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python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n", + "Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n", + "Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "\n", + "# 데이터 300개 제한\n", + "dataset = load_dataset(\"Multimodal-Fatima/OK-VQA_train\", split='train[0:300]')\n", + "\n", + "# 데이터 columns 선택\n", + "dataset = dataset.select_columns(['image', 'answers', 'question'])\n", + "\n", + "# 답변을 ID로 매핑하고, ID를 답변으로 매핑합니다.\n", + "answers = dataset['answers']\n", + "unique_answers = list(set([ans for item in answers for ans in item]))\n", + "answer_to_id = {answer: idx for idx, answer in enumerate(unique_answers)}\n", + "id_to_answer = {idx: answer for answer, idx in answer_to_id.items()}\n", + "\n", + "# 답변을 ID로 교체합니다.\n", + "for idx in range(len(dataset)):\n", + " dataset['answers'][idx] = [answer_to_id[ans] for ans in dataset['answers'][idx]]\n", + "\n", + "# 데이터셋 편평화\n", + "flattened_dataset = dataset.flatten()\n", + "\n", + "dataset.features\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 328, + "referenced_widgets": [ + "dca6bcf4058a44b88aeaab9998d4a261", + "3b08fa10bff9468cafc45758e0bb02d6", + "66103d87ea0e4e6a9fa896bc736bb4a3", + "dd8f9a87c42845989a859e16eeeca325", + "3a66a4e683f04ff29b008bd9ea7ae281", + "25b413b60a38439ab929c5a4e86dd9de", + "61ff7fdf17294f70b73977064ae2431f", + "d99cef26c4ee4de1a675337a2d278c03", + "777e813737eb4f27a42c5fa6339ff2a8", + "cd17ebc4ab854cbf8d0d423ba3f7d1b8", + "13daa72c45d44680a53bae216d0a3823", + "ea8c5e82881c4d0282ecc816929868c3", + "4510c1d4ce78491688ef89a58bf638a8", + "25238d1b63d5434f8970e1d7c274991a", + "3e37c712baa74105a773147fb8bc57e1", + "a27d9fb8a47341bcabbe77fdbfe3c7a7", + "379f7e3313f743bda980fab6f1272e8c", + "61184f8cd8bc4456a016ac4cfb6c487e", + "45570fe170e549ad8a4592c1169c81b2", + 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+ " output_dir=\"./git-vqa-finetuned-on-ok-vqa\",\n", + " per_device_train_batch_size=8,\n", + " num_train_epochs=20,\n", + " logging_dir=\"./logs\",\n", + ")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561 + }, + "id": "nNtfskxYYTPP", + "outputId": "a7993752-4f15-428c-fe91-b45c5bcab9a6" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "error", + "ename": "ImportError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel2id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mid_to_answer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m training_args = TrainingArguments(\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"./git-vqa-finetuned-on-ok-vqa\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mper_device_train_batch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memo...\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_torch_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m \u001b[0;32mand\u001b[0m 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install transformers[torch]` or `pip install accelerate -U`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1789\u001b[0m )\n", + "\u001b[0;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`", + "", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" + ], + "errorDetails": { + "actions": [ + { + "action": "open_url", + "actionText": "Open Examples", + "url": "/notebooks/snippets/importing_libraries.ipynb" + } + ] + } + } + ] + } + ] +} \ No newline at end of file