{"cells":[{"cell_type":"markdown","source":["# **미세튜닝된 어댑터 업로드한 ID: yelin1001**"],"metadata":{"id":"8WVANRh09Lmi"},"id":"8WVANRh09Lmi"},{"cell_type":"markdown","metadata":{"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},"source":["# 강의 11주차: llama2-food-order-understanding\n","\n","1. llama-2-7b-chat-hf 를 주문 문장 이해에 미세 튜닝\n","\n","- food-order-understanding-small-3200.json (학습)\n","- food-order-understanding-small-800.json (검증)\n","\n","\n","종속적인 필요 내용\n","- huggingface 계정 설정 및 llama-2 사용 승인\n","- 로깅을 위한 wandb"],"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},{"cell_type":"markdown","source":["**종속성 설치**"],"metadata":{"id":"bw2BFoFLTKUm"},"id":"bw2BFoFLTKUm"},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":8705,"status":"ok","timestamp":1702196144558,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"nDZe_wqKU6J3","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e6dd1dd9-debb-4436-f7bc-56bbcb8f92aa"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n","Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.7.0)\n","Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.25.0)\n","Requirement already satisfied: optimum in /usr/local/lib/python3.10/dist-packages (1.15.0)\n","Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.3)\n","Requirement already satisfied: trl in 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(from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n"]}],"source":["pip install transformers peft accelerate optimum bitsandbytes trl wandb"],"id":"nDZe_wqKU6J3"},{"cell_type":"markdown","source":["**라이브러리 및 모듈 임포트**\n","\n","> 데이터 처리, 모델 학습, 추론에 사용되는 라이브러리 및 모듈을 포함함."],"metadata":{"id":"4_gMKrh6TjGX"},"id":"4_gMKrh6TjGX"},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":14335,"status":"ok","timestamp":1702196162680,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","colab":{"base_uri":"https://localhost:8080/"},"outputId":"3e9731bf-0785-48a2-c214-c90fabfe2975"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n","  warnings.warn(\n"]}],"source":["import os\n","from dataclasses import dataclass, field\n","from typing import Optional\n","import re\n","\n","import torch\n","import tyro\n","from accelerate import Accelerator\n","from datasets import load_dataset, Dataset\n","from peft import AutoPeftModelForCausalLM, LoraConfig\n","from tqdm import tqdm\n","from transformers import (\n","    AutoModelForCausalLM,\n","    AutoTokenizer,\n","    BitsAndBytesConfig,\n","    TrainingArguments,\n",")\n","\n","from trl import SFTTrainer\n","\n","from trl.trainer import ConstantLengthDataset"],"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["6c3c61b83cd64895937672390d7d23ee","0211c736ccd34d1a883453a0c7a75968","63e98766b62245e49b008692b300f2a5","922e92f8c4aa4b04a5d4eb2553460b16","9f75bb819f944448a1c48692de4264b3","9db00cec03a04177a27ee68e7b3e8566","d0e2410a804e434590702b7ebf22ac6a","93089b782db543d6bcd698360c6e7cce","4bb9b8b910244b6ba45acf68e8c17fbf","a74143e64e6540358b32ec393fd6ca8a","6afdf54c14144c5695af1c986482d5f4","ae18cd14111e4c2ca8cec89c57d343c4","fa5f74b5f8c440ae81008a623fea9d8f","3d62a99998234c99ae900f564c9a53e3","74c090ffb4f641acba87a8b8e73bf9a2","7bba3d2cf45c48ad92c5377cd6932541","6a085462311f4fbaa1eac0225e55e1f4","b9186db9ec4040eb9cb1c5831994a9f3","e69eea3a02594848b2a34c23b4a0a319","77865c0d156d4da28694b7351d8516b2","52eacf6b63324c8c8b137939034459f0","fffaee2b18324f3294b77466d8bcfd3f","f178f4ef9e8a426a924ef7370da16a29","66ff6848ba0b46d3b34055af04ca8d5d","fd62b22cb52d4d59aba9965853674b1a","c15cf50dafcf4d05a81ac7e3b778372d","bfbf4f2c9d0e41f0a9daf904ff728722","40c2db0f041a41e4a2852cb2a7ad70ba","47b5041a893747d1b51577bc4140ccfa","4d205bfdf7b2463aba5dd9aeabd4b1b9","639699879c444db5a1a877f052690e75","9ca13b8c658a43379330ccfaee4b1853"]},"executionInfo":{"elapsed":512,"status":"ok","timestamp":1702196168826,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"tX7gYxZaVhYL","outputId":"65a19ba6-4b9a-4847-ab45-4bf56358a2df"},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co./front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6c3c61b83cd64895937672390d7d23ee"}},"metadata":{}}],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"id":"tX7gYxZaVhYL"},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":4311,"status":"ok","timestamp":1702196180811,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"4DF9D2SXVpHP","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a71c8cc4-f665-46d7-a0d7-d66e2118368b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/gdrive')"],"id":"4DF9D2SXVpHP"},{"cell_type":"markdown","metadata":{"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},"source":["# 매개 변수 설정\n","\n","\n","> 스크립트에 사용되는 다양한 인자들을 설정함.\n","\n","\n","\n"],"id":"036eece3-5f89-4fec-b0cd-268478b5e83d"},{"cell_type":"code","execution_count":null,"metadata":{"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf"},"outputs":[],"source":["@dataclass\n","class ScriptArguments:\n","    cache_dir: Optional[str] = field(\n","        default=None, metadata={\"help\": \"the cache dir\"}\n","    )\n","    model_name: Optional[str] = field(\n","        default=\"meta-llama/Llama-2-7b-chat-hf\", metadata={\"help\": \"the model name\"}\n","    )\n","\n","    dataset_name: Optional[str] = field(\n","        default=None,\n","        metadata={\"help\": \"the dataset name\"},\n","    )\n","    seq_length: Optional[int] = field(\n","        default=1024, metadata={\"help\": \"the sequence length\"}\n","    )\n","    num_workers: Optional[int] = field(\n","        default=8, metadata={\"help\": \"the number of workers\"}\n","    )\n","    training_args: TrainingArguments = field(\n","        default_factory=lambda: TrainingArguments(\n","            output_dir=\"./results\",\n","            # max_steps=500,\n","            logging_steps=20,\n","            # save_steps=10,\n","            per_device_train_batch_size=1,\n","            per_device_eval_batch_size=1,\n","            gradient_accumulation_steps=2,\n","            gradient_checkpointing=False,\n","            group_by_length=False,\n","            learning_rate=1e-4,\n","            lr_scheduler_type=\"cosine\",\n","            # warmup_steps=100,\n","            warmup_ratio=0.03,\n","            max_grad_norm=0.3,\n","            weight_decay=0.05,\n","            save_total_limit=20,\n","            save_strategy=\"epoch\",\n","            num_train_epochs=1,\n","            optim=\"paged_adamw_32bit\",\n","            fp16=True,\n","            remove_unused_columns=False,\n","            report_to=\"wandb\",\n","        )\n","    )\n","\n","    packing: Optional[bool] = field(\n","        default=True, metadata={\"help\": \"whether to use packing for SFTTrainer\"}\n","    )\n","\n","    peft_config: LoraConfig = field(\n","        default_factory=lambda: LoraConfig(\n","            #r=8,\n","            r=16,\n","            lora_alpha=16,\n","            #lora_dropout=0.05,\n","            lora_dropout=0.15,\n","            target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"down_proj\", \"up_proj\", \"gate_proj\"],\n","            bias=\"none\",\n","            task_type=\"CAUSAL_LM\",\n","        )\n","    )\n","\n","    merge_with_final_checkpoint: Optional[bool] = field(\n","        default=False, metadata={\"help\": \"Do only merge with final checkpoint\"}\n","    )"],"id":"e03d01b5-eaeb-4626-9dc5-47c3691e7fcf"},{"cell_type":"markdown","metadata":{"id":"b0b34850-006c-4c87-a7d5-27c6c871e7de"},"source":["# 유틸리티"],"id":"b0b34850-006c-4c87-a7d5-27c6c871e7de"},{"cell_type":"code","execution_count":null,"metadata":{"id":"8224d213-8766-4b40-899e-1a1b8d164365"},"outputs":[],"source":["def chars_token_ratio(dataset, tokenizer, nb_examples=400):\n","    \"\"\"\n","    Estimate the average number of characters per token in the dataset.\n","    \"\"\"\n","    total_characters, total_tokens = 0, 0\n","    for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n","        text = prepare_sample_text(example)\n","        total_characters += len(text)\n","        if tokenizer.is_fast:\n","            total_tokens += len(tokenizer(text).tokens())\n","        else:\n","            total_tokens += len(tokenizer.tokenize(text))\n","\n","    return total_characters / total_tokens\n","\n","\n","def print_trainable_parameters(model):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","    \"\"\"\n","    trainable_params = 0\n","    all_param = 0\n","    for _, param in model.named_parameters():\n","        all_param += param.numel()\n","        if param.requires_grad:\n","            trainable_params += param.numel()\n","    print(\n","        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n","    )"],"id":"8224d213-8766-4b40-899e-1a1b8d164365"},{"cell_type":"markdown","metadata":{"id":"58085944-b951-4c9b-bdeb-0ccc60c2a3b3"},"source":["# 데이터 로딩"],"id":"58085944-b951-4c9b-bdeb-0ccc60c2a3b3"},{"cell_type":"markdown","source":["**텍스트 전처리 함수**\n","\n","> prepare_sample_text 함수는 데이터셋의 각 예제를 전처리하는데 사용됨. 아래 주어진 템플릿으로 텍스트를 생성함."],"metadata":{"id":"ceujqBmdUoJr"},"id":"ceujqBmdUoJr"},{"cell_type":"code","execution_count":null,"metadata":{"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba"},"outputs":[],"source":["def prepare_sample_text(example):\n","    \"\"\"Prepare the text from a sample of the dataset.\"\"\"\n","\n","    prompt_template = \"\"\"###System;{System}\n","    ###User;{User}\n","    ###Midm;{Midm}\"\"\"\n","\n","    default_system_msg = (\n","        \"너는 먼저 사용자가 작성한 영화 리뷰의 감정을 분석하는 에이전트이다. 이로부터 긍정 또는 부정을 판단해야 한다.\"\n","    )\n","\n","    text = (\n","        prompt_template.format(System=default_system_msg,\n","                               User=example[\"document\"],\n","                               Midm=\"긍정\" if example[\"label\"]==1 else \"부정\"\n","                               )\n","    )\n","\n","    return text"],"id":"b49c3470-480e-4ff2-b2c5-fcf1d3a13fba"},{"cell_type":"markdown","source":["**훈련 데이터셋 생성 함수**\n","\n","\n","> create_train_datasets 함수는 훈련 데이터셋을 생성함. 훈련 데이터 개수를 지정하고 토큰화하여 모델 훈련에 사용될 수 있도록 데이터셋 구성함.\n","\n"],"metadata":{"id":"L6ZySGtGVQuK"},"id":"L6ZySGtGVQuK"},{"cell_type":"code","execution_count":null,"metadata":{"id":"5d9abfb4-339d-414b-855b-ced51631b752"},"outputs":[],"source":["def create_train_datasets(tokenizer, args):\n","    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)\n","    train_data = dataset['train'].select(range(150000))\n","\n","    chars_per_token = chars_token_ratio(train_data, tokenizer)\n","    print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")\n","\n","    train_dataset = ConstantLengthDataset(\n","        tokenizer,\n","        train_data,\n","        formatting_func=prepare_sample_text,\n","        infinite=True,\n","        seq_length=args.seq_length,\n","        chars_per_token=chars_per_token,# 부가정보, 컨트롤하는 정보\n","    )\n","\n","    return train_dataset"],"id":"5d9abfb4-339d-414b-855b-ced51631b752"},{"cell_type":"markdown","source":["**테스트 데이터셋 생성**\n","\n","> create_valid_datasets 함수는 테스트 데이터셋을 생성함. 테스트 데이터 개수를 지정하고 토큰화하여 모델 추론에 사용될 수 있도록 데이터셋 구성함.\n","\n"],"metadata":{"id":"Q_aYVx7QWE2f"},"id":"Q_aYVx7QWE2f"},{"cell_type":"code","execution_count":null,"metadata":{"id":"rwC9GQ3HHP_Z"},"outputs":[],"source":["def create_valid_datasets(tokenizer, args):\n","    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir) # nsmc 데이터셋을 허깅페이스에서 가져옴\n","    valid_data = dataset['test']\n","    valid_dataset = valid_data.select(range(1000))\n","\n","    return valid_dataset"],"id":"rwC9GQ3HHP_Z"},{"cell_type":"markdown","metadata":{"id":"2e7ef79d-a354-4c80-9435-c130ffed9e32"},"source":["# 미세 튜닝용 모델 로딩"],"id":"2e7ef79d-a354-4c80-9435-c130ffed9e32"},{"cell_type":"code","execution_count":null,"metadata":{"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff"},"outputs":[],"source":["script_args = ScriptArguments(\n","    num_workers=2,\n","    seq_length=512,\n","    dataset_name='nsmc',\n","    model_name='meta-llama/Llama-2-7b-chat-hf',\n","    )"],"id":"bf4c65e0-5ffa-4a20-9e78-6c1f030572ff"},{"cell_type":"code","execution_count":null,"metadata":{"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},"outputs":[],"source":["script_args.training_args.logging_steps = 100\n","script_args.training_args.max_steps = 1600\n","script_args.training_args.output_dir = '/gdrive/MyDrive/nlp/lora-llama-2-7b-nsmc'\n","script_args.training_args.run_name = 'llama-2-7b-nsmc'"],"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},{"cell_type":"code","execution_count":null,"metadata":{"id":"bac62c01-21ef-491e-a686-cf4988186c58","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702196197581,"user_tz":-540,"elapsed":3,"user":{"displayName":"장예람","userId":"15119681621712404836"}},"outputId":"be61b1f4-4e78-408c-b3cd-4e7d51756a64"},"outputs":[{"output_type":"stream","name":"stdout","text":["ScriptArguments(cache_dir=None, model_name='meta-llama/Llama-2-7b-chat-hf', dataset_name='nsmc', seq_length=512, num_workers=2, training_args=TrainingArguments(\n","_n_gpu=1,\n","adafactor=False,\n","adam_beta1=0.9,\n","adam_beta2=0.999,\n","adam_epsilon=1e-08,\n","auto_find_batch_size=False,\n","bf16=False,\n","bf16_full_eval=False,\n","data_seed=None,\n","dataloader_drop_last=False,\n","dataloader_num_workers=0,\n","dataloader_pin_memory=True,\n","ddp_backend=None,\n","ddp_broadcast_buffers=None,\n","ddp_bucket_cap_mb=None,\n","ddp_find_unused_parameters=None,\n","ddp_timeout=1800,\n","debug=[],\n","deepspeed=None,\n","disable_tqdm=False,\n","dispatch_batches=None,\n","do_eval=False,\n","do_predict=False,\n","do_train=False,\n","eval_accumulation_steps=None,\n","eval_delay=0,\n","eval_steps=None,\n","evaluation_strategy=no,\n","fp16=True,\n","fp16_backend=auto,\n","fp16_full_eval=False,\n","fp16_opt_level=O1,\n","fsdp=[],\n","fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n","fsdp_min_num_params=0,\n","fsdp_transformer_layer_cls_to_wrap=None,\n","full_determinism=False,\n","gradient_accumulation_steps=2,\n","gradient_checkpointing=False,\n","gradient_checkpointing_kwargs=None,\n","greater_is_better=None,\n","group_by_length=False,\n","half_precision_backend=auto,\n","hub_always_push=False,\n","hub_model_id=None,\n","hub_private_repo=False,\n","hub_strategy=every_save,\n","hub_token=<HUB_TOKEN>,\n","ignore_data_skip=False,\n","include_inputs_for_metrics=False,\n","include_tokens_per_second=False,\n","jit_mode_eval=False,\n","label_names=None,\n","label_smoothing_factor=0.0,\n","learning_rate=0.0001,\n","length_column_name=length,\n","load_best_model_at_end=False,\n","local_rank=0,\n","log_level=passive,\n","log_level_replica=warning,\n","log_on_each_node=True,\n","logging_dir=./results/runs/Dec10_08-16-34_612e1d9dbd6f,\n","logging_first_step=False,\n","logging_nan_inf_filter=True,\n","logging_steps=100,\n","logging_strategy=steps,\n","lr_scheduler_type=cosine,\n","max_grad_norm=0.3,\n","max_steps=1600,\n","metric_for_best_model=None,\n","mp_parameters=,\n","neftune_noise_alpha=None,\n","no_cuda=False,\n","num_train_epochs=1,\n","optim=paged_adamw_32bit,\n","optim_args=None,\n","output_dir=/gdrive/MyDrive/nlp/lora-llama-2-7b-nsmc,\n","overwrite_output_dir=False,\n","past_index=-1,\n","per_device_eval_batch_size=1,\n","per_device_train_batch_size=1,\n","prediction_loss_only=False,\n","push_to_hub=False,\n","push_to_hub_model_id=None,\n","push_to_hub_organization=None,\n","push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n","ray_scope=last,\n","remove_unused_columns=False,\n","report_to=['wandb'],\n","resume_from_checkpoint=None,\n","run_name=llama-2-7b-nsmc,\n","save_on_each_node=False,\n","save_safetensors=True,\n","save_steps=500,\n","save_strategy=epoch,\n","save_total_limit=20,\n","seed=42,\n","skip_memory_metrics=True,\n","split_batches=False,\n","tf32=None,\n","torch_compile=False,\n","torch_compile_backend=None,\n","torch_compile_mode=None,\n","torchdynamo=None,\n","tpu_metrics_debug=False,\n","tpu_num_cores=None,\n","use_cpu=False,\n","use_ipex=False,\n","use_legacy_prediction_loop=False,\n","use_mps_device=False,\n","warmup_ratio=0.03,\n","warmup_steps=0,\n","weight_decay=0.05,\n","), packing=True, peft_config=LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=16, target_modules={'up_proj', 'v_proj', 'down_proj', 'gate_proj', 'k_proj', 'o_proj', 'q_proj'}, lora_alpha=16, lora_dropout=0.15, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}), merge_with_final_checkpoint=False)\n"]}],"source":["print(script_args)"],"id":"bac62c01-21ef-491e-a686-cf4988186c58"},{"cell_type":"markdown","source":["**4비트 양자화**\n","\n","\n","> 데이터를 4비트로 양자화하여 로드함\n","\n"],"metadata":{"id":"bnQg8GPOW9Lm"},"id":"bnQg8GPOW9Lm"},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ff1422e-184d-4438-b033-40ae8bdaa5fd"},"outputs":[],"source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16,\n",")"],"id":"1ff1422e-184d-4438-b033-40ae8bdaa5fd"},{"cell_type":"markdown","source":["**사전 훈련된 모델 로딩**"],"metadata":{"id":"DdtsgFvYXh0L"},"id":"DdtsgFvYXh0L"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":334,"referenced_widgets":["d51ba58ca7334171a6434e1268eb70ee","43fc4ef4564f499e9a7f7349d0b7dc01","edc1b4b45a9e413b8f77a44e66a38c69","533af0786ce6474f80a803222ed2efdf","c7cae4abfe764090aa48ab64b366ea9c","aae1344b30bb4998be01b9814ec05648","41918e06c008477eb4ee3ac717f45a2b","9056e96242e4416ea69c9b2ef81f3ee1","f42dd57d767c4137861922721256e53b","e8c029b1bf1f4497aac51222656e7963","6f7792c675e646f9820f5300e4d6eb67","1105a1d14a654218aa6dd8bbc726e0e3","10fa9b1068864ac8b4a05d8c380b45aa","ec2847d08f4f446aa3c48362fd59a4e0","3f262537d261443c9078676e0350e5dc","a12fc594a6da4c4b803905787a35b103","d475bc5796fe4fdbb85cf9a1a72ce2cb","5fb2931e69b14af8ba5b368a7a8cbc4a","27a17c05a6f74ecfba2f56211367531e","a5fcc9f5d3174e25b56225fd90d022d0","6fc54c3788d947259b36ef20ff137532","7da442a79279458da5a9ad58a2e0e473","9e4ae598bfff492294c3b0ec65cb180d","276bce6412f0427aa95a4619f94b9f17","cdbf90f276244a2e91458fff5c788f96","fb5635912f4c45f485d991bb5f7cb891","4eb4a3c0929448ee887c125f6c76ac50","56685a4dfb514f198e0c72fea425e8c2","60f428e5e830403e903414bf0b42f449","50b394d713a5447fb349d6366cdc6e45","d77798638a1d4627a8463a9aaa7fb904","d049d5cd83204088a217b61a6bf83802","929e57274939496fa91eb693af209721","4fe3532393ea44899d6ae24a99c4b4e3","19e3d121709b4333ac06859a202b7f30","d9611b0a88904f2bb81170bc01c67e82","4075d082f13f4054b26a9a6f97d49e2f","26dc9684276a44469cebc1f39653d0ca","cb3c60b7d5524dfe8ed4064997c71f8b","0e911deef88c4a47bf9b861d00220288","fedd0bd6406a4ff58a969bdccccae8e0","4cabda1dc0b24296bb0874e3d5ef5c96","57727fcbbd4c4c50940c5996430d508f","d1e0a96628c14e38ad70be07f77f119f","fa83b562ef9545d8a844a16ec331c7aa","1f742c3a5f784793a64eb694675285c3","c5b9bb9c70b04324a17f1a396f001e60","7d5942f44d64460faa7f12b00803dea4","6c07cb04aa3e4c18adfa944bef99905d","41d79c2ed9594e56a1608995df861f7a","b0813f261b6f449db131d9c91c405e4e","e6849bdc51c2400d8dddbd29409a7d13","977037e2664a4157a2989b07891f9b7b","c59e99603a524eaba314f12bc67cbed4","40fe3d5cf9294afc96c5cc6a273a7df5","c46d0b8b22f442de8d822128e32a3f9a","eff6e70ae2014c3798f421784b85c57b","816e6005212544f29e69bf8196496858","3d185371372346cc880f02f281c81271","285a2cb60f914770b241b073f38c73f7","8720f9d709ce4d3cb9a26a7528d0813e","b9e297b327394c938bee5b795b769639","6c7ca5a245f148458349613b5b94ed4a","088aee16d58943b880a8082dcbda4f65","7af7c9a7faa04cd9b8e6862e0645d06d","635398984b3e46ccb426de3b7203b805","59a2e11f34fb41928dc64d0a4d5b427b","2376b4379df94285b40e55e863ed8378","66503968f9a94d8baee1e967c2876514","1bc2fe53aa8f4c10ae47972db66b9d17","1ffdaf8a53cb4bff9cfa201ffb658c6c","8c3e4234817b477e8a23a87db341a386","d7b114212c964380ad9dab1e7bf51f24","a5641317b9ff409bb63db46d5c490948","a183b3bb64834db3be39859bd8be43b5","92d526db32d542738b0b565d19d12a77","d35bb9aa2aba4ae9af0715f3c1887578"]},"executionInfo":{"elapsed":149798,"status":"ok","timestamp":1702189553749,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d","outputId":"594f9ce3-7d4a-4441-a67f-9d12383877a0"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:472: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d51ba58ca7334171a6434e1268eb70ee"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1105a1d14a654218aa6dd8bbc726e0e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9e4ae598bfff492294c3b0ec65cb180d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4fe3532393ea44899d6ae24a99c4b4e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa83b562ef9545d8a844a16ec331c7aa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c46d0b8b22f442de8d822128e32a3f9a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:374: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"59a2e11f34fb41928dc64d0a4d5b427b"}},"metadata":{}}],"source":["base_model = AutoModelForCausalLM.from_pretrained(\n","    script_args.model_name,\n","    quantization_config=bnb_config,\n","    device_map=\"auto\",  # {\"\": Accelerator().local_process_index},\n","    trust_remote_code=True,\n","    use_auth_token=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","base_model.config.use_cache = False"],"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1154,"status":"ok","timestamp":1702189560337,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"E9D239NqbDba","outputId":"4d757e4b-8751-4968-a9de-a04eb4748b37"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (k_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (v_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","          (rotary_emb): LlamaRotaryEmbedding()\n","        )\n","        (mlp): LlamaMLP(\n","          (gate_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","          (up_proj): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","          (down_proj): Linear4bit(in_features=11008, out_features=4096, bias=False)\n","          (act_fn): SiLUActivation()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")"]},"metadata":{},"execution_count":15}],"source":["base_model"],"id":"E9D239NqbDba"},{"cell_type":"code","execution_count":null,"metadata":{"id":"d37b485f-4fd3-404f-ab02-2bf3e93b3fc2"},"outputs":[],"source":["peft_config = script_args.peft_config"],"id":"d37b485f-4fd3-404f-ab02-2bf3e93b3fc2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1702189563885,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"4420fcc4-2bac-413d-b7aa-89455c512419","outputId":"638bcae1-5610-4f87-d14b-c7952ca765ef"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=16, target_modules={'k_proj', 'q_proj', 'gate_proj', 'down_proj', 'up_proj', 'o_proj', 'v_proj'}, lora_alpha=16, lora_dropout=0.15, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={})"]},"metadata":{},"execution_count":17}],"source":["peft_config"],"id":"4420fcc4-2bac-413d-b7aa-89455c512419"},{"cell_type":"markdown","source":["**토크나이저 초기화 및 토크나이저, 모델 설정**"],"metadata":{"id":"SX7JSIJmYRau"},"id":"SX7JSIJmYRau"},{"cell_type":"code","execution_count":null,"metadata":{"id":"f47f9584-3988-46b8-a062-29dcde75a0e2","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["1e3723ffc3a54970b8e0b43fd8805866","db97d9726e364feca273d047c94b2fab","ae8afc4e07ff4dd28b8969cfb41da39e","94103dfa72b3429fb2519b8a0f10d164","b7658436be8c4458a4df39384050d8f0","dc023f49f8f547158ee3cf053b228eb4","7f768cb85f9a4fc088e00d6908345a29","c549af59dee04d5c860e5867eff8ec9c","65287a12732342c7986c4252bdd21a20","a9df2643e83b41a188654b7ecd1608cd","b340a76c1a5f41c680292b1cdc1b2df9","29f1a120a03a41608d4f91f02614f601","521819d3f5814201b0576cdc7adb6791","925e7f28609c49aa9c093563e6ad62ab","5eb3396ba6084534be1b882c41e0e5a7","04c95f2ff8b54742a53c8456289e120e","487987caa5a84be8bbfe6bfb89f1e93b","9e4b1667cce24dfd8d9f40479630458b","1514a2fa1d2a4a8baf3f355fd70314d8","aa2bef8b8ba942c9bef823df57ca92d2","987016cb790342b7823a7a3b7b9ad7d0","1a6858f6fdf3422b8f40f54c5530a82b","6b0af2e48c6e44d99675b18204f417fc","1e665df88e6c484c913b63f4d8231b2b","effc0a61f48c45769ef0455d8f33f22f","246b143df1974eff9c77e54e99fe125c","85afda700a94403a9f8208ea7032afee","1d090f39741b4b9eb2ebf1cb335193a6","a751918b6ebe457080d86cee1e46d6af","6444f14eb8d341e384a1e36c3437ebb0","9017381ce8ca477fb926f4f7780324c5","21890f56fa7b470db1dfef43df751a0b","5918b08e3f1e44e4a4d1ef53ef259fa4","3d3493430c1b4c55b9270df79d5bab9f","5f73c442984d47b48c162a8ce88564c9","810fd3b0ab584d5c963052c68c8098ba","e242a0eb27ce43d791b28c272e64e1c0","448bd7624de141399b61de179727efbe","4da520704cad4fab8e0d76aa055af82b","df5f1691c7b34142a2bac1294af134c2","f9958b0aa6ee4e638f14871be08b232e","aaabb6c1128d40caaae3002eb548a3bf","a05c56ba0bac4272a3e015541b8f6de2","deda2b17745441df9e1c3bb26146f9b6"]},"executionInfo":{"status":"ok","timestamp":1702189570112,"user_tz":-540,"elapsed":3825,"user":{"displayName":"장예람","userId":"15119681621712404836"}},"outputId":"a97f9d0d-9f60-4f9d-da52-5f12529a6f6a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 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AutoTokenizer.from_pretrained(\n","    script_args.model_name,\n","    trust_remote_code=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","\n","if getattr(tokenizer, \"pad_token\", None) is None:\n","    tokenizer.pad_token = tokenizer.eos_token\n","tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training\n","\n","base_model.config.pad_token_id = tokenizer.pad_token_id"],"id":"f47f9584-3988-46b8-a062-29dcde75a0e2"},{"cell_type":"code","execution_count":null,"metadata":{"id":"abd17c83-ab8d-44cb-b69b-fc0936c2cec5"},"outputs":[],"source":["training_args = script_args.training_args"],"id":"abd17c83-ab8d-44cb-b69b-fc0936c2cec5"},{"cell_type":"markdown","source":["**데이터셋 생성**"],"metadata":{"id":"_Z1y6UmwYjrh"},"id":"_Z1y6UmwYjrh"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":398,"referenced_widgets":["a0985679c87e48d8a309814366d21d9f","d4f0145ceda647a4a531486ce5e917e1","5e8d869d10f14e2d83456fa5a95c4510","a5ead19de5ed46ea9e6ecdbb06c60e0e","7f44ea52e6c0402aad17595be9e3b92f","4f7cf00a38ff4e05abd7c0c6ce892dd1","8ea404a4b6d047bf9a62771557237783","1b4408345fe14fc199eec67d35e66320","87825c022b6d440ba1568b8e383c0679","17fe480ce2d2442088ec52a436dc99c8","21fe9bcfa2cc4bf094de1dc7a42456ce","1643cbc72dd8402c87f673021be8c7eb","6f25fa60055345e1a8918fb9658d8cc4","7bfd11dca3224aa7af23933763a78ab8","3ce8becda22a487596b74a60522e5e5f","b59c5843a91744238535ee937a7f708f","b84329c6808246cc89b72b305f519304","9a0e060f23b24bbca71b0022db415680","6d257961070e465f836fc1b3ff9e326a","d99879284c9b422481cb151ab049cae3","14289f892d8a423989fa75d2bd4bc4db","37b7be306b804480855a2ac2ab7b0cdf","7a8164789179442db837624e060fb25e","e186354343b44fc2adab3fab8c12f306","dd11106fa0334d4b805f662817d2c630","83d015b2ebef4f058d4c8c7225f6e1bc","14897ff0b6eb4f17872ab61f3cf98d2b","2b356d6e9ce54666bc73c28b8c29bb42","e6e5ee699d5b4e6cb82f1caea6a92442","ce95367d94fa4b4ea15d6ccf23d16495","815ee92544404aa88c382c1ab34094d0","9549ac5e4f1c43f2a6300c555fff032e","127f92d210c54a9ca07cccc1d0e755e9","809bfe057aa94ceca782713454717274","4dfb06b7f694433d989374ce4fdcb2b2","2dea5e18ab394fd0a8b4de1d416f9553","e4e3e22e227844abb7546456a8f87ab0","8d303c399a3c424ab07bd1cfb405302b","a0bd8f760335498e937c0174b1a10083","2f601b7d7a5e449a992b73c90545fc31","ecab67ea289242bbbfdfab6e5630e301","5e01707c1a1f49c8869bc3e78adcefaf","a96e28ee95634be4b1ff24d690a9250c","4962014453974e6198edf234607c202b","8d48048b9fc64577a17994ad68cb48f4","046c75d73fbf4154bfce65a6041b9092","91d5e80c0b6447d48ca4bfccfc997c52","1ed1c7d380004d47a78e6aea9c5768c7","e4790ae592ac4657b25356a4d158be26","433822e8d67a4b7b8059c9511a705bfe","fefb57acac8c410ebf5cea89d9df3e56","94273685c405445ca6ec860aa63fa191","d1b825de7fe149b09db2b0b4d56381e1","01d2a81d49fc48bdaf48b96c68416715","66e3f1d844f34c39afa96a0041f6b858","7b92a30230154266ab895096f6801190","2deca322a37c4496ba6cff5a0b181655","2c76bec4333246ec8bbd13b3ba8b81da","f4854966009940b792e7b690532359f4","8ac67c6cc03f48148225caf469098bc9","f5f9f850f8de491d81c5539941c34f45","52bbb23cdd1d407fbf1f3c18a02bbf83","9d0c5fe434eb45d7b21041e3c43aa727","e22d2ac321804015b3cd07ec8c08470e","65caa5212af240e38b6d2b0cb1da7a33","9bb5487a135242478afa6205402e132a","7a8e106f026a4e4ba7d3c6c77e1d1c9f","44783ddf52ef45c9b8ce124772855d65","c132abb9a37e4f709a2b3723779532b6","034ca95c482a4c5dbeeef67a3fa0bc82","8c6ac0285c1a451089bbccfe77be5424","9a4b6cad80804c1ea3147c20cbf8524b","21a8c7701f3c45b99d2fda1bef965354","06ad82be1f9e43cf8a139189b9a7733c","10e95cadfba44da1a3a018ae0dbc3a18","bf2a6cc0b0664b6b8b69ecc9abe65650","5092e3b2a568406ba00cb561c1fe3306","53ef229f13014155941c3cce9265a7d9","8712a703b1144b41a7626d74b451b1ba","422d31147c55448da086d0d9f33597fd","4169aa94dd7146539cb5e0b1314ae7e9","be672a192e1c464498aa549045252f7f","3e7fe31cb36b4f48b35432dadbca3af1","a33e22d5289448c589e7c08e43b95e37","9923c1ef95c54cee8439431c34adb345","b1a4587cfea841ba90e2714efa327f77","58ffef8c1d3740e78d7f3a8c98dbe8de","a619a929e86f4b7f98ca2401e9f27a42","8752b4bd4045494fb8464ef75fbf1be6","43d344a1d0b84c89b39f3dedf9c5a431","fddba4effb98459698bc261310db2205","ede00f559ee14bf186fa7c63e4da2035","bfcbd4b2c91b4052b934250f986aedeb","a875880f6fae4428a5b3601dc0f8b299","ed927caeddaf4e1bb3d8e7ec70ec6036","67011ae2eabf4262a45daa7dfdc6fbb5","97f5eb9a44324f56aefa96d20c277767","274a7ce184ad495fa55fa68a5e4027ef","f44a75fe4fb34c4c9828587a34baa4ac"]},"executionInfo":{"elapsed":19590,"status":"ok","timestamp":1702189594936,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","outputId":"3a6aea5f-7634-457d-e490-2af204abb3a1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.18k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a0985679c87e48d8a309814366d21d9f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1643cbc72dd8402c87f673021be8c7eb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/3.74k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7a8164789179442db837624e060fb25e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"809bfe057aa94ceca782713454717274"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/6.33M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8d48048b9fc64577a17994ad68cb48f4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/2.12M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b92a30230154266ab895096f6801190"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7a8e106f026a4e4ba7d3c6c77e1d1c9f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/150000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53ef229f13014155941c3cce9265a7d9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8752b4bd4045494fb8464ef75fbf1be6"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["100%|██████████| 400/400 [00:00<00:00, 2590.25it/s]\n","/usr/local/lib/python3.10/dist-packages/trl/trainer/utils.py:548: UserWarning: The passed formatting_func has more than one argument. Usually that function should have a single argument `example` which corresponds to the dictionary returned by each element of the dataset. Make sure you know what you are doing.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["The character to token ratio of the dataset is: 0.84\n"]}],"source":["train_dataset = create_train_datasets(tokenizer, script_args)\n","valid_dataset = create_valid_datasets(tokenizer, script_args)"],"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1702189594936,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"000314e9-f10b-4685-8da6-0511494a9eb4","outputId":"ec6dd301-2638-41b1-c21b-19b6ebd73d2a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["150000"]},"metadata":{},"execution_count":21}],"source":["len(train_dataset)"],"id":"000314e9-f10b-4685-8da6-0511494a9eb4"},{"cell_type":"markdown","source":["**SFTTrainer 초기화**\n","\n","\n",">\n","\n"],"metadata":{"id":"8dbpPmzXYzOV"},"id":"8dbpPmzXYzOV"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2642,"status":"ok","timestamp":1702189603659,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","outputId":"e3ba786b-363a-41d7-ee77-fb51994d1517"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:267: UserWarning: You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached.\n","  warnings.warn(\n"]}],"source":["trainer = SFTTrainer(\n","    model=base_model,\n","    formatting_func=prepare_sample_text,\n","    train_dataset=train_dataset,\n","    eval_dataset=valid_dataset,\n","    peft_config=peft_config,\n","    packing=script_args.packing,\n","    max_seq_length=script_args.seq_length,\n","    tokenizer=tokenizer,\n","    args=training_args,\n",")"],"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1702189604115,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"gw9xbeUgbZEo","outputId":"6ead70c7-7d6a-437c-c1e6-881056b13712"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LlamaForCausalLM(\n","  (model): LlamaModel(\n","    (embed_tokens): Embedding(32000, 4096)\n","    (layers): ModuleList(\n","      (0-31): 32 x LlamaDecoderLayer(\n","        (self_attn): LlamaAttention(\n","          (q_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (k_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (v_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (o_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (rotary_emb): LlamaRotaryEmbedding()\n","        )\n","        (mlp): LlamaMLP(\n","          (gate_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=11008, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (up_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=4096, out_features=11008, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=4096, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=11008, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (down_proj): lora.Linear4bit(\n","            (base_layer): Linear4bit(in_features=11008, out_features=4096, bias=False)\n","            (lora_dropout): ModuleDict(\n","              (default): Dropout(p=0.15, inplace=False)\n","            )\n","            (lora_A): ModuleDict(\n","              (default): Linear(in_features=11008, out_features=16, bias=False)\n","            )\n","            (lora_B): ModuleDict(\n","              (default): Linear(in_features=16, out_features=4096, bias=False)\n","            )\n","            (lora_embedding_A): ParameterDict()\n","            (lora_embedding_B): ParameterDict()\n","          )\n","          (act_fn): SiLUActivation()\n","        )\n","        (input_layernorm): LlamaRMSNorm()\n","        (post_attention_layernorm): LlamaRMSNorm()\n","      )\n","    )\n","    (norm): LlamaRMSNorm()\n","  )\n","  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",")"]},"metadata":{},"execution_count":23}],"source":["base_model"],"id":"gw9xbeUgbZEo"},{"cell_type":"markdown","source":["**base_model 장치 확인**\n","\n","\n","> device 속성은 모델이 CPU에 있는지 GPU에 있는지 알려줌.\n","\n"],"metadata":{"id":"wbWUTqvWZOsR"},"id":"wbWUTqvWZOsR"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":527,"status":"ok","timestamp":1702189607940,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"cuc1RNfUK_sP","outputId":"922a1951-7789-4f7c-b902-baa0365f061a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["device(type='cuda', index=0)"]},"metadata":{},"execution_count":24}],"source":["base_model.device\n"],"id":"cuc1RNfUK_sP"},{"cell_type":"markdown","source":["**학습 가능한 파라미터 정보**"],"metadata":{"id":"Hga34BUfZgwV"},"id":"Hga34BUfZgwV"},{"cell_type":"code","execution_count":null,"metadata":{"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1702189609684,"user_tz":-540,"elapsed":3,"user":{"displayName":"장예람","userId":"15119681621712404836"}},"outputId":"d764ad17-da4d-473c-98da-12d3a4096b9d"},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 39976960 || all params: 3540389888 || trainable%: 1.1291682911958425\n"]}],"source":["print_trainable_parameters(base_model)"],"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":966},"id":"tFeEyBJnCi0C","outputId":"a90738a6-5bed-4d7c-f572-bf0f255e7755","executionInfo":{"status":"ok","timestamp":1702195618937,"user_tz":-540,"elapsed":4586203,"user":{"displayName":"장예람","userId":"15119681621712404836"}}},"outputs":[{"data":{"application/javascript":["\n","        window._wandbApiKey = new Promise((resolve, reject) => {\n","            function loadScript(url) {\n","            return new Promise(function(resolve, reject) {\n","                let newScript = document.createElement(\"script\");\n","                newScript.onerror = reject;\n","                newScript.onload = resolve;\n","                document.body.appendChild(newScript);\n","                newScript.src = url;\n","            });\n","            }\n","            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n","            const iframe = document.createElement('iframe')\n","            iframe.style.cssText = \"width:0;height:0;border:none\"\n","            document.body.appendChild(iframe)\n","            const handshake = new Postmate({\n","                container: iframe,\n","                url: 'https://wandb.ai/authorize'\n","            });\n","            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n","            handshake.then(function(child) {\n","                child.on('authorize', data => {\n","                    clearTimeout(timeout)\n","                    resolve(data)\n","                });\n","            });\n","            })\n","        });\n","    "],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n","\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"data":{"text/html":["Tracking run with wandb version 0.16.1"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in <code>/content/wandb/run-20231210_062710-fuf9tsl1</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run <strong><a href='https://wandb.ai/yerini/huggingface/runs/fuf9tsl1' target=\"_blank\">llama-2-7b-nsmc</a></strong> to <a href='https://wandb.ai/yerini/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View project at <a href='https://wandb.ai/yerini/huggingface' target=\"_blank\">https://wandb.ai/yerini/huggingface</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[" View run at <a href='https://wandb.ai/yerini/huggingface/runs/fuf9tsl1' target=\"_blank\">https://wandb.ai/yerini/huggingface/runs/fuf9tsl1</a>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='374' max='1600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [ 374/1600 23:05 < 1:16:07, 0.27 it/s, Epoch 0.00/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>100</td>\n","      <td>0.999800</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.662000</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.635600</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1600' max='1600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1600/1600 1:39:34, Epoch 0/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>100</td>\n","      <td>0.999800</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.662000</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.635600</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.624600</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.612100</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.590900</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.623500</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.575600</td>\n","    </tr>\n","    <tr>\n","      <td>900</td>\n","      <td>0.605100</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.594400</td>\n","    </tr>\n","    <tr>\n","      <td>1100</td>\n","      <td>0.600400</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.608700</td>\n","    </tr>\n","    <tr>\n","      <td>1300</td>\n","      <td>0.591000</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.581000</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.599200</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.587100</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=1600, training_loss=0.6306900310516358, metrics={'train_runtime': 6006.4783, 'train_samples_per_second': 0.533, 'train_steps_per_second': 0.266, 'total_flos': 6.53458201903104e+16, 'train_loss': 0.6306900310516358, 'epoch': 0.02})"]},"metadata":{},"execution_count":26}],"source":["trainer.train()\n"],"id":"tFeEyBJnCi0C"},{"cell_type":"markdown","metadata":{"id":"76sRe172fGlm"},"source":["구글 코랩 T-4 GPU: 1:37:34 예상시간\n","- 총 1,600 스텝 필요\n","- 하지만 이보다 일찍 종료됨 약 900번 미만 스텝에서 종료됨\n","\n","시퀀스 길이 512의 경우\n","- 14.4 G / 15.0 G 사용\n","- 메모리 오버플로우 발생시 512보다 줄일 것"],"id":"76sRe172fGlm"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":516,"status":"ok","timestamp":1702196105028,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"3Y4FQSyRghQt","outputId":"f715a831-ca2d-4db2-dd28-5d03feaad25c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/gdrive/MyDrive/nlp/lora-llama-2-7b-nsmc'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":27}],"source":["script_args.training_args.output_dir"],"id":"3Y4FQSyRghQt"},{"cell_type":"code","execution_count":null,"metadata":{"id":"49f05450-da2a-4edd-9db2-63836a0ec73a"},"outputs":[],"source":["trainer.save_model(script_args.training_args.output_dir)"],"id":"49f05450-da2a-4edd-9db2-63836a0ec73a"},{"cell_type":"markdown","metadata":{"id":"652f307e-e1d7-43ae-b083-dba2d94c2296"},"source":["# 추론 테스트"],"id":"652f307e-e1d7-43ae-b083-dba2d94c2296"},{"cell_type":"code","execution_count":null,"metadata":{"id":"ea8a1fea-7499-4386-9dea-0509110f61af"},"outputs":[],"source":["from transformers import pipeline, TextStreamer"],"id":"ea8a1fea-7499-4386-9dea-0509110f61af"},{"cell_type":"code","execution_count":null,"metadata":{"id":"52626888-1f6e-46b6-a8dd-836622149ff5"},"outputs":[],"source":["instruction_prompt_template = \"\"\"###System;다음은 네이버 영화 리뷰들을 모아놓은 문장이다. 이를 분석하여 사용자가 작성한 영화 리뷰의 감정을 긍정 또는 부정으로 예측하라.\n","\n","### 리뷰 내용: {0} ### 분석 결과:\n","\"\"\"\n","\n","prompt_template = \"\"\"###System;{System}\n","###User;{User}\n","###Midm;\"\"\"\n","\n","default_system_msg = (\n","        \"너는 먼저 사용자가 작성한 영화 리뷰의 감정을 분석하는 에이전트이다. 이로부터 긍정 또는 부정을 판단해야 한다.\"\n",")"],"id":"52626888-1f6e-46b6-a8dd-836622149ff5"},{"cell_type":"code","execution_count":null,"metadata":{"id":"1919cf1f-482e-4185-9d06-e3cea1918416"},"outputs":[],"source":["def wrapper_generate(model, input_prompt):\n","    data = tokenizer(input_prompt, return_tensors=\"pt\")\n","    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n","    input_ids = data.input_ids[..., :-1]\n","    with torch.no_grad():\n","        pred = model.generate(\n","            input_ids=input_ids.cuda(),\n","            #input_ids = input_ids.to('cuda'),\n","            streamer=streamer,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            temperature=0.5\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    return (decoded_text[0][len(input_prompt):])"],"id":"1919cf1f-482e-4185-9d06-e3cea1918416"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1537373,"status":"ok","timestamp":1701343970452,"user":{"displayName":"장예린","userId":"14823696343626614035"},"user_tz":-540},"id":"Lxptc3T1kDSO","outputId":"1604b12c-871e-4d41-f739-b7e54ad03091"},"outputs":[{"name":"stdout","output_type":"stream","text":[";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍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= {i: wrapper_generate(model=base_model, input_prompt=prompt_template.format(System=default_system_msg, User=example[\"document\"]))for i, example in enumerate(valid_dataset)}"],"id":"Lxptc3T1kDSO"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":398,"status":"ok","timestamp":1701265256607,"user":{"displayName":"장예린","userId":"08326973126538993419"},"user_tz":-540},"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6","outputId":"642ae37a-4c0c-4385-f75c-20a8f0957242"},"outputs":[{"name":"stdout","output_type":"stream","text":["부정\n"]}],"source":["print(eval_dic[8])"],"id":"fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6"},{"cell_type":"markdown","metadata":{"id":"3f471e3a-723b-4df5-aa72-46f571f6bab6"},"source":["# 미세튜닝된 모델 로딩 후 테스트"],"id":"3f471e3a-723b-4df5-aa72-46f571f6bab6"},{"cell_type":"code","source":["from transformers import pipeline, TextStreamer"],"metadata":{"id":"U5AcpecYRRlC"},"id":"U5AcpecYRRlC","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**프롬프트 정의**\n","\n","\n","> 사용자 영화 리뷰의 예측을 생성하기 위한 프롬프트 정의함.\n","\n"],"metadata":{"id":"qVr_AMtJa-AF"},"id":"qVr_AMtJa-AF"},{"cell_type":"code","source":["instruction_prompt_template = \"\"\"###System;다음은 네이버 영화 리뷰들을 모아놓은 문장이다. 이를 분석하여 사용자가 작성한 영화 리뷰의 감정을 긍정 또는 부정으로 예측하라.\n","\n","### 리뷰 내용: {0} ### 분석 결과:\n","\"\"\"\n","\n","prompt_template = \"\"\"###System;{System}\n","###User;{User}\n","###Midm;\"\"\"\n","\n","default_system_msg = (\n","        \"너는 먼저 사용자가 작성한 영화 리뷰의 감정을 분석하는 에이전트이다. 이로부터 긍정 또는 부정을 판단해야 한다.\"\n",")"],"metadata":{"id":"nzxPwBS5RY1P"},"id":"nzxPwBS5RY1P","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**wrapper_generate 함수**\n","\n","\n","> 사용자 영화 리뷰에 대한 입력 프롬프트 생성.\n","\n"],"metadata":{"id":"zkWiQd1NaFZz"},"id":"zkWiQd1NaFZz"},{"cell_type":"code","source":["def wrapper_generate(model, input_prompt):\n","    data = tokenizer(input_prompt, return_tensors=\"pt\")\n","    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n","    input_ids = data.input_ids[..., :-1]\n","    with torch.no_grad():\n","        pred = model.generate(\n","            input_ids=input_ids.cuda(),\n","            streamer=streamer,\n","            use_cache=True,\n","            max_new_tokens=float('inf'),\n","            temperature=0.5\n","        )\n","    decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n","    return (decoded_text[0][len(input_prompt):])"],"metadata":{"id":"nPNJNH5TRcYQ"},"id":"nPNJNH5TRcYQ","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**양자화 설정**\n","\n","\n","> 4비트로 양자화함.\n","\n"],"metadata":{"id":"XwPG_xZ3aXaL"},"id":"XwPG_xZ3aXaL"},{"cell_type":"code","execution_count":null,"metadata":{"id":"a43bdd07-7555-42b2-9888-a614afec892f"},"outputs":[],"source":["bnb_config = BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type=\"nf4\",\n","    bnb_4bit_compute_dtype=torch.bfloat16,\n",")"],"id":"a43bdd07-7555-42b2-9888-a614afec892f"},{"cell_type":"markdown","source":["**모델 불러오기**\n","\n","> 미세튜닝된 모델을 4비트로 양자화하여 저장된 모델을 가져옴.\n","\n"],"metadata":{"id":"m2z3YCa1aoP4"},"id":"m2z3YCa1aoP4"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["1bf559f3bca24143856ad4ae301fbbdd","f9a6930ee95e47ffb42bdc3b261ca9ee","d92a0b5ae4eb41f1b0c979bdbdd4d9e8","5006257fb8974369b5f5d7f06618714c","a1b69b152643481b809dbabd0d861bfe","910cd365fa204024b95359cdda0733a4","acbb7b75e0a14062ae38ebd027277479","117d8169a35d412f8ae31bf1f605f878","484c97490f354e21a62b6e29423c3f30","88b0fd56cdeb4ab39ba32c88a751461c","ffd4cb3c6fcf456695c7acf1f667564b"]},"id":"39db2ee4-23c8-471f-89b2-bca34964bf81","outputId":"e0853c55-6dc7-4683-865e-ab4869bdd232","executionInfo":{"status":"ok","timestamp":1702196292221,"user_tz":-540,"elapsed":76093,"user":{"displayName":"장예람","userId":"15119681621712404836"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1bf559f3bca24143856ad4ae301fbbdd"}},"metadata":{}}],"source":["trained_model = AutoPeftModelForCausalLM.from_pretrained(\n","    script_args.training_args.output_dir,\n","    quantization_config=bnb_config,\n","    device_map=\"auto\",\n","    cache_dir=script_args.cache_dir,\n",")"],"id":"39db2ee4-23c8-471f-89b2-bca34964bf81"},{"cell_type":"markdown","source":["**토크나이저 초기화**\n","\n","\n","> 지정된 모델에서 미리 훈련된 토크나이저를 로드함.\n","\n"],"metadata":{"id":"aZSH97Zmb3fK"},"id":"aZSH97Zmb3fK"},{"cell_type":"code","execution_count":null,"metadata":{"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},"outputs":[],"source":["tokenizer = AutoTokenizer.from_pretrained(\n","    script_args.model_name,\n","    trust_remote_code=True,\n","    cache_dir=script_args.cache_dir,\n",")\n","\n","if getattr(tokenizer, \"pad_token\", None) is None:\n","    tokenizer.pad_token = tokenizer.eos_token\n","tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training\n","trained_model.config.pad_token_id = tokenizer.pad_token_id"],"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},{"cell_type":"markdown","source":["**데이터셋 생성**\n","\n","\n","> 추론에 사용할 테스트 데이터 생성함.\n","\n"],"metadata":{"id":"qHcNDlVObcoa"},"id":"qHcNDlVObcoa"},{"cell_type":"code","execution_count":null,"metadata":{"id":"b5O6GD2BVr67"},"outputs":[],"source":["valid_dataset = create_valid_datasets(tokenizer, script_args)"],"id":"b5O6GD2BVr67"},{"cell_type":"markdown","metadata":{"id":"X1tRCa4EiYXp"},"source":["추론 과정에서는 GPU 메모리를 약 5.5 GB 활용"],"id":"X1tRCa4EiYXp"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1899461,"status":"ok","timestamp":1702198228843,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999","outputId":"8abff7db-796b-4a9c-8720-b366eb0134aa"},"outputs":[{"output_type":"stream","name":"stdout","text":[";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";긍정\n",";긍정\n",";긍정\n",";부정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n",";긍정\n",";부정\n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= {i: wrapper_generate(model=trained_model, input_prompt=prompt_template.format(System=default_system_msg, User=example[\"document\"]))for i, example in enumerate(valid_dataset)}"],"id":"e374555b-9f8a-4617-8ea7-c1e6ee1b2999"},{"cell_type":"markdown","source":["**모델 평가 및 결과**\n","\n","\n","> 정확도 및 혼돈 행렬로 모델을 평가함.\n","\n"],"metadata":{"id":"PtEfo3dHg6b2"},"id":"PtEfo3dHg6b2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":525},"executionInfo":{"elapsed":647,"status":"ok","timestamp":1702199173554,"user":{"displayName":"장예람","userId":"15119681621712404836"},"user_tz":-540},"id":"zGWyzs1tbSJH","outputId":"aea1608e-5bb1-4329-faed-4c6a9f582816"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.874\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 400x200 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAADpCAYAAAAqJfzJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwfUlEQVR4nO3dd1yV5f/H8dc57OHCxUocdRChHAiKuUeaoxzRMDHNkWGujBx907TpnlmpWFrZUNw5CjMnzgrLhTMVFUVFgYPM+/cHP04S50ZA4Mjt5/l4nMcD7vu+7nPd58D7XOe6r/u6dYqiKAghhNAsvaUrIIQQomRJ0AshhMZJ0AshhMZJ0AshhMZJ0AshhMZJ0AshhMZJ0AshhMZJ0AshhMZJ0AshhMZJ0GtMVlYWX331Fd26deOJJ57A29sbb29vi9Ql57kvXrxoked/2I0dOxZvb2/mzZtn6aoIC7O2dAXKgvPnz7NixQr27t3LxYsXuX37Nvb29nh4eNCwYUO6du1KQECApasJwKeffsr8+fPR6XQ89thjODs7W7pKZULbtm2JjY0FICgoiK+++kp1W0VRaNeunWn75s2bEx4eXiz1iIyM5NixYwQGBtKkSZNi2acQEvT5yMzMZPr06SxbtoyMjAwAPD098fDwIDk5mXPnznHixAm+//57AgIC+OabbyxaX0VR+PbbbwGYOXMmnTt3tmh9atWqBYCNjY1F61FY+/bt49KlS7i7u5tdv3fvXlPIF7fIyEhWr17NG2+8cd9BX7VqVWrVqkWlSpWKqXairJKgV6EoCiNGjOCXX37BxsaG0NBQevfuTdWqVU3bpKSksGPHDr744gsOHDhgwdpmu3HjBjdv3gSgTZs2Fq4NbN682dJVKLQ6depw+vRpVq9ezdChQ81us2rVqlzbPqhGjx7N6NGjLV0N8QCQPnoVS5YsMYX8okWLGDFiRK6QB3BwcKBjx45EREQwYsQIC9X0X3fu3DH97ODgYMGalF3du3dHp9OxZs0azE3smpSUxC+//EKFChVo166dBWooROFJ0JthNBpZuHAhAAMGDCAoKCjf7XU6HaGhoXmWK4rChg0b6N+/P02aNMHPz4+WLVsyevRojhw5YnZfq1atwtvbm5CQENPvwcHBNGzYkEaNGhESEsLu3btzlbl48SLe3t60bdvWtCznROjdJ+P+u29zQkJC8Pb2NrVa7xYVFcXQoUNp3rw5vr6++Pv70759e4YOHcrKlSvzbH+vk7E7duxgyJAhNGvWDD8/P5588klCQ0OJiooyu/2+fftyHefWrVsJCQmhcePGNGjQgODgYH766SfVYysIDw8PAgMDOX/+PAcPHsyzfuPGjaSkpNC1a1dsbW1V93P27FkWLlxISEgIbdq04fHHH6dx48a8+OKLLFu2jLS0tFzb57yHq1evBmD+/Pm53sO739t58+bh7e3N2LFjSUtL4/PPP6dbt240bNgw14l3cydjMzIyePHFF/H29jb7NwvZ77OPjw++vr78/vvvBXvhxANNgt6M7du3k5CQgF6vp2/fvkXaR0ZGBiNGjGD06NHs2bMHe3t7vL29MRqNbNiwgeDgYH744Yd89zF+/HjGjRvHtWvXqFWrFoqisH//fgYOHEhkZKRpOzs7Oxo1aoSfn59pWaNGjUwPNze3Ih3D3VasWEG/fv2IjIwkNTWVRx99FA8PD27fvk1kZCRz584t1P4+/PBDBg0axLZt2wCoW7cumZmZbN26lX79+jF79ux8y8+fP5/Q0FDOnDlDjRo1sLGx4fDhw7z55pv3fa6kZ8+eAGY/7HKW5WyjZtasWcyYMYO///4bGxsbvL29KV++PH/88QcffvghAwYMyBX2Oe9h5cqVAXBzc8v1Ht793uZITU0lJCSEWbNmcefOHWrXrn3Pk+/W1tbMmDGD8uXLs3XrVr7++utc669fv05YWBhZWVkMGzaMRo0a5bs/UUYoIo/3339fMRgMSteuXYu8j3nz5ikGg0GpX7++8vPPP5uWp6amKh9//LFiMBgUHx8f5c8//8xVLiIiQjEYDIqvr68SGBio7Nq1y7QuOTlZGTp0qGIwGJQ2bdooWVlZucpeuHBBMRgMisFgMFunnH336dNHtd59+vRRDAaDEhERYVqWkZGhBAYGKgaDQVm2bJmSnp6eq8ypU6eUpUuX5tlXTl0uXLiQa/mqVatMx798+XIlMzPT9DxLlixRvL29FYPBoGzatClXub1795pemyeeeEJZt26daV16erry3nvvKQaDQWnQoIGSmJioeozmtGnTRjEYDMqGDRsUo9GoNGzYUGnQoIGSlJRk2ub06dO5/i7mzp2rGAwG5dVXX82zv19++UWJjo7O8x6dOnVKef755xWDwaB88cUXecqNGTNGMRgMyty5c1XrmvO8Pj4+SuvWrZXDhw+b1qWkpBRoX5s2bVIMBoPi5+enHD16VFEURcnKylJeffVVxWAwKH379jW9L6Lskxa9GXFxcQA88sgjRSpvNBr58ssvAXjjjTfo0KGDaZ2trS1jx46lcePGZGZm8tlnn5ndR3p6OuPHj+fJJ580LXN0dGTixInY2NgQGxvLiRMnilS/wrpx4wYJCQmUL1+ekJAQrK1zn8OvU6dOob75LFiwAIAXXniBl156Cb0++8/QysqK/v37061bNyB7qKg56enpvPbaa6btILulOnbsWFxcXDAajezbt69Qx3g3BwcHOnfujNFozHVCOac136tXr3vuo3379jzxxBPodLpcy+vUqcPUqVMBTN00RZWZmcmMGTN4/PHHTcvs7e0LVLZTp0688MILpKWlMWrUKIxGI+Hh4ezatQsXFxemTZtmel9E2SfvpBlJSUlAdrAWxcGDB0lKSsLOzo6XXnrJ7DavvvoqAHv27MnTXwtQrlw5nnnmmTzLq1atioeHB5A9vr80VK5cGXt7exITE9m+fft97ev06dOmevfv39/sNgMGDAAgJiaGS5cumd2md+/eeZbZ2dlRr1494P5fmx49egD/hnFmZiZr167FxsbG7PtizvXr11m2bBlvvfUW/fv3p3fv3rz00kuMHTsWyO7Hv/sEemHVqVPnvrpW3nnnHQwGA2fPnmXo0KHMnj0bnU7HJ598QrVq1Yq8X/HgkeGVZuT0cxqNxiKVP3v2LJB9Ys/JycnsNgaDAcjuZ42NjTWNOc/h5eWVpzWYo0qVKpw7d47k5OQi1a+w9Ho9r776KgsWLGDw4MEYDAaCgoJo0KABAQEBeUYj5SfntbG3t6dGjRpmt3n00UexsrIiMzOTM2fO5BnPXqlSJSpWrGi2bE4f9/2+Nv7+/tSsWZODBw9y/vx5zp49y9WrV+nQoQMuLi73LL9582bGjRuX79+QoijcunWrwK3w/3r00UeLVC6HnZ0ds2bNolevXuzZsweAfv360apVq/var3jwSNCbUb16dQAuXLhQpPI5IVOlShXVbe5uMZkLpfy+TeR8pVZK8b7uw4cPx93dnW+++Ybjx48TExPD0qVL0el0BAUFMWbMGOrWrXvP/eQca04gm2NtbU2lSpWIj4+36GvTs2dPZs6cyapVqzhz5oxp2b1cvHiRsLAw0tLSePrppwkJCaF27dqUK1cOa2trsrKy8PHxAbK7oYqqqN8471ajRg08PT05deoUAM8///x971M8eKTrxgx/f38ATp06xfXr1wtdPqcVHx8fr7rN1atX82xf0nK+IeQXgmotUJ1OR3BwMGvXriUqKopPP/2UV155hSpVqrBnzx5eeeUV07mN/OQca36va0ZGhunCr9J6bczp3r07er2elStX8uuvv1KlShVatmx5z3IbN24kLS2NJ554gpkzZ+Lv70+lSpVM5zYSEhJKuOYFN336dE6dOmX6gBw3bpzpKnChHRL0ZrRs2ZKKFSuSlZXFsmXLCl2+du3aAMTGxqp2IcTExADZX59z+txLWs5FVPl9AP3zzz/33I+Liwvt27dn/PjxbN68GU9PTxISEgo0hj3ntblz545qP/qpU6fIzMwEsvuhLaV69eo0a9aMa9eukZ6ezjPPPJPnRLQ5OdcN+Pv7mz2h+eeff6qWVeuuKwm//fYby5Ytw8bGhiVLluDh4UF0dPQ9h7aKskeC3gwnJycGDhwIQHh4uOoFPDkURck1esbf3x9nZ2dSU1P57rvvzJbJGZXTrFmzfC+8KU41a9YEsoPIXNivW7eOxMTEQu3T2dnZdJFOQVr0tWvXxsvLC/j3NfivnOUGg6FYrgG4H3379iUoKIigoCCee+65ApXJ6XO/du1annWKorBkyZJ7lk1JSSlCbQsuLi6OsWPHoigKb775JkFBQcyYMQNra2sWL15s6rMX2iBBr2LgwIG0bduW9PR0Bg0axNy5c/P846amphIZGUlwcHCuVpCjo6NpRMn8+fNzXdyUlpbG1KlTOXDgAFZWVrz++uulcjyQHZweHh6kp6czefLkXGESFRXFRx99ZHYCslOnTjF+/HgOHjxIVlZWrnW7d+82fRDePcwvPznH/MMPP/D999+bupKysrJYunQpa9euBVCda6Y0tWrViq+++oqvvvqqwN8uAgMDgewTsr/99ptpeVJSEu+88w6HDx9WLZvzIXjo0CGzo7GKQ1ZWFmFhYdy8eZMWLVqY/lYbNmzIsGHDUBSFt99+u0jdluLBJCdjVeh0OubNm8fUqVP55ptv+PTTT1mwYAGenp5UqlSJ5ORkLl68SGpqKgBNmzbNVX7IkCHExMSwZcsWhg4dipubm2m0TGJiInq9nokTJ1K/fv1SOya9Xs/48eMZNmwYW7ZsYefOndSqVYubN29y6dIlevXqxYULF9i/f3+ucunp6URERBAREYGDgwM1atTA1taWuLg407mGdu3aFXi2zB49enD06FGWLVvGxIkTmTdvHm5ubsTGxnLjxg0g+/Xr1KlT8b4ApaRt27YEBgayf/9+XnvtNTw9PalQoQJnzpwhNTWVjz/+mDFjxpgt27FjR2bPns2ff/5Jq1at8PLywsbGhipVqjBr1qxiqd8XX3zBvn37qFKlClOmTMnVXTR48GCioqLYu3cvY8eOZeHChaXanSRKhrTo82Ftbc348ePZtGkTgwYNws/Pj6SkJI4ePUpcXBy1atXipZdeYvny5SxdujRP2Tlz5jB9+nSaNm2K0Wjk+PHjODg40LVrV1asWMELL7xQ6sfUvn17wsPDTa3OM2fOUKlSJT744AM++ugjs2Vq1qzJhx9+SNeuXXFzc+PKlSscO3aM9PR0mjVrxpQpU5g/f36hLrB55513WLhwIW3atCErK4tjx46h0+lo164dX331FaNGjSqW47UEvV7PokWLGDx4MJ6ensTFxXH58mWaNGnC0qVL6d69u2pZNzc3wsPDadmyJYqiEB0dzf79+4mOji6Wuv3++++m+xVMmTIlz+gnvV7P1KlTqVSpEjt27Mh3Xn5RduiU0hyjJ4QQotRJi14IITROgl4IITROgl4IITROgl4IITROgl4IITROgl4IITROgl4IITROrowVQmiCQ6PhqutSfi/cPY21pswFvUPDNyxdBVFKUv6Yzx2ZMfehYX+/aSRTNagqc0EvhBBm6a0sXYMHlgS9EEIbJOhVSdALIbRBJ2NL1EjQCyG0QVr0qiTohRDaIEGvSoJeCKENMupGlQS9EEIb9BJnauSVEUJog5V03aiR09RCCG3Q6dQfxUxRFPr27Yu3tzfe3t6cPn3a7Hbnz58nLCyM5s2b8/jjj9OhQwemT59OcnJyvvv+7rvv6NmzJw0aNCAwMJBXX32VqKioItdXgl4IoQ16K/VHMfvhhx/Yt29fvjdOP3LkCN27d2fdunVUq1aNdu3akZmZyaJFi3jxxRdJTEzMU0ZRFMLCwnjvvfc4d+4cLVq0wM/Pj71799K/f39WrFhRpPpK0AshtKGUgv7KlStMmzaNFi1a4O7ubnabzMxM3nzzTZKTkxk9ejSrVq1i9uzZbN68mTZt2hATE8O0adPylFu7di3r16/H09OTTZs2MW/ePJYsWcLSpUuxtrZm0qRJxMbGFrrOEvRCCG3Q6dUfxWjChAlkZWUxadIk1W22bt3KuXPnMBgMDBo0yLTc1taWyZMnY21tTUREBDdv3sxVLjw8HICwsDCqV69uWh4QEEBwcDDp6eksXbq00HWWoBdCaEMptOjXrFnD9u3bGTFiBB4eHqrbbdu2DYCOHTvm6d6pVq0a/v7+ZGRksH37dtPyixcvEhMTg52dHW3bts2zz86dOwPZHyKFJUEvhNCGEg76+Ph4Pv74Yx5//HH69u2b77bHjh0DwM/Pz+x6X19fAI4fP25alvPzY489hq2tbZ4y9erVA7I/EJKSkgpVdwl6IYQ2lHDXzeTJk0lKSuKDDz5Ar89/n5cuXQLA1dXV7Pqcbpmc7QpSxsnJiXLlyuUpVxAyjl4IoQ35tNzbtWuXb9F7dYds2bKFLVu2MHjwYOrWrXvPqhiNRgAcHBzMrndycgLINczyXmUAHB0dSUxMzHd4pjkS9EIIbSihuW4SEhKYPHkyXl5evPFG2bzxkQS9EEIb8hnTXpQTmDk+/vhj4uPjmT59OnZ2dgUq4+joyK1bt0hJSTG7PqdFntOyzykDqJaBf1v9d5crCAl6IYQm3KvfvKi2bt2KnZ0dCxYsYMGCBbnWXbt2DYAxY8bg4ODAyy+/TKdOnXB3d+fWrVtcuXLFbFdPXFwcQK5x+Dk/X7lyxWw9kpOTTRdZqY3fVyNBL4TQBJ2+5GavTE1NZf/+/arr//rrL+DfcwE+Pj4cO3aMv//+m9atW+fZ/siRIwC5PgRyfj558iRpaWl5Rt4cPXoUAE9PT5ydnQtVfxl1I4TQBJ1Op/q4HwcPHuTEiRNmHzlj6Tdu3MiJEyfo168fAG3atAGyT+IqipJrf1evXuXQoUNYW1vTsmVL03JPT08MBgOpqan8+uuveeqxceNG4N4nls2RoBdCaIJer1d9lLa2bdtSs2ZNYmJiWLRokWl5WloaEyZMICMjg169euHi4pKr3IABAwCYNm2aqXsH4MCBA6xYsQIbGxteeeWVQtdHum6EEJpQkl03hWVtbc2MGTMICQlhxowZbN68GS8vL6Kjo4mNjcVgMBAWFpan3LPPPsvOnTvZsGEDnTt3plmzZhiNRqKiosjKyuL999/P94pc1foUx0EJIYSl3W8XTXHz8/NjzZo1zJs3j6ioKGJiYnB1dWXgwIGEhoaaHTmj0+mYPn06/v7+rFixgh07dmBjY0OTJk0YPHgwQUFBRaqLTvlvB9IDzqFh2RzHKgov5Y/53MmwdC1EabG/z2Zn5b7fqa67vuyl+9t5GScteiGEJjxIXTcPGgl6IYQmPGhdNw8SCXohhCZIi15dkYP+2rVrXLp0CVtbW+rUqWN2Wk0hhCgtlhhGWVYUOuhPnjzJu+++S3R0tGmZra0tzz33HGPGjJHAL4S3B3Rk0hvdOHLqEo2DPzK7TQVnBw6vnUA1l3L0DlvM6sg/Teta+D/Gz4tHmC3Xqu909v91rgRqLYrDsaNH+OzTefzx+++kpaXi4fkIvYKf5+U+2fOcL174Ob9t+5WLF86TnJyMq6sbLVq2YuBrr+cZey2ySdeNukIFfVxcHH369OH27du5rvZKTU1l+fLlxMfHM2fOnGKvpBZ5VKvI2wOeIsmYmu92E0K74Gif/4fnp8u3cfDI+VzLTl+4dt91FCVjz+5dDB86hLo+9Rg8JBRHR0cuXDjP1bvmODl65AjedevS6enOODo5cfbMGVat/JGdO7bzQ8Qa0wRY4l/SdaOuUEG/ePFibt26Rb169Rg3bhz16tUjKSmJrVu3MmPGDH7++WeOHj1quhOKUPfxmz3Yf/gcVlZ6Klc0PxNdvTpuDHquBR8t2sTE0K6q+9r9x+lcLX3x4EpKSuJ/48bQolVrZsyaq9rdMHPOvDzL6tdvwOhRw9n+2zae7tylpKta5kjXjbpCvTK7du2ifPnyLF68mICAAJycnKhevTq9e/fm7bffRlEUdu3aVVJ11YwnG9WhR7sGhE2PyHe76WHPsW5bNLt/P33PfTo72mFlJX/oD7qNP63n+vV4hg0fhV6vx2g0kpWVVaCy7v9/RWRi4u2SrGKZVVJz3WhBoZLh8uXL1K9f32wfYfv27U3bCHV6vY6ZY4L5ck0UR06p3w6sZ/uGNK1fi/Gz19xzn1+814dru2eQsHcWmxcOp1G9GsVYY1Gc9kVF4ezszNWrcTzTpSNBAQ1pFujPB5MnkpqauxtPURRu3rxB/LVr/H7oIFM++gArKysCAgItVPsHm06vU3087ArVdXPnzh2qVKlidl3lypUB8vyxitwGPdeCGm4udBkyX3UbezsbPn6zB/O+3cb5yzfwcq9sdrv0jAxWR/7B5l1HuJ6QjE9tV0aEtCMyfCRt+s0k+sTFkjoMUUTn/zlHRmYmI4aF0qPncwwfOZqDB/bz3bdfk3g7kSnTZ5q2vR4fT7vWzU2/V3d15eOp06lVu44lqv7Ak5a7umIfR1/GZlQoVS4VnHj39S58smgz8TfV7+L+Vv8O2FhbMTV8S7772xt9lr3R4abff9r+F6si/+DAD+OZPOwZnn1jQT6lhSUYU4zcSUkh+IUXGTv+fwC07/AU6elprPzxB0KHDcfLqyYAFSpU4IvFX5KamsrxY0fZGvmL6Q5DIi/po1dX6KCPj4/nwIEDRVofEBBQ2KfTlIlDu3LzdjILvtuuuk0NNxdG9W3PyE9+JDklrdDPceZCPBu2H+bZtvXR63VkZckH74PEzs4egKc75z653rlLN1b++AOH//zTFPQ2trY0DWoGQKvWbWjSNIhX+ryEi0tlWrVuU6r1Lguki0ZdoYN+165dqidcdTqd6nqdTme6Q8rDqE6Nqgzo+SRh0yNwq1rBtNze1hobaytquLmQmHyHCaFduHQtgR0HT1LDLftciGuV8gBUqeRMDTcXLly5me83p4tXbmJna4OTgx2JyXdK9sBEoVStVo3Tp06aujpz5Jz3un37lmrZBg0bUbVqVTZuWC9Bb4Z03agrVNAX9j6F4l/uVStiZaVn5phgZo4JzrP+xMbJzP92G4+4uvBojWoc/2lSnm3mjn8RANcWYdxKUr+BcC3PKqTcSbvnGH1R+urV82Xvnt3ExcVRs1Zt0/KrV68CUKlS/hdDpaamkZSUWKJ1LKv00qJXVaigN3d7K1EwR09f4vlRC/Msnzi0K+Wc7Hlr6krOXIyngrM9lSvmvh9kvUfdeG9oN2Z8+Qv7Dp8l+U52gFep5Jynr/9xgwddWj3Olt1H5XzJA+ipTk+zZPFC1qxaSZOm/84tvjpiJdbW1jQODMRoNKLT6XBwcMhVNvLnLdy+fYt6vn6lXe0yQYJencx1U0quJySz/rfDeZa/8XL2V3Bz63IkJGa33g8d/SfXdl9/0p+U1HT2Rp/l2o1EfGq78mqvJzHeSePduWuL+QhEcfDxqUf3nr1YsyqCjMxMGjcO4OCB/fy8ZTMDBr1GtWrVOX7sGK8N7EfHTp2pWbs2ep2eI0f+ZuOGdbh7ePBySF9LH8YDSXpu1MlcN2XY+t8O8+LTAQzv05byTvbEJySxdms0Hy7cyJkL8ZaunlDxvwmTcHNzZ+3qVfwaGYm7uzthY8bRp28/AKq7Vqddh47s37+XdWvXkJGRjpu7By++9DIDXxtCxYqVLHsADygrK0l6NYW6w1RcXBzPPPNMnrluIPtEyFNPPVXic93IHaYeHnKHqYfL/d5h6vF3f1Fd99f7He5v52VcoQae5sx14+Pjw9dff82hQ4fYvn07EyZMwNHR0TTXjRBClDaZAkFdoT5D757rJmc4mJOTE71798bKyoqJEyeya9cumdRMCFHq5GSsOpnrRgihCXq9TvXxsJO5boQQmiBdNOpkrhshhCZIy12dzHUjhNAECXp1MteNEEITpOdGncx1I4TQBGnRq5O5boQQmiBBr67YT8YKIYQlyKgbdRL0QghNkBa9Ogl6IYQmSNCrk6AXQmiCdN2ok6AXQmiClbToVUnQCyE0Qbpu1EnQCyE0QXJenQS9EEITpEWvToJeCKEJVnIyVpUEvRBCE2TUjToJeiGEJsioG3US9EIITZA+enUS9EIITZCeG3US9EIITZCTseok6IUQmiB99Ook6IUQmiCjbtRJ0AshNEFOxqqToBdCaIJ03aiToBdCaILEvDoJeiGEJkiLXp0EvRBCE6SPXp0EvRBCE/Qy6kaVBL0QQhOkRa+uzAV9yh/zLV0FUYrsy9xfqLAUuTJWXZn7N7qTYekaiNJibw0OT75j6WqIUpKy+8P7Ki8NenVlLuiFEMIcGXWjToJeCKEJVnpL1+DBJUEvhNAEGXWjToJeCKEJVpLzqiTohRCaIH306iTohRCaIDmvToJeCKEJ0qJXJ0EvhNAEuWBKnQS9EEITpEGvToJeCKEJ0nWjToJeCKEJcsGUOgl6IYQmyAVT6iTohRCaUFIt+vT0dPbt28dvv/3Gvn37uHDhApmZmbi6utK8eXMGDhyIh4eH2bLnz59n3rx5REVFcevWLVxdXenYsSOvv/46Tk5OZssoisL333/PihUrOHPmDLa2tvj5+TFo0CCCgoKKdAw6RVGUIpW0EJm98uEhs1c+XO539soFe86prgttVrPI+92zZw/9+/cHwM3NDV9fXwAOHz7M1atXcXZ2ZvHixTRs2DBXuSNHjhASEkJycjK+vr7UqFGDw4cPExsbi8FgYPny5ZQrVy5XGUVRCAsLY/369Tg5OfHkk0+SnJzM3r17ycrK4v333yc4OLjQxyAteiGEJpTUuVidTkfHjh3p379/rjBPTU3lvffeY9WqVYwePZotW7ZgY2MDQGZmJm+++SbJycmMHj2awYMHA5CWlsbw4cPZtm0b06ZNY/Lkybmea+3ataxfvx5PT0+WL19O9erVAThw4AD9+/dn0qRJNGvWTPUbhBo5fSGE0AQrvU71cT+CgoKYO3dunha7nZ0dEydOpFy5csTGxvLHH3+Y1m3dupVz585hMBgYNGiQabmtrS2TJ0/G2tqaiIgIbt68mWuf4eHhAISFhZlCHiAgIIDg4GDS09NZunRpoY9Bgl4IoQklFfT5sbe3p2bNmgBcvXrVtHzbtm0AdOzYEd1/ThJXq1YNf39/MjIy2L59u2n5xYsXiYmJwc7OjrZt2+Z5rs6dOwPZHyKFJUEvhNAEfT6PkpKZmUlsbCwAVapUMS0/duwYAH5+fmbL5fTzHz9+3LQs5+fHHnsMW1vbPGXq1asHZH8gJCUlFaqeEvRCCE3Q63Sqj5Kydu1abty4gYuLC40aNTItv3TpEgCurq5my+V0y+RsV5AyTk5OppO3d5crCDkZK4TQhPzmumnXrl2+ZYvSHXLx4kWmTJkCwKhRo3K1wo1GIwAODg5my+YMrUxOTi5wGQBHR0cSExNzlSsICXohhCaU5vVSSUlJhIaGkpCQQKdOnXj++edL78mLQIJeCKEJ+bXoi9JiV5Oamsrrr7/OiRMnCAoKYtq0aXm2cXR05NatW6SkpJjdR06L/O6LphwdHQFUy8C/rX61i63USB+9EEITSqOPPj09nWHDhrF//34aNGjAggULzJ44dXd3B+DKlStm9xMXF5dru4KUSU5OJjExMU+5gpCgF0Jogk6nU30Uh6ysLMLCwti+fTt169Zl4cKFplb4f/n4+ADw999/m11/5MgRAOrWrWtalvPzyZMnSUtLy1Pm6NGjAHh6euLs7FyoukvQCyE0wUqnU33cL0VR+N///semTZuoVasWS5YsoUKFCqrbt2nTBoAtW7bw31lmrl69yqFDh7C2tqZly5am5Z6enhgMBlJTU/n111/z7HPjxo3AvU8smyNBL4TQBL1O/XG/PvnkEyIiIvD09GTp0qVUrlw53+3btm1LzZo1iYmJYdGiRablaWlpTJgwgYyMDHr16oWLi0uucgMGDABg2rRppu4dyJ4CYcWKFdjY2PDKK68Uuv4yqZl4YMmkZg+X+53UbP1fcarruj1eXXXdvURGRjJ06FAAmjRpoto/3r59e9q3b2/6/e+//yYkJASj0Yivry9eXl5ER0ffc1Kzt956iw0bNuDs7EyzZs0wGo1ERUXJpGZCCFFSF0bdvn3b9PO+fftUt/Pw8MgV9H5+fqxZs8Y0TXFMTAyurq4MHDiQ0NBQsyNndDod06dPx9/fnxUrVrBjxw5sbGxo0qQJgwcPLp1pips3b16kJ4HsA9i5c2eRy+eQFv3DQ1r0D5f7bdFvPnJNdV0n36r3te+yrlAt+vj4+CI/UXGd+daqY0eP8Nmn8/jj999JS0vFw/MRegU/z8t9+gKweOHn/LbtVy5eOE9ycjKurm60aNmKga+9nqefTzyY3u7bmkmvdeDImTgah8wFwMHOhr5dGtG1hQ++tV1xdrTl9MXrLFl7gPB1B8jKyt0Oq+3hwgevd6S1fx3sbK3488QlJi2OZMfvZy1xSA8UiRh1hQr6wlx0kJGRwYoVK/j2229JSUmRoM/Hnt27GD50CHV96jF4SCiOjo5cuHCeq3eNpz165AjedevS6enOODo5cfbMGVat/JGdO7bzQ8Qa1WFe4sHgUbU8b/dtRZIxNdfyWu6VmDmqK9sOnWHeD7u4nZxKhyaPMTfsWQL9HmHQBxGmbT2rVeC3L4aQmZXFrO92YkxJI6SLPxtm9efp4UvYHX2ulI/qwSK3ElRX7CdjFUVhzZo1fPrpp8TGxqIoCq1bt2bkyJG5xowWlda6bpKSknimc0fqN2zIjFlz0esLPhAq8uctjB41nE+mzeTpzl1KsJaWoaWum2WTXqBKRSes9DoqV3QytegrV3Ckmoszx85ezbX95+N68kpXf3yfn8GZ2BsAzHqzGwOeDcA/ZC4nz2d/u3awsyF6+UiuJSTz5IAFpXtQxex+u25+PX5ddV3buvmPktG6Yh1euXHjRrp06cL48eO5ePEiQUFB/Pjjj3z++efFEvJatPGn9Vy/Hs+w4aPQ6/UYjUaysrIKVNb9/+8yk5h4+x5bCkt6sn5NerT2JWzOT3nWXb9lzBPyAOt2ZF8cU7dmtVz7iY65bAp5gJTUdH7afZxGdT2o4/lwh5lOp/542BXLqJtff/2VOXPmEBMTg6Io+Pv7M2LECAIDA4tj95q2LyoKZ2dnrl6NY+TwUP45dw4HB0e6PvMMYWPGY2dnZ9pWURQSEm6SmZHJ+fP/MGfmdKysrAgIkNf5QaXX65g5qitfbjjEkTPqw//+q7pL9pWP1xP+naXQztaKhMS886AY72RfRdnI253TF9VbtVpXHBdGadV9Bf3u3buZM2cOf/31F4qi4Ovry8iRI2nRokVx1U/zzv9zjozMTEYMC6VHz+cYPnI0Bw/s57tvvybxdiJTps80bXs9Pp52rf8d+VTd1ZWPp06nVu06lqi6KIBB3QOp4VqRLiOXFLiMjbUVb7zQjLOxNzh4PNa0POZ8PE8+URNnR1uSjP9eIt/sCS8A3KuWL76Kl0E6JOjVFCnoDx48yJw5czh48CCKovDYY48xYsSIXGNIRcEYU4zcSUkh+IUXGTv+fwC07/AU6elprPzxB0KHDcfLqyYAFSpU4IvFX5KamsrxY0fZGvmLaTY78eBxKe/AuwPb88lX24hPKPj7NOvNbtSrVZ3uby0lM/PfbrxFq/fTtbkPX09+kfe++IXkO2m81rMJjepmd+HZ29kU+zGUJSV4x8Ayr1BB/9dffzF79mz27NmDoih4eXkxbNgwunTpIqNqisjOzh6Apzt3zbW8c5durPzxBw7/+acp6G1sbWka1AyAVq3b0KRpEK/0eQkXl8q0at2mVOst7m3i4A7cvG1kwcq9BS4zqndzBjwbwHsLf2FLVEyudT/vjWHUzPW8P+Qp9n71BgCnLsTz3sJf+Gjo0ySn5J0I62EiXTfqChX0wcHB6HQ67O3tefnll+nRowd6vZ5z584VqHytWrWKUkdNq1qtGqdPncwzd0bO2Pjbt2+plm3QsBFVq1Zl44b1EvQPmDqelRnwTABhc3/Crcq/l7nb21ljY62nhmtFEpNTuXlXn3ufzg354PWOLFy9jylLfzO7388j9rLsp0M8XseVtIxMok9epl/XxgCcvFD061w0QXJeVZG6bu7cuUN4eDjh4eEFLqPT6UzTbIp/1avny949u4mLi6Nmrdqm5Tl3lK9UKf+LoVJT00hKSizROorCc69aHisrPTNHdWPmqG551p+ICGP+j7sJm5M9I2HX5j58NqYHa7cfZeSM9fnu23gnnX1HLph+b9u4DsY7aUQd/qd4D6KMkXH06goV9IWd7F7c21OdnmbJ4oWsWbWSJk3/ncdidcRKrK2taRwYiNFoRKfT5bmXZOTPW7h9+xb1fM3faV5YztEzcTw/9ps8yycO7kA5R1vemv2TaXz8k/VrsmzyC+yKPke/ST/mmdY2P039avBsq3osWrOf28mp9y6gYZLz6goV9ObmSBb3x8enHt179mLNqggyMjNp3DiAgwf28/OWzQwY9BrVqlXn+LFjvDawHx07daZm7drodXqOHPmbjRvW4e7hwcshfS19GOI/rt8ysn7nsTzL33g++xxLzroa1SuyckofFEVh9bYj9GyT+0P779NX+Pt0nGnbb95/kQ27jhN3I5F6taoxsHsgf52OY8IXP5fwET34ZNSNuiIPr7x27RqXLl3C1taWOnXqmL2dliiY/02YhJubO2tXr+LXyEjc3d0JGzOOPn37AVDdtTrtOnRk//69rFu7hoyMdNzcPXjxpZcZ+NoQKlasZNkDEEXm5V6JiuWyv6nNeeuZPOs/CN9qCvrbxjtcuZ7IkF5NcSnvwKVrt1mwIoopy37LNdzyYSWjbtQVegqEkydP8u677xIdHW1aZmtry3PPPceYMWNKPPC1NgWCUKelKRDEvd3vFAh//KN+rqqhVznVdQ+DQrXo4+Li6NOnD7dv387Vj5iamsry5cuJj49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0.8759842519685039\n","Recall: 0.8759842519685039\n","F1 Score: 0.8759842519685039 \n","\n","Classification Report:\n","               precision    recall  f1-score   support\n","\n","    Negative       0.87      0.87      0.87       492\n","    Positive       0.88      0.88      0.88       508\n","\n","    accuracy                           0.87      1000\n","   macro avg       0.87      0.87      0.87      1000\n","weighted avg       0.87      0.87      0.87      1000\n","\n"]}],"source":["import seaborn as sns\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, classification_report\n","\n","true_labels = [example[\"label\"] for example in valid_dataset]\n","\n","predicted_labels = [1 if \"긍정\" in eval_dic[i] else 0 for i in range(len(valid_dataset))]\n","\n","conf_matrix = confusion_matrix(true_labels, predicted_labels)\n","\n","accuracy = accuracy_score(true_labels, predicted_labels)\n","\n","TN = conf_matrix[0, 0] if len(conf_matrix) > 1 else 0\n","TP = conf_matrix[1, 1] if len(conf_matrix) > 1 else 0\n","FP = conf_matrix[0, 1] if len(conf_matrix) > 1 else 0\n","FN = conf_matrix[1, 0] if len(conf_matrix) > 1 else 0\n","\n","# 강의자료 표처럼 만들기 위해 TP, TN 위치 바꿈\n","conf_matrix[0, 0] = TP\n","conf_matrix[1, 1] = TN\n","\n","# PP, PN\n","data = {\n","    'True Positive (TP)': [TP, FN],\n","    'True Negative (TN)': [FP, TN],\n","}\n","\n","df_2x2 = pd.DataFrame(data, index=['Predicted Positive (PP)', 'Predicted Negative (PN)'])\n","\n","print(\"Accuracy:\", accuracy)\n","#print(df_2x2)\n","\n","# Create a heatmap of the confusion matrix\n","plt.figure(figsize=(4,2))\n","sns.set(font_scale=1.4)  # 폰트 크기 조절\n","\n","# 히트맵 생성\n","sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", linewidths=.5, annot_kws={\"size\": 12})\n","plt.xticks(ticks=[0.5, 1.5], labels=['TP', 'TN'])\n","plt.yticks(ticks=[0.5, 1.5], labels=['PP', 'PN'])\n","plt.title('Confusion Matrix')\n","plt.show()\n","\n","# 정밀도, 재현율, F1 점수 계산\n","precision = precision_score(true_labels, predicted_labels)\n","recall = recall_score(true_labels, predicted_labels)\n","f1 = f1_score(true_labels, predicted_labels)\n","\n","print(\"Precision:\", precision)\n","print(\"Recall:\", recall)\n","print(\"F1 Score:\", f1, \"\\n\")\n","\n","# 분류 리포트 출력\n","class_report = classification_report(true_labels, predicted_labels, target_names=['Negative', 'Positive'])\n","print(\"Classification Report:\\n\", class_report)"],"id":"zGWyzs1tbSJH"},{"cell_type":"markdown","source":["# 테스트 데이터에 대한 분류 결과 리포트\n","\n","**모델 평가 결과**\n","\n","> 정확도 : 0.874\n","\n","> 정밀도 : 0.876\n","\n","> 재현율 : 0.876\n","\n","> F1 점수 : 0.876\n","\n","> Confusion Matrix\n","\n",">  |  | TP | TN |\n","|----------|----------|----------|\n","| PP | 445 | 63 |\n","| PN | 63 | 429 |\n","\n",">\n","\n","**결과 분석**\n","\n","> 정확도는 0.874로 모델이 1000개 테스트 데이터 중 874개에 대해 정확한 예측을 수행했다.\n","\n","> TP : 445, TN : 429, FP : 63, FN : 63의 결과가 나왔다.\n","\n","\n"],"metadata":{"id":"uKo5-WjkhVwV"},"id":"uKo5-WjkhVwV"}],"metadata":{"accelerator":"GPU","colab":{"provenance":[],"collapsed_sections":["652f307e-e1d7-43ae-b083-dba2d94c2296"]},"kernelspec":{"display_name":"Python 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<img\nsrc=https://huggingface.co./front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co./settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. 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token as git 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