{"cells":[{"cell_type":"markdown","source":["# **미세튜닝된 어댑터 업로드한 ID: yelin1001**"],"metadata":{"id":"3vV0Eyh39Q5U"},"id":"3vV0Eyh39Q5U"},{"cell_type":"markdown","metadata":{"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},"source":["# 강의 11주차: midm-food-order-understanding\n","\n","1. KT-AI/midm-bitext-S-7B-inst-v1 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\n","\n","\n","history\n","\n","v1.2\n","- KT-AI/midm-bitext-S-7B-inst-v1 에 safetensors 포맷이 올라왔기에, 해당 리포에서 받도록 설정 변경\n","- 전체 과정 재검증"],"id":"28e4c4d1-a73f-437b-a1bd-c2cc3874924a"},{"cell_type":"markdown","source":["**종속성 설치**"],"metadata":{"id":"QGv6ld9q1XHv"},"id":"QGv6ld9q1XHv"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nDZe_wqKU6J3","outputId":"ba12f714-4a8e-4ede-d2ef-30ae5684aca4","executionInfo":{"status":"ok","timestamp":1702034765617,"user_tz":-540,"elapsed":28982,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages 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optimum-1.15.0 peft-0.7.0 pyarrow-hotfix-0.6 sentencepiece-0.1.99 sentry-sdk-1.38.0 setproctitle-1.3.3 shtab-1.6.5 smmap-5.0.1 trl-0.7.4 tyro-0.6.0 wandb-0.16.1\n"]}],"source":["pip install transformers peft accelerate optimum bitsandbytes trl wandb einops"],"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":"mxXOIhar1c_2"},"id":"mxXOIhar1c_2"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"51eb00d7-2928-41ad-9ae9-7f0da7d64d6d","outputId":"cdd1ead9-5511-4ac9-d491-81745915c552","executionInfo":{"status":"ok","timestamp":1702034791587,"user_tz":-540,"elapsed":21068,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"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":162,"referenced_widgets":["c9ce3698b3394296b6798cbbf8ddc724","232b14f9c2c24d288da416db01805a63","ac386ff03b65403baec9d51fd4974751","f8ea2806c9f04fba9405ef67d88b5e1d","e4cab837d16a4c469c34b5d3a10ad66a","65e9cdbbcd07433e9e16ded92090932f","7165fc33c43e4a7e8f9b543ac75c7ab2","6845b38b57a342798208d1ec003d4fae","aa6ac3b0818e436b80e3d2e1de20aa4c","4409ed630ac54adda1447851dc83e406","a064832a02fd4619b15f7dc33e393a2a","737d67665a4d460fa66a5476450b7b26","a32f94b7399046c3bc372cb47789c0c7","e6c60a658df047c68655aa19679f953e","385aaaa585ca416b8d47209926d90fff","458a7a274b4048fbabf247e7bcf0311d","fc1720a332f8456fafa28201ef15c7b4","5f50951016d0433cb713a635919701b2","2dde2a96536941ce9be7b515449007d4","9323cb30144a4456958e5c6e4ee1ad32","8af34a10528d42dbac7bc7d8cd8b7b38","56767534afe44dc8805f3691f25c7d6a","c5d8b5766bb74bd288bad8519659bba8","1e4a1cb769e84cd9922e792ec64e894b","6449f00cea404eac9d47ef9c66bcf947","d65da8e94de941b98f5b924a75b5cfbe","117c506e02ec4955ad5ed3f1bff101e1","6010cf9a274f48f2b7ea6be4a39f1036","d4919d97690540f9b0435401b1991399","e853c95344f845f398c01ffd4398f078","ea29c770c607482d8e4307ca108e0c94","b602bf9ab61a4b608cc281f5ba085e55"]},"id":"tX7gYxZaVhYL","outputId":"e19a087a-9f52-4715-f429-d9b6016f5f0f","executionInfo":{"status":"ok","timestamp":1702034804376,"user_tz":-540,"elapsed":367,"user":{"displayName":"장예린","userId":"10756148590164405752"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='
스크립트에 사용되는 다양한 인자들을 설정함."],"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"," lora_alpha=16,\n"," lora_dropout=0.05,\n"," target_modules=[\"c_attn\", \"c_proj\", \"c_fc\"],\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":"3_8RynWf1nn6"},"id":"3_8RynWf1nn6"},{"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 사용자가 작성한 리뷰의 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."],"metadata":{"id":"pPPapcN01rYb"},"id":"pPPapcN01rYb"},{"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(5000))\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."],"metadata":{"id":"b3bJ6RxJ1xfZ"},"id":"b3bJ6RxJ1xfZ"},{"cell_type":"code","source":["def create_valid_datasets(tokenizer, args):\n"," dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)\n"," valid_data = dataset['test']\n"," valid_dataset = valid_data.select(range(1000))\n","\n"," return valid_dataset"],"metadata":{"id":"cofSBNtjZpGp"},"id":"cofSBNtjZpGp","execution_count":null,"outputs":[]},{"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=384,\n"," dataset_name='nsmc',\n"," model_name='KT-AI/midm-bitext-S-7B-inst-v1',\n"," # model_name='jangmin/midm-7b-safetensors-only',\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 = 50\n","script_args.training_args.max_steps = 800\n","script_args.training_args.output_dir = '/gdrive/MyDrive/nlp/lora-midm-7b-nsmc-v1.2'\n","script_args.training_args.run_name = 'midm-7b-nsmc-v1.2'"],"id":"372c64be-8fc0-4cc8-bda8-92ecf3632cc3"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bac62c01-21ef-491e-a686-cf4988186c58","outputId":"bea4e041-3755-4a0c-efcf-b8b56a6de5b1","executionInfo":{"status":"ok","timestamp":1701177022120,"user_tz":-540,"elapsed":5,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["ScriptArguments(cache_dir=None, model_name='KT-AI/midm-bitext-S-7B-inst-v1', dataset_name='nsmc', seq_length=384, 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=,\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/Nov28_13-10-17_31a37b1fc47f,\n","logging_first_step=False,\n","logging_nan_inf_filter=True,\n","logging_steps=50,\n","logging_strategy=steps,\n","lr_scheduler_type=cosine,\n","max_grad_norm=0.3,\n","max_steps=800,\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-midm-7b-nsmc-v1.2,\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=,\n","ray_scope=last,\n","remove_unused_columns=False,\n","report_to=['wandb'],\n","resume_from_checkpoint=None,\n","run_name=midm-7b-nsmc-v1.2,\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=, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=8, target_modules={'c_fc', 'c_proj', 'c_attn'}, lora_alpha=16, lora_dropout=0.05, 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={}), 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"],"metadata":{"id":"iU4k9KYi13pz"},"id":"iU4k9KYi13pz"},{"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":"ceO2qzi72Adz"},"id":"ceO2qzi72Adz"},{"cell_type":"markdown","metadata":{"id":"elg7gcB-5zb7"},"source":["원본인 'KT-AI/midm-bitext-S-7B-inst-v1' 는 *.bin 형태로 모델을 제공한다.\n","- 코랩에서 CPU 메모리 부족 발생\n","\n","해결책\n","- safetensors로 변환한 모델을 업로드 하고 이를 사용하기로 한다."],"id":"elg7gcB-5zb7"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":142,"referenced_widgets":["88acc2080c7243d3a2e18b36ae3d8277","971b95d99dfa4f0a90b8ed1045b67b04","67c7846ae04249aaaa05d57aa473a498","5967a4b70baf4a099bc42caba006cbe5","8dac1f4ca630434e8ad93290a4194f1b","c71b361a8491421c94f9f28cd7f67713","40917badb9624b8dbed59504cbad6223","98c8468d08f844d6be9896fec3e31348","84d4d2c2c30649f39914fdbb2a52539f","23ad1550288d4e15ab8b78c436686d22","dd2f1488a7e046718640ad15987a6e7d"]},"id":"15c8425e-bb0b-40c5-bfe8-385bac699b9d","outputId":"f7d33334-3bce-4ac7-ec00-b7a8fcf6d747","executionInfo":{"status":"ok","timestamp":1701177156681,"user_tz":-540,"elapsed":97693,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"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":["Loading checkpoint shards: 0%| | 0/2 [00:00, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=8, target_modules={'c_fc', 'c_proj', 'c_attn'}, lora_alpha=16, lora_dropout=0.05, 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={})"]},"metadata":{},"execution_count":16}],"source":["peft_config"],"id":"4420fcc4-2bac-413d-b7aa-89455c512419"},{"cell_type":"markdown","source":["**토크나이저 초기화 및 토크나이저, 모델 설정**"],"metadata":{"id":"erNS2deR2D20"},"id":"erNS2deR2D20"},{"cell_type":"code","execution_count":null,"metadata":{"id":"f47f9584-3988-46b8-a062-29dcde75a0e2"},"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","\n","tokenizer.add_special_tokens(dict(bos_token=''))\n","\n","base_model.config.pad_token_id = tokenizer.pad_token_id\n","base_model.config.bos_token_id = tokenizer.bos_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":"rpWQQMIn2LmA"},"id":"rpWQQMIn2LmA"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"62e8139f-5179-4c75-84a7-0c818ab0a35a","outputId":"d4b5c999-0c70-4dca-fe6c-e2eec6194390","executionInfo":{"status":"ok","timestamp":1701177171278,"user_tz":-540,"elapsed":1797,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 400/400 [00:00<00:00, 4449.10it/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: 1.58\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/"},"id":"000314e9-f10b-4685-8da6-0511494a9eb4","outputId":"a07645b5-e0ce-42a1-979a-309bbee68a3f","executionInfo":{"status":"ok","timestamp":1701176676372,"user_tz":-540,"elapsed":401,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["5000"]},"metadata":{},"execution_count":23}],"source":["len(train_dataset)"],"id":"000314e9-f10b-4685-8da6-0511494a9eb4"},{"cell_type":"markdown","source":["**SFTTrainer 초기화**"],"metadata":{"id":"p9uTf2Fy2PND"},"id":"p9uTf2Fy2PND"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4ba80a64-0ec7-4b29-ac95-7b3d34549f17","outputId":"8b56170d-dea0-4fb7-bc0e-22d3abef88e1","executionInfo":{"status":"ok","timestamp":1701177175437,"user_tz":-540,"elapsed":1048,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"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=None,\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/"},"id":"6qPxfovivMuH","outputId":"7353e349-6152-4925-bcb6-17e5aea7718e","executionInfo":{"status":"ok","timestamp":1701176683744,"user_tz":-540,"elapsed":432,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["MidmLMHeadModel(\n"," (transformer): MidmModel(\n"," (wte): Embedding(72192, 4096)\n"," (rotary_pos_emb): RotaryEmbedding()\n"," (drop): Dropout(p=0.0, inplace=False)\n"," (h): ModuleList(\n"," (0-31): 32 x MidmBlock(\n"," (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n"," (attn): MidmAttention(\n"," (c_attn): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=12288, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=4096, out_features=12288, bias=False)\n"," )\n"," (c_proj): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n"," )\n"," (attn_dropout): Dropout(p=0.0, inplace=False)\n"," (resid_dropout): Dropout(p=0.0, inplace=False)\n"," )\n"," (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n"," (mlp): MidmMLP(\n"," (c_fc): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=21760, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=4096, out_features=21760, bias=False)\n"," )\n"," (c_proj): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=10880, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=10880, out_features=4096, bias=False)\n"," )\n"," (dropout): Dropout(p=0.0, inplace=False)\n"," )\n"," )\n"," )\n"," (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n"," )\n"," (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n",")"]},"metadata":{},"execution_count":25}],"source":["base_model"],"id":"6qPxfovivMuH"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gw9xbeUgbZEo","outputId":"d216fac2-9aa2-4fd4-b024-65c8fa2492fd","executionInfo":{"status":"ok","timestamp":1701176686208,"user_tz":-540,"elapsed":7,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["PeftModelForCausalLM(\n"," (base_model): LoraModel(\n"," (model): MidmLMHeadModel(\n"," (transformer): MidmModel(\n"," (wte): Embedding(72192, 4096)\n"," (rotary_pos_emb): RotaryEmbedding()\n"," (drop): Dropout(p=0.0, inplace=False)\n"," (h): ModuleList(\n"," (0-31): 32 x MidmBlock(\n"," (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n"," (attn): MidmAttention(\n"," (c_attn): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=12288, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=4096, out_features=12288, bias=False)\n"," )\n"," (c_proj): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n"," )\n"," (attn_dropout): Dropout(p=0.0, inplace=False)\n"," (resid_dropout): Dropout(p=0.0, inplace=False)\n"," )\n"," (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n"," (mlp): MidmMLP(\n"," (c_fc): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=21760, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=4096, out_features=21760, bias=False)\n"," )\n"," (c_proj): Linear4bit(\n"," (lora_dropout): ModuleDict(\n"," (default): Dropout(p=0.05, inplace=False)\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=10880, out_features=8, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=8, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (base_layer): Linear4bit(in_features=10880, out_features=4096, bias=False)\n"," )\n"," (dropout): Dropout(p=0.0, inplace=False)\n"," )\n"," )\n"," )\n"," (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n"," )\n"," (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n"," )\n"," )\n",")"]},"metadata":{},"execution_count":26}],"source":["trainer.model"],"id":"gw9xbeUgbZEo"},{"cell_type":"markdown","source":["**학습 가능한 파라미터 정보**"],"metadata":{"id":"4CWLQz-M2XFH"},"id":"4CWLQz-M2XFH"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"edb204be-ec15-4800-af49-6cfbad2f7f9a","outputId":"ee19a6c0-b645-405a-89fa-a679fea209db","executionInfo":{"status":"ok","timestamp":1701177183122,"user_tz":-540,"elapsed":422,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 16744448 || all params: 3821510656 || trainable%: 0.4381630592527648\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/"},"id":"sVA-VzeTigHs","outputId":"732a5289-2d93-4b29-d4d4-cb541b8a10dd","executionInfo":{"status":"ok","timestamp":1701177184722,"user_tz":-540,"elapsed":4,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["7795015808"]},"metadata":{},"execution_count":26}],"source":["base_model.get_memory_footprint()"],"id":"sVA-VzeTigHs"},{"cell_type":"code","source":["trainer.model.print_trainable_parameters()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CTZmx_faQ-Xj","outputId":"b97ac162-dce9-40b4-8c9c-08b9d62c7da0","executionInfo":{"status":"ok","timestamp":1701177185739,"user_tz":-540,"elapsed":6,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"id":"CTZmx_faQ-Xj","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 16,744,448 || all params: 7,034,347,520 || trainable%: 0.23803839591934178\n"]}]},{"cell_type":"markdown","metadata":{"id":"76sRe172fGlm"},"source":["midm 모델을 주문 문장 이해에 적용시 특징\n","- 모델 로딩 과정에서 CPU도 5.1기가, 디스크 42.4기가, GPU 메모리: 7,4 기가\n","\n","구글 코랩 T-4 GPU: 300스텝 (13:47초 예상)\n","\n","시퀀스 길이 384의 경우\n","- 14.7 G / 15.0 G 사용\n","- 메모리 오버플로우 발생시 이보다 줄일 것"],"id":"76sRe172fGlm"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":828},"id":"14019fa9-0c6f-4729-ac99-0d407af375b8","outputId":"d140316e-cd7a-4296-d3b3-5fc60f079bc2","executionInfo":{"status":"ok","timestamp":1701173388756,"user_tz":-540,"elapsed":2298174,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"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"," "]},"metadata":{}},{"output_type":"stream","name":"stderr","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:"]},{"name":"stdout","output_type":"stream","text":[" ··········\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["Tracking run with wandb version 0.16.0"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["Run data is saved locally in /content/wandb/run-20231128_113144-sxco8kez"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["Syncing run midm-7b-nsmc-v1.2 to Weights & Biases (docs)
"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":[" View project at https://wandb.ai/yerini/huggingface"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":[" View run at https://wandb.ai/yerini/huggingface/runs/sxco8kez"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/utils.py:570: UserWarning: The dataset reached end and the iterator is reset to the start.\n"," warnings.warn(\"The dataset reached end and the iterator is reset to the start.\")\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
501.698200
1001.034100
1501.001700
2001.013100
2500.948000
3001.010000
3500.972000
4001.057900
4501.020900
5000.986600
5500.998400
6001.005900
6500.916100
7000.919100
7500.908800
8000.944000

"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=800, training_loss=1.027174186706543, metrics={'train_runtime': 2297.0794, 'train_samples_per_second': 0.697, 'train_steps_per_second': 0.348, 'total_flos': 2.48413559980032e+16, 'train_loss': 1.027174186706543, 'epoch': 0.32})"]},"metadata":{},"execution_count":28}],"source":["trainer.train()"],"id":"14019fa9-0c6f-4729-ac99-0d407af375b8"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"3Y4FQSyRghQt","outputId":"15c3e43a-e930-429e-f1d7-f9f05bb10730","executionInfo":{"status":"ok","timestamp":1701173403220,"user_tz":-540,"elapsed":450,"user":{"displayName":"장예린","userId":"08326973126538993419"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/gdrive/MyDrive/nlp/lora-midm-7b-nsmc-v1.2'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":29}],"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 주어진 영화 리뷰에 대한 긍정 또는 부정을 추출해야 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, do_stream=False):\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 if do_stream else None,\n"," use_cache=True,\n"," max_new_tokens=float('inf'),\n"," do_sample=False\n"," )\n"," decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n"," decoded_text = decoded_text[0].replace(\"<[!newline]>\", \"\\n\")\n"," return (decoded_text[len(input_prompt):])"],"id":"1919cf1f-482e-4185-9d06-e3cea1918416"},{"cell_type":"code","source":["eval_dic = {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)}"],"metadata":{"id":"ab-3Q0ZnZHeg"},"id":"ab-3Q0ZnZHeg","execution_count":null,"outputs":[]},{"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":"XMU2ydLG2k3Z"},"id":"XMU2ydLG2k3Z","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**프롬프트 정의**\n","\n","\n","> 사용자 영화 리뷰의 예측을 생성하기 위한 프롬프트 정의함."],"metadata":{"id":"e-SkE9mf2sWx"},"id":"e-SkE9mf2sWx"},{"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 사용자가 작성한 리뷰의 긍정 또는 부정을 판단해야 한다.\"\n",")"],"metadata":{"id":"XUvbhk6t2pXs"},"id":"XUvbhk6t2pXs","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**wrapper_generate 함수**\n","\n","\n","> 사용자 영화 리뷰에 대한 입력 프롬프트 생성."],"metadata":{"id":"p4J4p1b62yAM"},"id":"p4J4p1b62yAM"},{"cell_type":"code","source":["def wrapper_generate(model, input_prompt, do_stream=False):\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 if do_stream else None,\n"," use_cache=True,\n"," max_new_tokens=float('inf'),\n"," do_sample=False\n"," )\n"," decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n"," decoded_text = decoded_text[0].replace(\"<[!newline]>\", \"\\n\")\n"," return (decoded_text[len(input_prompt):])"],"metadata":{"id":"iov1rlPJ21K-"},"id":"iov1rlPJ21K-","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**양자화 설정**\n","\n","\n","> 4비트로 양자화함.\n"],"metadata":{"id":"Vs2CLd2D24ha"},"id":"Vs2CLd2D24ha"},{"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."],"metadata":{"id":"q6n3Tr4L27kR"},"id":"q6n3Tr4L27kR"},{"cell_type":"code","execution_count":null,"metadata":{"id":"39db2ee4-23c8-471f-89b2-bca34964bf81","colab":{"base_uri":"https://localhost:8080/","height":539,"referenced_widgets":["c2fd65a3f6d941e7b2ee311abd843de1","4d27ca442602458183365f64069d8060","ec252d6aba434310963f90ce0ed38d31","ad24ffb33db14ead93d17d7604118c09","9ed48cf506e04feab20197b3ce72de2e","b7088a7d4ff44d99952c4f93ea6703d6","3a644ec4a7e24f3695007a4bc0517b1a","cfad46ba0e3348a999b704bb884e53e0","412183eef1d44071bf5a5c711ec4b0c3","23e478d6e4b04b408b3c09530acad811","97ae019cd979462b9849183b154363ce","f89669afd8344e78af7cb65c2193388d","d1652fd6364a4ae4977f3d8fa4c30bf4","17a1eb7be7f9438fbc0a442e75867a00","0ed614bcbb984736b4d76489b242dabd","9b473abde4fe4e048260a4d6cc261fcc","16bec1ee9a714a58a4f8cc2c73df3054","5c6a90593db44479a00a17d08f2f372f","83f14d6621c74bffaa7636a1bc6fca52","efc891bb308b4954ad7d2a159d8d432e","fea05935ee3c48d9bd8e323e3727c444","218c579528a54c32a3a11360e241c2b7","4e0e55a1fc304d419cd039309a4ad772","854102a7dcd5407ca9594d8a29b85034","340e4ae8dd484e43a368ede33bd6018f","9fdb56aa70e34571954452534169da92","9d74e809eeb14cb2b49b93e7f1cba670","17df0fe7f3824fd8ac5bee2564a147ea","7da31d43b1bb450187abdf03bc84169e","69f1fcc5c6034926a18b13e0fa7ff851","7dc6c47f66ff4d029ee5497c6549bfb8","56032c34d6914226bdcb2e7640da4381","737ce0b9d9a84b15aad21bf13ebd1136","96c28d9784844a38bd43f17fcf341526","d54f28a82077491d85890c3133f8bb17","5a8f94f9001a4bb0b7666381ec44787f","e16f931caca74f708aed43c6a3dc10cc","49bd70185c124c68bb4ef315560ddaec","0562521df5e0458cb5d48ce7d241933f","6bf0eb9b098e4f18807904fbfbd4b20b","1a9be19bc6794718b8bae064f98f5ff8","f913fa5302b04842958e3ee710879d6e","9cabaac5c8484bc4863e19da135763b6","0d1a0c77a7d544f4b5e3142ce8d48081","7789f290df334e30a1ad829853e31e15","75b9922909dd4103b188778aff457311","083013e09b9f49579358736c66e9d032","88a9d66d6c3e4fe38fe0c2a26e6402da","b6d3b77208104d50936526089a204548","b55a75c9d89b45c5825256f5296eeebb","605580e4e53145c9b4a6061857ce6185","938745b20e1a49c194327e18e39e2916","30d86f839ddd4f418b58473d66e0f2b7","53c838b5c07940f2be0dd6a6ab02b3cc","f46eaf640feb4cfe8d3e8adaead28209","f12a7f89f6d2435f9dfdf05fb7c5daaa","32ecc05b86984b8997ad43e27dd63512","36bae218caf4460796435b10a1aaa6b3","29b37cf59b564ee6bc1ff6948b33ee24","55dcc8edeb474e2ba7793509dff51879","2ff04b51e24747018b6903beb2948a65","e7392eade55846f6b3a603c38bc85dab","b9b96ac6dc29485cb04f1b1bbcd8cb7e","b6d262184a1541b296f94a7d9eab9128","918cf5037bed4fc6b86cec27bbbd8df0","e92c4ec7873d4b31b696c6f5a098dbcc","c88098d28e11421f87463e83e81d3e17","441ccd3214244c44b6d239e3c164a49d","d848184f52cc4f27939b02683ebebfef","316c740ea79e4b4bac22aeaefabea537","1278f290c4c1441c9903b229d5f74a33","5b21b6a5a4f74b119ce3aa4b47518c04","696f283b47854e0b8b3b650d48fc4c7c","7b7ba03b3a0243a29e09127af03d1a9d","a8d344bd1c87403899d15d45c803dab7","74c15cae3a6d4dc0af178f7ea775759f","0b8dabc9628043f2b9f459ed8b17d148","bab910c60b044b9e972fe84b3860189f","65ad4b9159c642a4b0b7c13d4999f229","e29409a04a204ea995aaefb5079d460f","4fbdec2cd7e94b1fa4811dc89b8f0620","5c96f6fb78be4c7c991e52373853338f","76fa12d3ba99455181b2df000d34173f","9c7846adcf934a8aa89ccb8fc7f9a55c","45af7599bc5143a4b8d2046340389a63","b83005e296fd48628f479e049d19dea9","3e41896793364e539163b8fb229e1313","d2f1f59d26724bd48e91ce0ab339627b","204a944a12884c9d8eff0f0891fe5c23","aed8d7b38e724749803a816e7ec8d0c4","fdc5ad3fdbd74ecea5851e807ccd5665","062027b01a76428ba755e1aca4d477e1","bf39c3fdcab54c3fb99234568daf7166","45b03f0ec28f4b0e8a0dcc1005df2953","85fafa11386a468fb016374080a023c7","c9cb72bc82a54193bc360b5b869a3534","960264073f5b4338b7f8c7f21356816a","84b012ffcd644e02bd262196a8b979ad","7f6964afc9c34b3caf901d2b8bb977fa","915fd3620fe84b8cb8cef82704adf868","dba36473c9fe4c208fd31bd00d7268fd","11bcd762a6b44b02be9157945ea1e7f1","2a229f6c77fb49ed8a2c4cb8287301a2","3f5d388ceb5c4df2bea0e241292cde7d","7092f348997e4d85a6741a08a47eabfc","a66a74a482d74f80ad15f1d70304d450","10c08106c76c4ac68728f2e3f0359aa0","e2d94819ed0f4d3b84ad70f959cd0502","c3163dfcf853426bae55293c9f600fcf","fb805a205ad8403dbf74fe56d2b935b6"]},"executionInfo":{"status":"ok","timestamp":1702035314460,"user_tz":-540,"elapsed":200285,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"0a76048a-2303-4827-f841-55c47eb3b103"},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/1.14k [00:00 지정된 모델에서 미리 훈련된 토크나이저를 로드함."],"metadata":{"id":"blIuzrDC2-Mr"},"id":"blIuzrDC2-Mr"},{"cell_type":"code","execution_count":null,"metadata":{"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52","colab":{"base_uri":"https://localhost:8080/","height":187,"referenced_widgets":["81a15629dac64a6faa9bcfb5410f3588","e807dda9a7de4d4b86e5c47d1fd951a1","55baa7e325984defbd9a5d53fdb25bda","5264a42c14894af29c70ca4a00562cda","34010f6a2efa4d65b8961817fba44656","1a0306813d514b35afcde99fc7deaee6","6089cbb639724d4898f252199f7933d6","da0e74aa4f3a4905a9f4c6d63a8a489d","38a23ddce9c941a4aa8907125f304ca4","9f26a083a2e7465d94bb0f358001933c","81df7de0293e4ee3953133d479133945","4bfa73ff754548518b55f1e461290b11","2bcabfdad3f34f758de776f8c3d2814d","4a1f25c9fc07474994ec722f58f1bd6a","fe9fcfca5b3a46ccba2bca9620137322","f3c3a96d493a4d70a22ab7d40424d321","286caa64806d4116873d9ca69191e3d2","4942e858401d4e128de58ba58b77f0bd","3ef40a7706fa4cabb1e39bbdcc86d4cf","86ec597fdbb848d3aa9ed7c41faa3523","a2d671fa9905436c91fe32d3c3d59922","0b2ea01d783648f78b57e7677e46dfc4","abd68d68d5c84ecb8ce8daefd486300e","b7d1367a61404abf8a13a17556a72fe5","151d4d95f3954950baa69567abd1a0dc","078fffe629014e678fe30df577468de3","656c87b1edb94a40982da06783c46f61","c07657a02dce4c198c36e1d57211ea9c","e1ce68c3ab6b40b19c3fc9bd3ba236b7","4c5eb15964554c50ad99ff9ad7b83262","7480a95b4d414955a458aa84a461622f","4581e8a6eb4249b780e9773968f4a3d8","a3e657a9b87d4e06a2f8f73625efb049"]},"executionInfo":{"status":"ok","timestamp":1702035338823,"user_tz":-540,"elapsed":1990,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"e404a961-4323-4ee7-d8c4-1fe2c98051df"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/349 [00:00'))\n","\n","trained_model.config.pad_token_id = tokenizer.pad_token_id\n","trained_model.config.bos_token_id = tokenizer.bos_token_id"],"id":"b0b75ca4-730d-4bde-88bb-a86462a76d52"},{"cell_type":"markdown","source":["**데이터셋 생성**\n","\n","\n","> 추론에 사용할 테스트 데이터 생성함.\n"],"metadata":{"id":"Wi_2M_tE3A58"},"id":"Wi_2M_tE3A58"},{"cell_type":"code","source":["valid_dataset = create_valid_datasets(tokenizer, script_args)"],"metadata":{"id":"a_uxsZ4U3EHP","colab":{"base_uri":"https://localhost:8080/","height":305,"referenced_widgets":["2eaf7829ffc140a1b6d3208809466334","f4bf440fc087498e9732285b4cda952e","0794089ac98d4b71b9f4ab72a722b551","96f0f0e093024a028e68cfe0e7d12bfc","fa577ddf15034af8a3b18bc6e2c9ac2d","a110536755b1423c93f71859f593ca3e","d3a728e157aa48d8a8587525f1ceda3d","3e45464565d643c6b32b8102f1c7f209","f79554438f914040b9ec0be3cb7535a6","1c8cc175720a489d97839be5a87f4c16","914ac7f77beb4448b7f68814a5280514","e4343745ea8e47dc9bd457e59c03efa6","493074e2841c4cdeb05b2a978d1c27aa","3c3dc3032edd40b2b993dc3b9823302a","6842f78ad395443fb8e5d4bd7dcf90b2","a2516650762b4f8e900373721236a9b7","bbee9f26f99e43ec97a0e4a93657b8f0","68e04dea8b2d4c88b48f2acb90b639a4","eabc245366b54f5a896e54e8aa00706b","ad20749f4eb54ea897796946b8b26b70","bf57773cdc124e1ea9d0dc5555c2a7bf","dfd4783b4aae4eccaa58f5f638563c91","5de1cc1c663242ca905071a18fc132f6","bbab88a07d324036a35fd28f0601aa72","6b1dcf85dc954777bfb1a074cfcfb74e","1d6890f9b08c40aea9ac9e7f188a0d70","3510db1d4bac45b595b00eea59a561c7","e32eaad3f9a94797ba356760980ccf0f","daa09d2068dc449eb6f7dc5edb6d6d5e","169da7ad74bf4cafbb6e14077883035e","6ce52c88931b4dd2885712da6c795f0b","52bf3328cb2f4f938914037bfb2bc016","9cbf69c354214248a482de8ba3009761","84af4a82759c458ea43b18aa2e3fa78d","05b9b25d31ae40d88e3c3910cd6d05e8","d886251450e142299460d00b6a83478d","7f08f4e2f7794353a706c40a2d6fe16b","a593214ec31441dbbc33840b9818fc6e","54fdc654bb894cf7aa22fee3dc1db9e4","b6b7176d0c9741c389a4fc127ea3935e","a43bdfcfac814d17bd9fb02576761930","ebfcf61e0010469d8c2e9445944c25e5","c3806079349646d3b1260c14b42c24fc","dd91e810468a4ef09cc81746d3b3f002","84c548f8cace4bbda2a2dab1f78c3e29","294323d127564772a6654e8c21008ede","8e7d0b4ccee74857ae10136970199e72","9e554e0ba8a84a3baa50079f4b755cca","7cc4edc38c4c4a8c9236b5a880ba434d","a9e88671e5bb4809b36571ae4998c11a","092ea00b9f37476ea910287ca9fd594b","c52af5c452bf4dd4a1e5aaac5df2692b","41c17bf9195540fc8c78ea45429c9cd9","88fcfdcdd3c2433e8efee0a7c52df212","efc33584391e4ec99eacdff33e67bdc4","ec009d083c414688a22d8245eba8299b","5b56720078114f0b9a138d9f58c8e694","e0a18abaf0de475ebdda661a416a5296","81ef9f81ee7d4b9da630928f58fb3552","03740b16841242faaadf636e86fec671","eb160d88e7584c12b447d690613881cd","7348a1af040b4809b622997df68bcb5e","d00a686c6bf24a818247bb5e80f9094a","53c8bfb3ea384bfca527ba0ca9ee7a00","79fa46263201423a824d9eac5dea9517","85e8cf64b3c24ca3932d76fad2df34cf","4bbefa59b4964625bf344febe6cd498e","dafb892db7b949579011b3e4a9a8eada","e4a6f2ee44314785b071f33e8bca7346","cdc16a985df94125b233ec4f5935697f","75c19385910346fe956f391ffd281e7c","023fbf8f348645d9bafc51d7c8e057e8","f659cb7caff047d69802b09385c461de","bab379e08b3c44279e75c2686762e562","0d00703b1efb480dadb681bc119082c6","d591a8a4fa3f4dd1bb491d73fb326035","a0a13112dcab4ca6baf044e4df6ab098","8c4aae732a1146b680f3b2a41d167a08","61033c650eac4ba3941efb4e60b9e860","0e3e6dd4655d4eafbaa40307fc389168","b4d596da34d6463a92c8312e908ea6bc","5a00b40b55d14bf1b5b50879f218a5cc","256d0f9209f84f54961ddd871d0533b1","9ea42fea8c974593a4088863dc5f0b7a","064ec33c77cb499da2b5c95532cc1998","38f480b370744b11b941d645e6b8397a","1a9e4bba634f4f2b8c0e0fbe1f474c64","a03145c757ef4010ba961153df8ba0a1","27a4ae327f794d1ca4c76b2da68136e8","9d36060760c04bf9ac29aac2d4dad7a1","5876b33eba414869a43069e21de4d221","86f39bcada2c4fa091e350c0ccc368c7","b955712e65424f8dbd7f52764a434af8","a953b9b5ddac44c9b1490dc7b8de8862","83177f7df428445fb22a087f6122016d","e1866f1e20c94bd4beb6917dc4d25495","0ba40c87ac5c4406aa386d7c24216fef","9c5235da4ef748789d631e908b65181b","2d7f004eff654b3ca89fea1f2c2aa574"]},"executionInfo":{"status":"ok","timestamp":1702035353099,"user_tz":-540,"elapsed":12783,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"02b1ed9f-c670-4732-b2dd-f4a7f9e93652"},"id":"a_uxsZ4U3EHP","execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/3.18k [00:00 정확도 및 혼돈 행렬로 모델을 평가함."],"metadata":{"id":"O50xByhv3RoQ"},"id":"O50xByhv3RoQ"},{"cell_type":"code","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)"],"metadata":{"id":"TL7zOjZD3T14","colab":{"base_uri":"https://localhost:8080/","height":522},"executionInfo":{"status":"ok","timestamp":1702037385497,"user_tz":-540,"elapsed":1004,"user":{"displayName":"장예린","userId":"10756148590164405752"}},"outputId":"104717f4-2d79-48f3-a62c-b34e358cd527"},"id":"TL7zOjZD3T14","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.91\n"]},{"output_type":"display_data","data":{"text/plain":["

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tm2ZN28eJ06coFmzZri5uWFlZYWzszNz587NlfZ99913HD58GGdnZ2bMmJFhuGjgwIEEBwdz6NAhxo0bx+LFi/N1OEnkDenRZ8HS0pIJEyawdetWBgwYgIeHBzExMZw7d46IiAgqVarEe++9x+rVq1mxYkWmuvPnz2f27Nm8+uqrxMXFceHCBezs7OjQoQOBgYG88847+f6cWrduTUBAgLHXefXqVUqUKMHnn3/Ol19+abJOxYoV+eKLL+jQoQPlypXj9u3bnD9/nqSkJBo1asSMGTNYuHChWRfYTJw4kcWLF9OiRQtSU1M5f/48Op2OVq1asXz5ckaOHJkrz7cg6PV6lixZwsCBA3F1dSUiIoJbt27RsGFDVqxYQadOnVTrlitXjoCAAJo2bYqiKJw8eZIjR45w8uTJXGnbn3/+abxfwYwZMzLNftLr9cycOZMSJUqwd+/eLNflF4WHTsnPOXpCCCHynfTohRBC4yTohRBC4yTohRBC4yTohRBC4yTohRBC4yTohRBC4yTohRBC4+TKWCGEJtjVG6a6L/5P8+5prDWFLujtXptY0E0Q+ST+wBc8lhVzXxi2z5pGslSDqkIX9EIIYZLeoqBb8NySoBdCaIMEvSoJeiGENuhkbokaCXohhDZIj16VBL0QQhsk6FVJ0AshtEFm3aiSoBdCaINe4kyNvDJCCG2wkKEbNRL0QghtkKEbVRL0QghtkJOxqiTohRDaIEGvSoJeCKENcsGUKgl6IYQ2SI9elXwECiG0QW+h/shliqLQs2dP3N3dcXd358qVKybL3bhxAz8/Pxo3bszLL79MmzZtmD17NrGxsVkee82aNbz99tvUqVMHLy8v+vbtS3BwcI7bK0EvhNAGnV79kct+/vlnDh8+jC6LmT5nz56lU6dObNy4kdKlS9OqVStSUlJYsmQJ7777LtHR0ZnqKIqCn58fn376KdevX6dJkyZ4eHhw6NAh+vTpQ2BgYI7aK0EvhNCGfOrR3759m1mzZtGkSRPKly9vskxKSgqjRo0iNjaW0aNHs3btWubNm8e2bdto0aIFISEhzJo1K1O9DRs2sGnTJlxdXdm6dSv+/v4sW7aMFStWYGlpyZQpUwgLCzO7zRL0QghtyKegnzRpEqmpqUyZMkW1zM6dO7l+/ToGg4EBAwYYt1tbWzN16lQsLS0JCgriwYMHGeoFBAQA4OfnR5kyZYzbGzRogI+PD0lJSaxYscLsNkvQCyG0QadTf+SS9evXs2fPHoYPH46Li4tquV27dgHQtm3bTMM7pUuXxtPTk+TkZPbs2WPcHhoaSkhICDY2NrRs2TLTMdu3bw+kfYiYS4JeCKEJer1e9ZEbIiMjmTZtGi+//DI9e/bMsuz58+cB8PDwMLm/Vq1aAFy4cMG4Lf3natWqYW1tnalOzZo1gbQPhJiYGLPaLtMrhRCaoNOr99xbtWqVZd3s9JKnTp1KTEwMn3/++VM/PMLDwwEoW7asyf3pwzLp5bJTx8HBgSJFihAdHU14eDgGg+GpbU4nPXohhCbodDrVx7Pavn0727dvp2/fvlSvXv2p5ePi4gCws7Mzud/BwQEgwzTLp9UBsLe3z1QvO6RHL4TQhKx62TkZ104XFRXF1KlTcXNzY8iQITk+TkGSoBdCaEJWQzfPYtq0aURGRjJ79mxsbGyyVcfe3p6HDx8SHx9vcn96jzy9Z59eB1CtA//0+p+slx0S9EIITciNIRpTdu7ciY2NDYsWLWLRokUZ9t29exeAsWPHYmdnx/vvv0+7du0oX748Dx8+5Pbt2yaHeiIiIgAyzMNP//n27dsm2xEbG2u8yEpt/r4aCXohhCbk1uwaUxISEjhy5Ijq/tOnTwP/nPStUaMG58+f58yZMzRv3jxT+bNnzwJk+BBI//nSpUskJiZmmnlz7tw5AFxdXXF0dDSr/XIyVgihCTq9TvXxLI4dO8bFixdNPtLn0m/ZsoWLFy/Su3dvAFq0aAGkncRVFCXD8e7cucPx48extLSkadOmxu2urq4YDAYSEhL4448/MrVjy5YtwNNnEJkiQS+E0IS8nHVjrpYtW1KxYkVCQkJYsmSJcXtiYiKTJk0iOTmZLl264OTklKFev379AJg1a5ZxeAfg6NGjBAYGYmVlRa9evcxujwzdCCE0Ia9OxuaEpaUlc+bMwdfXlzlz5rBt2zbc3Nw4efIkYWFhGAwG/Pz8MtV766232LdvH5s3b6Z9+/Y0atSIuLg4goODSU1N5bPPPsvyilzV9uT0idy9e5fw8HCsra2pUqWKySu5hBAiv+TlGH1OeHh4sH79evz9/QkODiYkJISyZcvSv39/Bg0aZHLmjE6nY/bs2Xh6ehIYGMjevXuxsrKiYcOGDBw4EG9v7xy1Raf8ewDpKS5dusQnn3zCyZMnjdusra3p2rUrY8eOzfPAt3ttYp4ePz993LM5Uz5ow9mrEdT3XWCyTDFHW079NJLSJRzpPnE163afzVSmjqE8E/u1pNErbthaW3It/AHLNhxl0S85X7/6eRB/4AseJxd0K3LXmdOn2LhhPUePHCY8PIzixYrzSu3aDB42gooVK2Uou33bFlatWM71a1fR6y2oWq0avfv2p2mz5gXT+Dxm+4zjC+UGBqnuu7W4y7MdvJAz66WNiIigR48ePHr0KMMJhoSEBFavXk1kZCTz58/P9UZqkUuponzcsxkxcQlZlpvUvxX2Nlaq+1t5VSVohi8nL4UzffkuYuISqezihEvporndZJELvg9Yyom//qRN23YYDO5ERt7lp9U/8m7Xt1m15meqVUu7rH31j6uY8eXnNGnWnGGdRpOYkMDG9esYOugD5szzp3Wb1wv4mTx/nqehm+eNWUG/dOlSHj58SM2aNRk/fjw1a9YkJiaGnTt3MmfOHH777TfOnTtnXHxHqJs25D8cOXsTC72OksVNX/xQs1JpBnRuyJff72LygNaZ9hext2Hpf7uyLfgi701ck+nsvnj++PbqzfSZs7F64ptv2/+0p2unjixbuphpM2YDsObHH6jl8TL+X39rPJnY6e2utGnRhE0b1knQm/C8Dd08T8x6Zfbv30/RokVZunQpDRo0wMHBgTJlytC9e3c+/vhjFEVh//79edVWzXitdkU6N6+F3/xfsyw3e0QHNu45x4GT103uf+f12pQtWYTJ3/2OoijY21oVyAwDkX116tbLEPIAbm4VqVK1GteuXjVui42JwalkyQzvp6OjI/b2DtjY2uZbewuT52nWzfPGrKC/desWtWvXzjQlCKB169bGMkKdXq/jq5Ed+H7zcc5ejVAt93YLD159uQITFm1TLdOyfhUexjymfKminFwzgns7P+XOb58wf8yb2FjLhKrCQlEU7t2LpHjxEsZt9b28OLh/H6t/XEVYWCjXrl7hy8+mEBMTzfs9sl4i90WVV/PotcCsNHj8+DHOzs4m95UsWRJIG68X6gZ08qJC2eK8MWKZahlba0umDWmH/88HuHE7CrdyJUyWq+JaEksLPYHTe7Bi8zEmffsbTetWYpBPI4o72tLr0//l1dMQuejXzRu5ExHBoCHDjNvGjv8vUQ8eMOPLz5nx5ecAlChRgsUBy6ldp25BNfW5Jj13dbne7ZNxYnVORe34pH9rpi/fRWRUnGq5Mb7NsLK0YObKPaplABztrXGws2bxusOMnpc2DLRhzzmsrCwZ0MmLqUt3ciX0Xq4+B5G7rl29wrTPp1K7Tl3efKuzcbudrS0VK1aiTJmyNG3WnNi4WH5YuZxRw4fy/cofqeDmVoCtfj7JGL06s4M+MjKSo0eP5mh/gwYNzP11mjJ5YBsePIpj0S+HVMtUKFuckd0bM2LOJmLjE7M8XnxC2tzD//1+KsP2n387yYBOXjT0eEmC/jkWefcuQwZ9gKNjEWbPnY+FxT/3Nh0zajgWFpb4L/rWuK1Fi1Z0bN8W/wVzmTVnXgG0+PkmQzTqzA76/fv3q55w1el0qvt1Op1xUZ4XURXXkvR7swF+C36lnHMR43ZbG0usLPVUKFuc6NgEJvVvTfjdR+z96xoVyhYHoKxT2gJGzsUdqFC2ODcjHqIoCrciH1GrchnuPMh4W7G7///vEkXUb2AgClZ0dDSDPhxA9KNovl/5I6VL/3Mj6NCbNzmwfx+TPv0sQ51ixYtTt149Tvz1Z343t1CQoRt1ZgW9uUtjin+UL1UUCws9X43syFcjO2bafzHIj4X/O8BLZYpR9SVnLvwyJlOZBX5vAVC27Wc8jHnMXxfDae1VjfLORbl0I9JYrpxz2hz6yCjz7kIj8kdCQgLDBn/I339fZ/HS76lStWqG/ffupb2XKakpmeomJyeTkpx5u0ib6CBMMyvoTa2oJrLn3NUIuo37IdP2yQPbUMTemjHzfuVq2H2KOdpSsph9hjI1K5fh04FtmPPDXg6fuWEc0gnaeRo/32b07ujJnj//mZrXp2N9kpJT2PvXtbx9UsJsKSkpfDx6BKdOnmCe/yKTJ1ZfquCGXq9n+9Yt+HR719hTjbh9mz+PH6NuPc/8bnahIEGvTta6ySf3Hsaxad/5TNuHdGsEYHJfuqiYxwAcPx+aodzJS7dYvukYvTvWx9JCz76/rtO0biW6tHqZmSt3cysyOpefhXhWc2ZOZ/euP2jWvAUPH0axedOGDPs7dHwLJycnOnXuwtqgQAb07UWr1q8TFxfLzz+tJiEhgX4DPiig1j/fZORGndlBX9Br3YiMhs7awM2Ih/R8ox5vNq3JjdtR+M3/lYX/O1jQTRMmXLx4AYA9u3exZ/euTPs7dEwbnps46VMM7tVZt/YXFsybA0Atj5f5/MsZeNZ/sSc1qLGwkKRXY9aiZhEREbz55puZ1rqBtBMhr7/+ep6vdaOlRc1E1rS4qJlQ96yLmr38ye+q+05/1ubZDl7ImTXxNH2tmxo1arBq1SqOHz/Onj17mDRpEvb29sa1boQQIr/JEgjqzPoMfXKtm/RlEBwcHOjevTsWFhZMnjyZ/fv3y6Jm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0.9409282700421941\n","Recall: 0.8779527559055118\n","F1 Score: 0.9083503054989817 \n","\n","Classification Report:\n"," precision recall f1-score support\n","\n"," Negative 0.88 0.94 0.91 492\n"," Positive 0.94 0.88 0.91 508\n","\n"," accuracy 0.91 1000\n"," macro avg 0.91 0.91 0.91 1000\n","weighted avg 0.91 0.91 0.91 1000\n","\n"]}]},{"cell_type":"markdown","source":["# 테스트 데이터에 대한 분류 결과 리포트\n","\n","**모델 평가 결과**\n","\n","> 정확도 : 0.910\n","\n","> 정밀도 : 0.941\n","\n","> 재현율 : 0.878\n","\n","> F1 점수 : 0.908\n","\n","> Confusion Matrix\n","\n","> | | TP | TN |\n","|----------|----------|----------|\n","| PP | 446 | 28 |\n","| PN | 62 | 464 |\n","\n",">\n","\n","**결과 분석**\n","\n","> 정확도는 0.910으로 모델이 1000개 테스트 데이터 중 910개에 대해 정확한 예측을 수행했다.\n","\n","> TP : 446, TN : 464, FP : 28, FN : 62의 결과가 나왔다. 미세튜닝된 모델은 주로 긍정적인 리뷰를 부정적으로 잘못 예측하는 경향이 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