▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▆▆██</td></tr><tr><td>train/global_step</td><td>▁▃▃▆▆██</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄</td></tr><tr><td>train/loss</td><td>█▄▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.85755</td></tr><tr><td>eval/best_accuracy</td><td>0.85755</td></tr><tr><td>eval/loss</td><td>0.58951</td></tr><tr><td>eval/runtime</td><td>46.0542</td></tr><tr><td>eval/samples_per_second</td><td>61.58</td></tr><tr><td>eval/steps_per_second</td><td>7.708</td></tr><tr><td>train/epoch</td><td>0.06</td></tr><tr><td>train/global_step</td><td>594</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.2086</td></tr></table><br/></div></div>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>█▁▇▇</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▁▁</td></tr><tr><td>eval/loss</td><td>▁█▂▃</td></tr><tr><td>eval/runtime</td><td>▁▇█▇</td></tr><tr><td>eval/samples_per_second</td><td>█▂▁▂</td></tr><tr><td>eval/steps_per_second</td><td>█▂▁▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.91185</td></tr><tr><td>eval/best_accuracy</td><td>0.91714</td></tr><tr><td>eval/loss</td><td>0.30897</td></tr><tr><td>eval/runtime</td><td>46.0962</td></tr><tr><td>eval/samples_per_second</td><td>61.524</td></tr><tr><td>eval/steps_per_second</td><td>7.701</td></tr><tr><td>train/epoch</td><td>0.1</td></tr><tr><td>train/global_step</td><td>971</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.0966</td></tr><tr><td>train/total_flos</td><td>1230357198716928.0</td></tr><tr><td>train/train_loss</td><td>0.17252</td></tr><tr><td>train/train_runtime</td><td>575.4106</td></tr><tr><td>train/train_samples_per_second</td><td>13.5</td></tr><tr><td>train/train_steps_per_second</td><td>1.687</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 3.407783628903858e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 887.417779049362\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▂▅█</td></tr><tr><td>eval/best_accuracy</td><td>▁▂▅█</td></tr><tr><td>eval/loss</td><td>▅█▃▁</td></tr><tr><td>eval/runtime</td><td>▁▂▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▇▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▇▃▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▄▄▅▅▆▆█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▁▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.88963</td></tr><tr><td>eval/best_accuracy</td><td>0.88963</td></tr><tr><td>eval/loss</td><td>0.47849</td></tr><tr><td>eval/runtime</td><td>46.8442</td></tr><tr><td>eval/samples_per_second</td><td>60.541</td></tr><tr><td>eval/steps_per_second</td><td>7.578</td></tr><tr><td>train/epoch</td><td>0.09</td></tr><tr><td>train/global_step</td><td>887</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1089</td></tr><tr><td>train/total_flos</td><td>939988660862976.0</td></tr><tr><td>train/train_loss</td><td>0.17848</td></tr><tr><td>train/train_runtime</td><td>545.3395</td></tr><tr><td>train/train_samples_per_second</td><td>13.012</td></tr><tr><td>train/train_steps_per_second</td><td>1.627</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">treasured-sweep-9</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/snct1auc' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/snct1auc</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: wxqoee18 with config:\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 747.8252978281836\n"
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              "<style>\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▄█▁▇</td></tr><tr><td>eval/best_accuracy</td><td>▁███</td></tr><tr><td>eval/loss</td><td>▃▁█▂</td></tr><tr><td>eval/runtime</td><td>▁▇▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▂▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▂▃▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▁▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.86777</td></tr><tr><td>eval/best_accuracy</td><td>0.88152</td></tr><tr><td>eval/loss</td><td>0.52961</td></tr><tr><td>eval/runtime</td><td>46.9711</td></tr><tr><td>eval/samples_per_second</td><td>60.378</td></tr><tr><td>eval/steps_per_second</td><td>7.558</td></tr><tr><td>train/epoch</td><td>0.07</td></tr><tr><td>train/global_step</td><td>747</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.112</td></tr><tr><td>train/total_flos</td><td>791625174368256.0</td></tr><tr><td>train/train_loss</td><td>0.18493</td></tr><tr><td>train/train_runtime</td><td>494.9315</td></tr><tr><td>train/train_samples_per_second</td><td>12.074</td></tr><tr><td>train/train_steps_per_second</td><td>1.509</td></tr></table><br/></div></div>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>█▁▂▄</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▁▁</td></tr><tr><td>eval/loss</td><td>▁█▅▄</td></tr><tr><td>eval/runtime</td><td>▆█▂▁</td></tr><tr><td>eval/samples_per_second</td><td>▃▁▇█</td></tr><tr><td>eval/steps_per_second</td><td>▃▁▇█</td></tr><tr><td>train/epoch</td><td>▁▃▃▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87447</td></tr><tr><td>eval/best_accuracy</td><td>0.9256</td></tr><tr><td>eval/loss</td><td>0.48822</td></tr><tr><td>eval/runtime</td><td>46.4193</td></tr><tr><td>eval/samples_per_second</td><td>61.095</td></tr><tr><td>eval/steps_per_second</td><td>7.648</td></tr><tr><td>train/epoch</td><td>0.08</td></tr><tr><td>train/global_step</td><td>838</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.0957</td></tr><tr><td>train/total_flos</td><td>888061440589824.0</td></tr><tr><td>train/train_loss</td><td>0.18535</td></tr><tr><td>train/train_runtime</td><td>528.4803</td></tr><tr><td>train/train_samples_per_second</td><td>12.685</td></tr><tr><td>train/train_steps_per_second</td><td>1.586</td></tr></table><br/></div></div>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>█▁▅▅</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▁▁</td></tr><tr><td>eval/loss</td><td>▁█▄▂</td></tr><tr><td>eval/runtime</td><td>▁▆██</td></tr><tr><td>eval/samples_per_second</td><td>█▃▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▁▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▃▁▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.88858</td></tr><tr><td>eval/best_accuracy</td><td>0.90268</td></tr><tr><td>eval/loss</td><td>0.45724</td></tr><tr><td>eval/runtime</td><td>46.27</td></tr><tr><td>eval/samples_per_second</td><td>61.292</td></tr><tr><td>eval/steps_per_second</td><td>7.672</td></tr><tr><td>train/epoch</td><td>0.1</td></tr><tr><td>train/global_step</td><td>989</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1143</td></tr><tr><td>train/total_flos</td><td>1048082058166272.0</td></tr><tr><td>train/train_loss</td><td>0.1691</td></tr><tr><td>train/train_runtime</td><td>584.3615</td></tr><tr><td>train/train_samples_per_second</td><td>13.54</td></tr><tr><td>train/train_steps_per_second</td><td>1.692</td></tr></table><br/></div></div>"
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            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
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            "{'eval_loss': 0.3944683074951172, 'eval_accuracy': 0.8966854724964739, 'eval_best_accuracy': 0.8966854724964739, 'eval_runtime': 46.2602, 'eval_samples_per_second': 61.305, 'eval_steps_per_second': 7.674, 'epoch': 0.08}\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▆▁▇▇█</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▄▄█</td></tr><tr><td>eval/loss</td><td>▄█▂▂▁</td></tr><tr><td>eval/runtime</td><td>▁▅█▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▄▁▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▁▂▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▄▄▅▅▇▇███</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇███</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂▁</td></tr><tr><td>train/loss</td><td>█▄▂▂▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.89669</td></tr><tr><td>eval/best_accuracy</td><td>0.89669</td></tr><tr><td>eval/loss</td><td>0.39447</td></tr><tr><td>eval/runtime</td><td>46.2602</td></tr><tr><td>eval/samples_per_second</td><td>61.305</td></tr><tr><td>eval/steps_per_second</td><td>7.674</td></tr><tr><td>train/epoch</td><td>0.08</td></tr><tr><td>train/global_step</td><td>840</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1172</td></tr><tr><td>train/total_flos</td><td>890180918968320.0</td></tr><tr><td>train/train_loss</td><td>0.19091</td></tr><tr><td>train/train_runtime</td><td>571.7473</td></tr><tr><td>train/train_samples_per_second</td><td>11.753</td></tr><tr><td>train/train_steps_per_second</td><td>1.469</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">snowy-sweep-13</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/vcs6pri7' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/vcs6pri7</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: qhmfwd1q with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 7.727778638808646e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 660.5416419065895\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/qhmfwd1q' target=\"_blank\">bright-sweep-14</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>█▆▃▄▁</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▁▁▁</td></tr><tr><td>eval/loss</td><td>▅█▂▁▇</td></tr><tr><td>eval/runtime</td><td>▁▆███</td></tr><tr><td>eval/samples_per_second</td><td>█▃▁▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▁▁▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▆▆███</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇███</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂▁</td></tr><tr><td>train/loss</td><td>█▄▂▁▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.86777</td></tr><tr><td>eval/best_accuracy</td><td>0.90797</td></tr><tr><td>eval/loss</td><td>0.46213</td></tr><tr><td>eval/runtime</td><td>46.1823</td></tr><tr><td>eval/samples_per_second</td><td>61.409</td></tr><tr><td>eval/steps_per_second</td><td>7.687</td></tr><tr><td>train/epoch</td><td>0.07</td></tr><tr><td>train/global_step</td><td>660</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1106</td></tr><tr><td>train/total_flos</td><td>699427864903680.0</td></tr><tr><td>train/train_loss</td><td>0.21348</td></tr><tr><td>train/train_runtime</td><td>501.2614</td></tr><tr><td>train/train_samples_per_second</td><td>10.533</td></tr><tr><td>train/train_steps_per_second</td><td>1.317</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Job received.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: rqrzfrjx with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.989448327758108e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 679.2247567516173\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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              "<style>\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▇▃█▁</td></tr><tr><td>eval/best_accuracy</td><td>▁▁██</td></tr><tr><td>eval/loss</td><td>▆█▁▇</td></tr><tr><td>eval/runtime</td><td>▁▆▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▂▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▆▆█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▂▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87694</td></tr><tr><td>eval/best_accuracy</td><td>0.90903</td></tr><tr><td>eval/loss</td><td>0.52678</td></tr><tr><td>eval/runtime</td><td>46.2004</td></tr><tr><td>eval/samples_per_second</td><td>61.385</td></tr><tr><td>eval/steps_per_second</td><td>7.684</td></tr><tr><td>train/epoch</td><td>0.07</td></tr><tr><td>train/global_step</td><td>679</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1691</td></tr><tr><td>train/total_flos</td><td>719562909499392.0</td></tr><tr><td>train/train_loss</td><td>0.20238</td></tr><tr><td>train/train_runtime</td><td>460.6949</td></tr><tr><td>train/train_samples_per_second</td><td>11.791</td></tr><tr><td>train/train_steps_per_second</td><td>1.474</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: spdd5xes with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.869518460980536e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 993.0827094078484\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
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            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▂█▁</td></tr><tr><td>eval/best_accuracy</td><td>▁██</td></tr><tr><td>eval/loss</td><td>█▁▆</td></tr><tr><td>eval/runtime</td><td>▁▄█</td></tr><tr><td>eval/samples_per_second</td><td>█▅▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▆▆██</td></tr><tr><td>train/global_step</td><td>▁▃▃▆▆██</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄</td></tr><tr><td>train/loss</td><td>█▃▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.82546</td></tr><tr><td>eval/best_accuracy</td><td>0.8586</td></tr><tr><td>eval/loss</td><td>0.7715</td></tr><tr><td>eval/runtime</td><td>46.1595</td></tr><tr><td>eval/samples_per_second</td><td>61.439</td></tr><tr><td>eval/steps_per_second</td><td>7.691</td></tr><tr><td>train/epoch</td><td>0.06</td></tr><tr><td>train/global_step</td><td>597</td></tr><tr><td>train/learning_rate</td><td>4e-05</td></tr><tr><td>train/loss</td><td>0.1875</td></tr></table><br/></div></div>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▄█▅</td></tr><tr><td>eval/best_accuracy</td><td>▁▄██</td></tr><tr><td>eval/loss</td><td>▅▁▄█</td></tr><tr><td>eval/runtime</td><td>▁▅██</td></tr><tr><td>eval/samples_per_second</td><td>█▄▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▂▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▅▅▆▆▆▆█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▁</td></tr><tr><td>train/loss</td><td>█▄▃▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.86107</td></tr><tr><td>eval/best_accuracy</td><td>0.88681</td></tr><tr><td>eval/loss</td><td>0.6174</td></tr><tr><td>eval/runtime</td><td>46.119</td></tr><tr><td>eval/samples_per_second</td><td>61.493</td></tr><tr><td>eval/steps_per_second</td><td>7.697</td></tr><tr><td>train/epoch</td><td>0.04</td></tr><tr><td>train/global_step</td><td>422</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1161</td></tr><tr><td>train/total_flos</td><td>611469512196096.0</td></tr><tr><td>train/train_loss</td><td>0.21705</td></tr><tr><td>train/train_runtime</td><td>360.4631</td></tr><tr><td>train/train_samples_per_second</td><td>9.366</td></tr><tr><td>train/train_steps_per_second</td><td>1.171</td></tr></table><br/></div></div>"
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            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>█▁▇▅</td></tr><tr><td>eval/best_accuracy</td><td>▁▁▁▁</td></tr><tr><td>eval/loss</td><td>▁█▂▄</td></tr><tr><td>eval/runtime</td><td>▁▅▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▄▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▃▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87377</td></tr><tr><td>eval/best_accuracy</td><td>0.89915</td></tr><tr><td>eval/loss</td><td>0.61163</td></tr><tr><td>eval/runtime</td><td>46.2106</td></tr><tr><td>eval/samples_per_second</td><td>61.371</td></tr><tr><td>eval/steps_per_second</td><td>7.682</td></tr><tr><td>train/epoch</td><td>0.08</td></tr><tr><td>train/global_step</td><td>826</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1018</td></tr><tr><td>train/total_flos</td><td>875344570318848.0</td></tr><tr><td>train/train_loss</td><td>0.19162</td></tr><tr><td>train/train_runtime</td><td>518.2385</td></tr><tr><td>train/train_samples_per_second</td><td>12.751</td></tr><tr><td>train/train_steps_per_second</td><td>1.594</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 0r1gs9vg with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 8.70667439572547e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 721.0411922265966\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/0r1gs9vg' target=\"_blank\">solar-sweep-19</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              " View run <strong style=\"color:#cdcd00\">solar-sweep-19</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/0r1gs9vg' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/0r1gs9vg</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁█▅█</td></tr><tr><td>eval/best_accuracy</td><td>▁███</td></tr><tr><td>eval/loss</td><td>█▆▁▇</td></tr><tr><td>eval/runtime</td><td>▁▅▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▄▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▂▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▄▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.88293</td></tr><tr><td>eval/best_accuracy</td><td>0.88329</td></tr><tr><td>eval/loss</td><td>0.57723</td></tr><tr><td>eval/runtime</td><td>46.1774</td></tr><tr><td>eval/samples_per_second</td><td>61.415</td></tr><tr><td>eval/steps_per_second</td><td>7.688</td></tr><tr><td>train/epoch</td><td>0.07</td></tr><tr><td>train/global_step</td><td>721</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1327</td></tr><tr><td>train/total_flos</td><td>764071955447808.0</td></tr><tr><td>train/train_loss</td><td>0.23168</td></tr><tr><td>train/train_runtime</td><td>477.5812</td></tr><tr><td>train/train_samples_per_second</td><td>12.078</td></tr><tr><td>train/train_steps_per_second</td><td>1.51</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">solar-sweep-19</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/0r1gs9vg' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/0r1gs9vg</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.349071258062156e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 461.3966963980219\n"
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              "Run data is saved locally in <code>/home/ed/Downloads/OO-Fine-Tune/wandb/run-20230908_201440-4swdhwfv</code>"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv' target=\"_blank\">vague-sweep-20</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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              " View run at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv</a>"
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          "text": [
            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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          "data": {
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">vague-sweep-20</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'learning_rate' was locked by 'sweep' (ignored update).\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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          "text": [
            "{'eval_loss': 0.5191717743873596, 'eval_accuracy': 0.8794076163610719, 'eval_best_accuracy': 0.8794076163610719, 'eval_runtime': 45.9673, 'eval_samples_per_second': 61.696, 'eval_steps_per_second': 7.723, 'epoch': 0.02}\n",
            "{'loss': 0.1677, 'learning_rate': 3.4619719849667154e-05, 'epoch': 0.03}\n"
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          "text": [
            "{'eval_loss': 0.6440331339836121, 'eval_accuracy': 0.8519040902679831, 'eval_best_accuracy': 0.8794076163610719, 'eval_runtime': 46.0246, 'eval_samples_per_second': 61.619, 'eval_steps_per_second': 7.713, 'epoch': 0.03}\n",
            "{'loss': 0.1117, 'learning_rate': 9.246105956398726e-06, 'epoch': 0.04}\n"
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            "{'eval_loss': 0.5534007549285889, 'eval_accuracy': 0.8758815232722144, 'eval_best_accuracy': 0.8794076163610719, 'eval_runtime': 46.023, 'eval_samples_per_second': 61.621, 'eval_steps_per_second': 7.714, 'epoch': 0.04}\n",
            "{'train_runtime': 375.1829, 'train_samples_per_second': 9.83, 'train_steps_per_second': 1.229, 'train_loss': 0.23556971808576274, 'epoch': 0.05}\n"
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            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▅█▁▇</td></tr><tr><td>eval/best_accuracy</td><td>▁███</td></tr><tr><td>eval/loss</td><td>▁▅█▆</td></tr><tr><td>eval/runtime</td><td>▁▇██</td></tr><tr><td>eval/samples_per_second</td><td>█▂▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▂▁▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▁</td></tr><tr><td>train/loss</td><td>█▅▃▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.87588</td></tr><tr><td>eval/best_accuracy</td><td>0.87941</td></tr><tr><td>eval/loss</td><td>0.5534</td></tr><tr><td>eval/runtime</td><td>46.023</td></tr><tr><td>eval/samples_per_second</td><td>61.621</td></tr><tr><td>eval/steps_per_second</td><td>7.714</td></tr><tr><td>train/epoch</td><td>0.05</td></tr><tr><td>train/global_step</td><td>461</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1117</td></tr><tr><td>train/total_flos</td><td>488539766243328.0</td></tr><tr><td>train/train_loss</td><td>0.23557</td></tr><tr><td>train/train_runtime</td><td>375.1829</td></tr><tr><td>train/train_samples_per_second</td><td>9.83</td></tr><tr><td>train/train_steps_per_second</td><td>1.229</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">vague-sweep-20</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/4swdhwfv</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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          "data": {
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: l8lh0bbd with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 7.736554828849878e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 280.2188480900576\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▃█▇▆</td></tr><tr><td>eval/best_accuracy</td><td>▁▃███</td></tr><tr><td>eval/loss</td><td>▇█▁▂▅</td></tr><tr><td>eval/runtime</td><td>▁▆▇██</td></tr><tr><td>eval/samples_per_second</td><td>█▃▂▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▂▁▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▃▃▆▆▆▆███</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇███</td></tr><tr><td>train/learning_rate</td><td>▂█▆▄▂▁</td></tr><tr><td>train/loss</td><td>█▅▃▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.86248</td></tr><tr><td>eval/best_accuracy</td><td>0.88223</td></tr><tr><td>eval/loss</td><td>0.50369</td></tr><tr><td>eval/runtime</td><td>46.0837</td></tr><tr><td>eval/samples_per_second</td><td>61.54</td></tr><tr><td>eval/steps_per_second</td><td>7.703</td></tr><tr><td>train/epoch</td><td>0.03</td></tr><tr><td>train/global_step</td><td>280</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.113</td></tr><tr><td>train/total_flos</td><td>296726972989440.0</td></tr><tr><td>train/train_loss</td><td>0.25694</td></tr><tr><td>train/train_runtime</td><td>351.6229</td></tr><tr><td>train/train_samples_per_second</td><td>6.37</td></tr><tr><td>train/train_steps_per_second</td><td>0.796</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: 3c8qj9nb with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.903855997501048e-05\n",
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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            "{'train_runtime': 273.9016, 'train_samples_per_second': 5.958, 'train_steps_per_second': 0.745, 'train_loss': 0.32766352681552663, 'epoch': 0.02}\n"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▇██</td></tr><tr><td>eval/best_accuracy</td><td>▁▇██</td></tr><tr><td>eval/loss</td><td>█▂▁▁</td></tr><tr><td>eval/runtime</td><td>▁▆▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▂▁</td></tr><tr><td>train/epoch</td><td>▁▁▁▅▅▅▅███</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▃▁</td></tr><tr><td>train/loss</td><td>█▆▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.88117</td></tr><tr><td>eval/best_accuracy</td><td>0.88117</td></tr><tr><td>eval/loss</td><td>0.45707</td></tr><tr><td>eval/runtime</td><td>45.9873</td></tr><tr><td>eval/samples_per_second</td><td>61.669</td></tr><tr><td>eval/steps_per_second</td><td>7.72</td></tr><tr><td>train/epoch</td><td>0.02</td></tr><tr><td>train/global_step</td><td>204</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.2466</td></tr><tr><td>train/total_flos</td><td>216186794606592.0</td></tr><tr><td>train/train_loss</td><td>0.32766</td></tr><tr><td>train/train_runtime</td><td>273.9016</td></tr><tr><td>train/train_samples_per_second</td><td>5.958</td></tr><tr><td>train/train_steps_per_second</td><td>0.745</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: hoowzbn8 with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 8.685473792488989e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 25.866142800739233\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁█▁</td></tr><tr><td>eval/best_accuracy</td><td>▁██</td></tr><tr><td>eval/loss</td><td>█▁▅</td></tr><tr><td>eval/runtime</td><td>▁▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▁</td></tr><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▆▆█</td></tr><tr><td>train/learning_rate</td><td>▄█▆▃▁</td></tr><tr><td>train/loss</td><td>█▄▅▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.30853</td></tr><tr><td>eval/best_accuracy</td><td>0.67313</td></tr><tr><td>eval/loss</td><td>1.10786</td></tr><tr><td>eval/runtime</td><td>45.8249</td></tr><tr><td>eval/samples_per_second</td><td>61.888</td></tr><tr><td>eval/steps_per_second</td><td>7.747</td></tr><tr><td>train/epoch</td><td>0.0</td></tr><tr><td>train/global_step</td><td>20</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.5212</td></tr></table><br/></div></div>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁█▇</td></tr><tr><td>eval/best_accuracy</td><td>▁██</td></tr><tr><td>eval/loss</td><td>▁▃█</td></tr><tr><td>eval/runtime</td><td>▁▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▁</td></tr><tr><td>train/epoch</td><td>▁▁▁█████</td></tr><tr><td>train/global_step</td><td>▁▃▃▄▄▆▆█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▃▁</td></tr><tr><td>train/loss</td><td>█▆▃▃▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.84485</td></tr><tr><td>eval/best_accuracy</td><td>0.84838</td></tr><tr><td>eval/loss</td><td>0.45112</td></tr><tr><td>eval/runtime</td><td>45.8879</td></tr><tr><td>eval/samples_per_second</td><td>61.803</td></tr><tr><td>eval/steps_per_second</td><td>7.736</td></tr><tr><td>train/epoch</td><td>0.01</td></tr><tr><td>train/global_step</td><td>140</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1572</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">eternal-sweep-24</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ewkzljhc' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ewkzljhc</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 9.121983141754006e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 321.508964053983\n"
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              " View sweep at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              " View sweep at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▇██</td></tr><tr><td>eval/best_accuracy</td><td>▁▇██</td></tr><tr><td>eval/loss</td><td>█▃▁▄</td></tr><tr><td>eval/runtime</td><td>▁▆▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▂▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▃▃▆▆███</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▃▁</td></tr><tr><td>train/loss</td><td>█▅▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.89528</td></tr><tr><td>eval/best_accuracy</td><td>0.89528</td></tr><tr><td>eval/loss</td><td>0.52385</td></tr><tr><td>eval/runtime</td><td>46.0306</td></tr><tr><td>eval/samples_per_second</td><td>61.611</td></tr><tr><td>eval/steps_per_second</td><td>7.712</td></tr><tr><td>train/epoch</td><td>0.03</td></tr><tr><td>train/global_step</td><td>321</td></tr><tr><td>train/learning_rate</td><td>1e-05</td></tr><tr><td>train/loss</td><td>0.1478</td></tr><tr><td>train/total_flos</td><td>340176279748608.0</td></tr><tr><td>train/train_loss</td><td>0.25991</td></tr><tr><td>train/train_runtime</td><td>320.6873</td></tr><tr><td>train/train_samples_per_second</td><td>8.008</td></tr><tr><td>train/train_steps_per_second</td><td>1.001</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: caq8srgk with config:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tlearning_rate: 7.441596699203298e-05\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 450.4759697452623\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              " View run <strong style=\"color:#cdcd00\">electric-sweep-26</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/caq8srgk' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/caq8srgk</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▆█▇▇</td></tr><tr><td>eval/best_accuracy</td><td>▁▆███</td></tr><tr><td>eval/loss</td><td>▇▅▂█▁</td></tr><tr><td>eval/runtime</td><td>▁▆▇██</td></tr><tr><td>eval/samples_per_second</td><td>█▃▂▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▂▁▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▅▅▆▆█████</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇███</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂▁</td></tr><tr><td>train/loss</td><td>█▄▂▁▁▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.88364</td></tr><tr><td>eval/best_accuracy</td><td>0.88963</td></tr><tr><td>eval/loss</td><td>0.48029</td></tr><tr><td>eval/runtime</td><td>46.0632</td></tr><tr><td>eval/samples_per_second</td><td>61.568</td></tr><tr><td>eval/steps_per_second</td><td>7.707</td></tr><tr><td>train/epoch</td><td>0.04</td></tr><tr><td>train/global_step</td><td>450</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1823</td></tr><tr><td>train/total_flos</td><td>476882635161600.0</td></tr><tr><td>train/train_loss</td><td>0.23583</td></tr><tr><td>train/train_runtime</td><td>418.2927</td></tr><tr><td>train/train_samples_per_second</td><td>8.606</td></tr><tr><td>train/train_steps_per_second</td><td>1.076</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: mw0zwfet with config:\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 572.022208801758\n"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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              " View run <strong style=\"color:#cdcd00\">elated-sweep-27</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/mw0zwfet' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/mw0zwfet</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁█▆</td></tr><tr><td>eval/best_accuracy</td><td>▁██</td></tr><tr><td>eval/loss</td><td>█▁▂</td></tr><tr><td>eval/runtime</td><td>▁▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▆▆██</td></tr><tr><td>train/global_step</td><td>▁▃▃▆▆██</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄</td></tr><tr><td>train/loss</td><td>█▄▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.85578</td></tr><tr><td>eval/best_accuracy</td><td>0.8646</td></tr><tr><td>eval/loss</td><td>0.65743</td></tr><tr><td>eval/runtime</td><td>46.0389</td></tr><tr><td>eval/samples_per_second</td><td>61.6</td></tr><tr><td>eval/steps_per_second</td><td>7.711</td></tr><tr><td>train/epoch</td><td>0.03</td></tr><tr><td>train/global_step</td><td>345</td></tr><tr><td>train/learning_rate</td><td>2e-05</td></tr><tr><td>train/loss</td><td>0.136</td></tr></table><br/></div></div>"
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁█▆▆</td></tr><tr><td>eval/best_accuracy</td><td>▁███</td></tr><tr><td>eval/loss</td><td>▅▁▅█</td></tr><tr><td>eval/runtime</td><td>▁▅▇█</td></tr><tr><td>eval/samples_per_second</td><td>█▄▂▁</td></tr><tr><td>eval/steps_per_second</td><td>█▄▂▁</td></tr><tr><td>train/epoch</td><td>▁▃▃▃▃▆▆███</td></tr><tr><td>train/global_step</td><td>▁▂▂▄▄▅▅▇▇█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▃▁</td></tr><tr><td>train/loss</td><td>█▅▂▂▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.88822</td></tr><tr><td>eval/best_accuracy</td><td>0.90409</td></tr><tr><td>eval/loss</td><td>0.47407</td></tr><tr><td>eval/runtime</td><td>46.1015</td></tr><tr><td>eval/samples_per_second</td><td>61.516</td></tr><tr><td>eval/steps_per_second</td><td>7.7</td></tr><tr><td>train/epoch</td><td>0.03</td></tr><tr><td>train/global_step</td><td>331</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1202</td></tr><tr><td>train/total_flos</td><td>397402195968000.0</td></tr><tr><td>train/train_loss</td><td>0.23534</td></tr><tr><td>train/train_runtime</td><td>324.6173</td></tr><tr><td>train/train_samples_per_second</td><td>8.157</td></tr><tr><td>train/train_steps_per_second</td><td>1.02</td></tr></table><br/></div></div>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \tmax_steps: 133.62973336170617\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/5vpk8z6l' target=\"_blank\">upbeat-sweep-29</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
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            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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              "<style>\n",
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              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁██</td></tr><tr><td>eval/best_accuracy</td><td>▁██</td></tr><tr><td>eval/loss</td><td>█▃▁</td></tr><tr><td>eval/runtime</td><td>▁▆█</td></tr><tr><td>eval/samples_per_second</td><td>█▃▁</td></tr><tr><td>eval/steps_per_second</td><td>█▃▁</td></tr><tr><td>train/epoch</td><td>▁▁▁████</td></tr><tr><td>train/global_step</td><td>▁▃▃▆▆██</td></tr><tr><td>train/learning_rate</td><td>▁█▆▃</td></tr><tr><td>train/loss</td><td>█▅▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.86495</td></tr><tr><td>eval/best_accuracy</td><td>0.86495</td></tr><tr><td>eval/loss</td><td>0.32055</td></tr><tr><td>eval/runtime</td><td>45.9266</td></tr><tr><td>eval/samples_per_second</td><td>61.751</td></tr><tr><td>eval/steps_per_second</td><td>7.73</td></tr><tr><td>train/epoch</td><td>0.01</td></tr><tr><td>train/global_step</td><td>81</td></tr><tr><td>train/learning_rate</td><td>2e-05</td></tr><tr><td>train/loss</td><td>0.3497</td></tr></table><br/></div></div>"
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              "<IPython.core.display.HTML object>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: ghmc8pxw with config:\n",
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              " View sweep at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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              " View run at <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw</a>"
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          "text": [
            "Trying to set _wandb in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set assignments in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Trying to set metric in the hyperparameter search but there is no corresponding field in `TrainingArguments`.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
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          "data": {
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              " View run <strong style=\"color:#cdcd00\">playful-sweep-30</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "Find logs at: <code>./wandb/run-20230908_210614-ghmc8pxw/logs</code>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Ignored wandb.init() arg project when running a sweep.\n"
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              "Syncing run <strong><a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw' target=\"_blank\">playful-sweep-30</a></strong> to <a href='https://wandb.ai/eschwartz/OO_Fine_Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>Sweep page: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/sweeps/1wpa6h8x</a>"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'learning_rate' was locked by 'sweep' (ignored update).\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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            "{'loss': 0.7452, 'learning_rate': 5.792777849285827e-07, 'epoch': 0.0}\n",
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            "{'eval_loss': 0.4554911255836487, 'eval_accuracy': 0.8194640338504936, 'eval_best_accuracy': 0.8194640338504936, 'eval_runtime': 45.7372, 'eval_samples_per_second': 62.006, 'eval_steps_per_second': 7.762, 'epoch': 0.01}\n",
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            "{'eval_loss': 0.6618301272392273, 'eval_accuracy': 0.8423836389280677, 'eval_best_accuracy': 0.8423836389280677, 'eval_runtime': 46.016, 'eval_samples_per_second': 61.631, 'eval_steps_per_second': 7.715, 'epoch': 0.02}\n",
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            "{'eval_loss': 0.7814058065414429, 'eval_accuracy': 0.8589562764456982, 'eval_best_accuracy': 0.8589562764456982, 'eval_runtime': 46.031, 'eval_samples_per_second': 61.611, 'eval_steps_per_second': 7.712, 'epoch': 0.03}\n",
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          "data": {
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:red\">(failed 1).</strong> Press Control-C to abort syncing."
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            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▅█</td></tr><tr><td>eval/best_accuracy</td><td>▁▅█</td></tr><tr><td>eval/loss</td><td>▁▅█</td></tr><tr><td>eval/runtime</td><td>▁██</td></tr><tr><td>eval/samples_per_second</td><td>█▁▁</td></tr><tr><td>eval/steps_per_second</td><td>█▁▁</td></tr><tr><td>train/epoch</td><td>▁▂▂▄▄▅▅█</td></tr><tr><td>train/global_step</td><td>▁▃▃▄▄▆▆█</td></tr><tr><td>train/learning_rate</td><td>▁█▆▄▂</td></tr><tr><td>train/loss</td><td>█▅▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.85896</td></tr><tr><td>eval/best_accuracy</td><td>0.85896</td></tr><tr><td>eval/loss</td><td>0.78141</td></tr><tr><td>eval/runtime</td><td>46.031</td></tr><tr><td>eval/samples_per_second</td><td>61.611</td></tr><tr><td>eval/steps_per_second</td><td>7.712</td></tr><tr><td>train/epoch</td><td>0.05</td></tr><tr><td>train/global_step</td><td>464</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.1676</td></tr></table><br/></div></div>"
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            "text/html": [
              " View run <strong style=\"color:#cdcd00\">playful-sweep-30</strong> at: <a href='https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw' target=\"_blank\">https://wandb.ai/eschwartz/OO_Fine_Tune/runs/ghmc8pxw</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "Find logs at: <code>./wandb/run-20230908_210622-ghmc8pxw/logs</code>"
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              "<IPython.core.display.HTML object>"
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      ],
      "source": [
        "from ray import tune\n",
        "import ray\n",
        "import ray.tune.schedulers\n",
        "import ray.tune.search.hyperopt\n",
        "import ray.tune.progress_reporter\n",
        "\n",
        "def wandb_hp_space(trial):\n",
        "    return {\n",
        "        \"method\": \"bayes\",\n",
        "        # why do we need this and the metric arg to hyperparameter_search?\n",
        "        \"metric\": {\"name\": \"eval/accuracy\", \"goal\": \"maximize\"},\n",
        "        \"parameters\": {\n",
        "            \"learning_rate\": {\"distribution\": \"log_uniform_values\" if False else \"uniform\", \"min\": 1e-6, \"max\": 1e-4},\n",
        "            #\"per_device_train_batch_size\": {\"values\": [4, 8]},\n",
        "            \"max_steps\": {\"distribution\": \"uniform\", \"min\": 1, \"max\": 1000},\n",
        "            #\"num_train_epochs\": {\"distribution\": \"uniform\", \"min\": 0.1, \"max\": 4},\n",
        "            #\"lr_scheduler_type\": {\"values\": [\"linear\", \"cosine\"]},\n",
        "            #\"warmup_ratio\": {\"distribution\": \"uniform\", \"min\": 0.0, \"max\": 0.25},\n",
        "            #\"fp16\": {\"values\": [True, False]},\n",
        "        },\n",
        "        \"early_terminate\": {\n",
        "            \"type\": \"hyperband\",\n",
        "            \"min_iter\": 3,\n",
        "            \"eta\": 2\n",
        "        }\n",
        "    }\n",
        "\n",
        "def ray_hp_space(trial):\n",
        "    return {\n",
        "            \"learning_rate\": ray.tune.loguniform(1e-6, 1e-4),\n",
        "            \"num_train_epochs\": ray.tune.uniform(0.1, 4),\n",
        "            # choices don't work with PBT\n",
        "            #\"lr_scheduler_type\": ray.tune.choice([\"linear\", \"cosine\"]),\n",
        "            \"warmup_ratio\": ray.tune.uniform(0.0, 0.25),\n",
        "            #\"fp16\": ray.tune.choice([True, False]),\n",
        "        }\n",
        "\n",
        "if hyperopt:\n",
        "    best = trainer.hyperparameter_search(\n",
        "        direction=\"maximize\", \n",
        "        backend=\"wandb\",\n",
        "        hp_space=wandb_hp_space,\n",
        "        #backend=\"ray\",\n",
        "        #hp_space=ray_hp_space,\n",
        "        progress_reporter=ray.tune.progress_reporter.JupyterNotebookReporter(),\n",
        "        metric=\"eval/accuracy\",\n",
        "        compute_objective=lambda d: d['eval_accuracy'],\n",
        "        mode=\"max\",\n",
        "        n_trials=30, # number of trials\n",
        "        #search_alg=ray.tune.search.hyperopt.HyperOptSearch(),\n",
        "        #scheduler=ray.tune.schedulers.PopulationBasedTraining(hyperparam_mutations=ray_hp_space(None)),\n",
        "        keep_checkpoints_num=1,\n",
        ")\n",
        "else:\n",
        "    trainer.train(resume_from_checkpoint=resume)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "#trainer.evaluate()\n",
        "#trainer.evaluate(ds_dev)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "BestRun(run_id='uakeqy2o', objective=0.9160789844851904, hyperparameters={'learning_rate': 2.386135927313411e-05, 'max_steps': 887.5577347480429, 'assignments': {}, 'metric': 'eval/accuracy'}, run_summary=None)\n"
          ]
        },
        {
          "name": "stderr",
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          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at huggingface/CodeBERTa-small-v1 and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'learning_rate' was locked by 'sweep' (ignored update).\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Config item 'max_steps' was locked by 'sweep' (ignored update).\n"
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            "{'loss': 0.7452, 'learning_rate': 5.302524282918692e-07, 'epoch': 0.0}\n",
            "{'loss': 0.3788, 'learning_rate': 2.2422283922793875e-05, 'epoch': 0.02}\n"
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          "text": [
            "{'eval_loss': 0.6187645792961121, 'eval_accuracy': 0.8469675599435825, 'eval_best_accuracy': 0.8469675599435825, 'eval_runtime': 44.336, 'eval_samples_per_second': 63.966, 'eval_steps_per_second': 8.007, 'epoch': 0.02}\n",
            "{'loss': 0.1456, 'learning_rate': 1.669097516003374e-05, 'epoch': 0.04}\n"
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            "text/plain": [
              "  0%|          | 0/355 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.6571812629699707, 'eval_accuracy': 0.8519040902679831, 'eval_best_accuracy': 0.8519040902679831, 'eval_runtime': 45.2113, 'eval_samples_per_second': 62.728, 'eval_steps_per_second': 7.852, 'epoch': 0.04}\n",
            "{'loss': 0.17, 'learning_rate': 8.936054419349463e-06, 'epoch': 0.05}\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75609c1d7372434ab4edb7d4a541972b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/355 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.4925839304924011, 'eval_accuracy': 0.8797602256699577, 'eval_best_accuracy': 0.8797602256699577, 'eval_runtime': 45.7725, 'eval_samples_per_second': 61.959, 'eval_steps_per_second': 7.756, 'epoch': 0.05}\n",
            "{'loss': 0.1162, 'learning_rate': 2.4541553700878304e-06, 'epoch': 0.07}\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "220852f5783e4addb4c11e0215de539b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/355 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.3302706480026245, 'eval_accuracy': 0.9160789844851904, 'eval_best_accuracy': 0.9160789844851904, 'eval_runtime': 45.988, 'eval_samples_per_second': 61.668, 'eval_steps_per_second': 7.719, 'epoch': 0.07}\n",
            "{'train_runtime': 525.5844, 'train_samples_per_second': 13.501, 'train_steps_per_second': 1.688, 'train_loss': 0.18107375216994775, 'epoch': 0.09}\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f24f668649a4fce8b74d85d50562868",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/355 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import copy\n",
        "\n",
        "if hyperopt:\n",
        "    print(best)\n",
        "\n",
        "    eval_training_args = copy.copy(training_args)\n",
        "    for k, v in best.hyperparameters.items():\n",
        "        if k == \"max_steps\": v = int(v)\n",
        "        setattr(eval_training_args, k, v)\n",
        "\n",
        "    eval_trainer = transformers.Trainer(\n",
        "        model_init=model_init,\n",
        "        #model=best,\n",
        "        args=eval_training_args,\n",
        "        tokenizer=tokenizer,\n",
        "        train_dataset=small_ds_train,\n",
        "        eval_dataset=ds_dev,\n",
        "        compute_metrics=compute_metrics,\n",
        "        #callbacks = [EarlyStoppingCallback(early_stopping_patience=3)],\n",
        "        data_collator=data_collator,\n",
        "    )\n",
        "    eval_trainer.train()\n",
        "    eval_trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "#AutoTokenizer.from_checkpoint(\"/home/ed/Downloads/OO-Fine-Tune/oo-method-test-model-bylibrary-1percent/run-uw5f4ld2/checkpoint-4000\")\n",
        "#if hyperopt:\n",
        "#    tokenizer.push_to_hub(\"ejschwartz/oo-method-test-model-bylibrary-1percent\")\n",
        "#    checkpoint.push_to_hub(\"ejschwartz/oo-method-test-model-bylibrary-1percent\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "DDVdCOE4NfzN"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a680e0b532394736952ecd3857d29de8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/355 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 0.3302706480026245, 'eval_accuracy': 0.9160789844851904, 'eval_best_accuracy': 0.9160789844851904, 'eval_runtime': 43.8356, 'eval_samples_per_second': 64.696, 'eval_steps_per_second': 8.098, 'epoch': 0.09}\n"
          ]
        }
      ],
      "source": [
        "print(eval_trainer.evaluate(ds_dev))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "ename": "BadRequestError",
          "evalue": " (Request ID: Root=1-64fdc2f9-2887e3e476651a1b59fdcebb;c77f2b96-7a33-42b9-820e-0bfed2e5e71e)\n\nBad request for commit endpoint:\n\"model-index[0].results[0].dataset.config\" must be a string",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:261\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 261\u001b[0m     response\u001b[39m.\u001b[39;49mraise_for_status()\n\u001b[1;32m    262\u001b[0m \u001b[39mexcept\u001b[39;00m HTTPError \u001b[39mas\u001b[39;00m e:\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/requests/models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m \u001b[39mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1021\u001b[0m     \u001b[39mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m)\n",
            "\u001b[0;31mHTTPError\u001b[0m: 400 Client Error: Bad Request for url: https://huggingface.co/api/models/ejschwartz/oo-method-test-model-bylibrary/commit/main",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m      2\u001b[0m     \u001b[39m# Re-evaluate on best model\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m     eval_trainer\u001b[39m.\u001b[39;49mpush_to_hub(dataset\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mejschwartz/oo-method-test-split\u001b[39;49m\u001b[39m\"\u001b[39;49m, dataset_tags\u001b[39m=\u001b[39;49m[\u001b[39m\"\u001b[39;49m\u001b[39mejschwartz/oo-method-test-split\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/transformers/trainer.py:3656\u001b[0m, in \u001b[0;36mTrainer.push_to_hub\u001b[0;34m(self, commit_message, blocking, **kwargs)\u001b[0m\n\u001b[1;32m   3653\u001b[0m \u001b[39m# Wait for the current upload to be finished.\u001b[39;00m\n\u001b[1;32m   3654\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_finish_current_push()\n\u001b[0;32m-> 3656\u001b[0m \u001b[39mreturn\u001b[39;00m upload_folder(\n\u001b[1;32m   3657\u001b[0m     repo_id\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhub_model_id,\n\u001b[1;32m   3658\u001b[0m     folder_path\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49margs\u001b[39m.\u001b[39;49moutput_dir,\n\u001b[1;32m   3659\u001b[0m     commit_message\u001b[39m=\u001b[39;49mcommit_message,\n\u001b[1;32m   3660\u001b[0m     token\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49margs\u001b[39m.\u001b[39;49mhub_token,\n\u001b[1;32m   3661\u001b[0m     run_as_future\u001b[39m=\u001b[39;49m\u001b[39mnot\u001b[39;49;00m blocking,\n\u001b[1;32m   3662\u001b[0m     ignore_patterns\u001b[39m=\u001b[39;49m[\u001b[39m\"\u001b[39;49m\u001b[39m_*\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m**/*\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m   3663\u001b[0m )\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[39mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[39m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[39m=\u001b[39mfn\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, has_token\u001b[39m=\u001b[39mhas_token, kwargs\u001b[39m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:828\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    825\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrun_as_future(fn, \u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    827\u001b[0m \u001b[39m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3509\u001b[0m, in \u001b[0;36mHfApi.upload_folder\u001b[0;34m(self, repo_id, folder_path, path_in_repo, commit_message, commit_description, token, repo_type, revision, create_pr, parent_commit, allow_patterns, ignore_patterns, delete_patterns, multi_commits, multi_commits_verbose, run_as_future)\u001b[0m\n\u001b[1;32m   3497\u001b[0m     pr_url \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcreate_commits_on_pr(\n\u001b[1;32m   3498\u001b[0m         repo_id\u001b[39m=\u001b[39mrepo_id,\n\u001b[1;32m   3499\u001b[0m         repo_type\u001b[39m=\u001b[39mrepo_type,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3506\u001b[0m         verbose\u001b[39m=\u001b[39mmulti_commits_verbose,\n\u001b[1;32m   3507\u001b[0m     )\n\u001b[1;32m   3508\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 3509\u001b[0m     commit_info \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcreate_commit(\n\u001b[1;32m   3510\u001b[0m         repo_type\u001b[39m=\u001b[39;49mrepo_type,\n\u001b[1;32m   3511\u001b[0m         repo_id\u001b[39m=\u001b[39;49mrepo_id,\n\u001b[1;32m   3512\u001b[0m         operations\u001b[39m=\u001b[39;49mcommit_operations,\n\u001b[1;32m   3513\u001b[0m         commit_message\u001b[39m=\u001b[39;49mcommit_message,\n\u001b[1;32m   3514\u001b[0m         commit_description\u001b[39m=\u001b[39;49mcommit_description,\n\u001b[1;32m   3515\u001b[0m         token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m   3516\u001b[0m         revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   3517\u001b[0m         create_pr\u001b[39m=\u001b[39;49mcreate_pr,\n\u001b[1;32m   3518\u001b[0m         parent_commit\u001b[39m=\u001b[39;49mparent_commit,\n\u001b[1;32m   3519\u001b[0m     )\n\u001b[1;32m   3520\u001b[0m     pr_url \u001b[39m=\u001b[39m commit_info\u001b[39m.\u001b[39mpr_url\n\u001b[1;32m   3522\u001b[0m \u001b[39mif\u001b[39;00m create_pr \u001b[39mand\u001b[39;00m pr_url \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:118\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[39mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    116\u001b[0m     kwargs \u001b[39m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[39m=\u001b[39mfn\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, has_token\u001b[39m=\u001b[39mhas_token, kwargs\u001b[39m=\u001b[39mkwargs)\n\u001b[0;32m--> 118\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:828\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    825\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrun_as_future(fn, \u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m    827\u001b[0m \u001b[39m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m--> 828\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/hf_api.py:2728\u001b[0m, in \u001b[0;36mHfApi.create_commit\u001b[0;34m(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future)\u001b[0m\n\u001b[1;32m   2726\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   2727\u001b[0m     commit_resp \u001b[39m=\u001b[39m get_session()\u001b[39m.\u001b[39mpost(url\u001b[39m=\u001b[39mcommit_url, headers\u001b[39m=\u001b[39mheaders, data\u001b[39m=\u001b[39mdata, params\u001b[39m=\u001b[39mparams)\n\u001b[0;32m-> 2728\u001b[0m     hf_raise_for_status(commit_resp, endpoint_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcommit\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m   2729\u001b[0m \u001b[39mexcept\u001b[39;00m RepositoryNotFoundError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   2730\u001b[0m     e\u001b[39m.\u001b[39mappend_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)\n",
            "File \u001b[0;32m~/Downloads/OO-Fine-Tune/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py:299\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    295\u001b[0m \u001b[39melif\u001b[39;00m response\u001b[39m.\u001b[39mstatus_code \u001b[39m==\u001b[39m \u001b[39m400\u001b[39m:\n\u001b[1;32m    296\u001b[0m     message \u001b[39m=\u001b[39m (\n\u001b[1;32m    297\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39mBad request for \u001b[39m\u001b[39m{\u001b[39;00mendpoint_name\u001b[39m}\u001b[39;00m\u001b[39m endpoint:\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m endpoint_name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39mBad request:\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    298\u001b[0m     )\n\u001b[0;32m--> 299\u001b[0m     \u001b[39mraise\u001b[39;00m BadRequestError(message, response\u001b[39m=\u001b[39mresponse) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n\u001b[1;32m    301\u001b[0m \u001b[39m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[39;00m\n\u001b[1;32m    302\u001b[0m \u001b[39m# as well (request id and/or server error message)\u001b[39;00m\n\u001b[1;32m    303\u001b[0m \u001b[39mraise\u001b[39;00m HfHubHTTPError(\u001b[39mstr\u001b[39m(e), response\u001b[39m=\u001b[39mresponse) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
            "\u001b[0;31mBadRequestError\u001b[0m:  (Request ID: Root=1-64fdc2f9-2887e3e476651a1b59fdcebb;c77f2b96-7a33-42b9-820e-0bfed2e5e71e)\n\nBad request for commit endpoint:\n\"model-index[0].results[0].dataset.config\" must be a string"
          ]
        }
      ],
      "source": [
        "if push:\n",
        "    # Re-evaluate on best model\n",
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