[NLP] DialoGPT 를 이용해서 데이터 커스텀을 통한 GPT 생성 - HuggingFace

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"source": [
"# Fine-tune a DialoGPT model\n",
"\n",
"Adapted from the notebook in this Medium post."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00001-f1561e9e-4ec2-471c-947d-1fb1e9006488",
"deepnote_cell_type": "markdown",
"id": "Y17kuzFNUSrZ"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"cell_id": "00005-59bb6055-765e-499d-b35a-b84721c6fd19",
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"id": "dnv5kT-mLsB-"
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"source": [
"# all the imports\n",
"\n",
"import glob\n",
"import logging\n",
"import os\n",
"import pickle\n",
"import random\n",
"import re\n",
"import shutil\n",
"from typing import Dict, List, Tuple\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from torch.nn.utils.rnn import pad_sequence\n",
"from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n",
"from torch.utils.data.distributed import DistributedSampler\n",
"from tqdm.notebook import tqdm, trange\n",
"\n",
"from pathlib import Path\n",
"\n",
"from transformers import (\n",
" MODEL_WITH_LM_HEAD_MAPPING,\n",
" WEIGHTS_NAME,\n",
" AdamW,\n",
" AutoConfig,\n",
" PreTrainedModel,\n",
" PreTrainedTokenizer,\n",
" get_linear_schedule_with_warmup,\n",
")\n",
"\n",
"\n",
"try:\n",
" from torch.utils.tensorboard import SummaryWriter\n",
"except ImportError:\n",
" from tensorboardX import SummaryWriter"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00006-7f2ee539-7826-4795-b2a6-2d581899b8ad",
"deepnote_cell_type": "markdown",
"id": "BmrbGB8aUmBm"
},
"source": [
"## Get Data from Kaggle"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"cell_id": "00009-47197517-46a5-4c2a-adbf-fa563187bfe5",
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"id": "RXdJTSVwWGHj"
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"outputs": [],
"source": [
"data = pd.read_csv('./data/hospital.csv',encoding='cp949')"
]
},
{
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"execution_count": 24,
"metadata": {
"cell_id": "00010-53c4a4f6-97f0-4297-b4e1-95dbb2dda8dc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
},
"deepnote_cell_type": "code",
"id": "h6kGx-9eG7qA",
"outputId": "6091a58a-79c2-453d-b792-6eddaa40ec74"
},
"outputs": [
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"/tmp/ipykernel_21834/1955126597.py:2: SettingWithCopyWarning: \n",
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"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" data['발화자'][i] = data['발화자'][i].replace("A-1","A")\n",
"/tmp/ipykernel_21834/1955126597.py:3: SettingWithCopyWarning: \n",
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" data['발화자'][i] = data['발화자'][i].replace("A-2","A")\n"
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"source": [
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"CHARACTER_NAME = 'A'"
]
},
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"contexted = []\n",
"\n",
"# context window of size 7\n",
"n = 7\n",
"\n",
"for i in data[data.발화자 == CHARACTER_NAME].index:\n",
" if i < n:\n",
" continue\n",
" row = []\n",
" prev = i - 1 - n # we additionally substract 1, so row will contain current responce and 7 previous responces \n",
" for j in range(i, prev, -1):\n",
" row.append(data.번역문[j])\n",
" contexted.append(row)\n",
"\n",
"columns = ['response', 'context'] \n",
"columns = columns + ['context/' + str(i) for i in range(n - 1)]\n",
"\n",
"df = pd.DataFrame.from_records(contexted, columns=columns)"
]
},
{
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"height": 558
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"150 Do you know how much a double room costs? \n",
"53 Before the operation, I will take the vessel a... \n",
"103 The baby will be injected a fluid but I don't ... \n",
"186 There is a sleeping pill ingredient in the mor... \n",
"198 A steroid is contained in the liniment so don'... \n",
"105 This injection is given on the arm, not in the... \n",
"\n",
" context/5 \n",
"150 Double rooms are too expensive even with insur... \n",
"53 I am glad to see you recovered soon. \n",
"103 That is a very simple treatment method. \n",
"186 What kind of ingredients are taken out in the ... \n",
"198 How often do I apply the liniment? \n",
"105 I will do my best to inject on the back of her... "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sample(6)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"cell_id": "00014-d864c0fe-77a2-401d-8c3c-c554ebba3f3a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 510
},
"deepnote_cell_type": "code",
"id": "NGy0MxMQVIAP",
"outputId": "65830c0f-f8e3-4b1b-ed9e-b2e9574d306f"
},
"outputs": [
{
"data": {
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"19 I want consultation at gynecology and do you h... \n",
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" context/0 \\n",
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"19 At which department do you want a consultation? \n",
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"23 How many people are awaiting? \n",
"166 Do you feel less bloated than yesterday? \n",
"14 I dropped a glass container on the top of my f... \n",
"19 There is an address section as well. Should I ... \n",
"\n",
" context/3 \\n",
"35 Can you assign me another doctor today? \n",
"23 He has a quite long waiting list, so are you o... \n",
"166 Always be well and I wish you to recover soon. \n",
"14 It may happen when you overuse your hands but ... \n",
"19 Write down your name, contact information and ... \n",
"\n",
" context/4 \\n",
"35 Shall I receive you to doctor number 5 in char... \n",
"23 Can I get a consultation from the doctor of de... \n",
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"19 This is my first time getting a consultation a... \n",
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"166 I should have informed you in advance but I ha... \n",
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"19 These days, people living in new houses get it... "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trn_df, val_df = train_test_split(df, test_size=0.1)\n",
"trn_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"cell_id": "00015-bfc84225-09f2-4398-9ed6-c76fe23bf9ae",
"deepnote_cell_type": "code",
"id": "aEeJQlAKWtiJ"
},
"outputs": [],
"source": [
"# create dataset suitable for our model\n",
"def construct_conv(row, tokenizer, eos = True):\n",
" flatten = lambda l: [item for sublist in l for item in sublist]\n",
" conv = list(reversed([tokenizer.encode(x) + [tokenizer.eos_token_id] for x in row]))\n",
" conv = flatten(conv)\n",
" return conv\n",
"\n",
"class ConversationDataset(Dataset):\n",
" def init(self, tokenizer: PreTrainedTokenizer, args, df, block_size=512):\n",
"\n",
" block_size = block_size - (tokenizer.model_max_length - tokenizer.max_len_single_sentence)\n",
"\n",
" directory = args.cache_dir\n",
" cached_features_file = os.path.join(\n",
" directory, args.model_type + "cached_lm_" + str(block_size)\n",
" )\n",
"\n",
" if os.path.exists(cached_features_file) and not args.overwrite_cache:\n",
" logger.info("Loading features from cached file %s", cached_features_file)\n",
" with open(cached_features_file, "rb") as handle:\n",
" self.examples = pickle.load(handle)\n",
" else:\n",
" logger.info("Creating features from dataset file at %s", directory)\n",
"\n",
" self.examples = []\n",
" for , row in df.iterrows():\n",
" conv = construct_conv(row, tokenizer)\n",
" self.examples.append(conv)\n",
"\n",
" logger.info("Saving features into cached file %s", cached_features_file)\n",
" with open(cached_features_file, "wb") as handle:\n",
" pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"\n",
" def _len(self):\n",
" return len(self.examples)\n",
"\n",
" def getitem(self, item):\n",
" return torch.tensor(self.examples[item], dtype=torch.long)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"cell_id": "00016-e242a320-fac6-427e-9c66-9510fafa112d",
"deepnote_cell_type": "code",
"id": "-3iHwoKlWyrs"
},
"outputs": [],
"source": [
"# Cacheing and storing of data/checkpoints\n",
"\n",
"def load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False):\n",
" return ConversationDataset(tokenizer, args, df_val if evaluate else df_trn)\n",
"\n",
"\n",
"def set_seed(args):\n",
" random.seed(args.seed)\n",
" np.random.seed(args.seed)\n",
" torch.manual_seed(args.seed)\n",
" if args.n_gpu > 0:\n",
" torch.cuda.manual_seed_all(args.seed)\n",
"\n",
"\n",
"def sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]:\n",
" ordering_and_checkpoint_path = []\n",
"\n",
" glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-".format(checkpoint_prefix)))\n",
"\n",
" for path in glob_checkpoints:\n",
" if use_mtime:\n",
" ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n",
" else:\n",
" regex_match = re.match(".{}-([0-9]+)".format(checkpoint_prefix), path)\n",
" if regex_match and regex_match.groups():\n",
" ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n",
"\n",
" checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n",
" checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n",
" return checkpoints_sorted\n",
"\n",
"\n",
"def rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None:\n",
" if not args.save_total_limit:\n",
" return\n",
" if args.save_total_limit <= 0:\n",
" return\n",
"\n",
" # Check if we should delete older checkpoint(s)\n",
" checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)\n",
" if len(checkpoints_sorted) <= args.save_total_limit:\n",
" return\n",
"\n",
" number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)\n",
" checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n",
" for checkpoint in checkpoints_to_be_deleted:\n",
" logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))\n",
" shutil.rmtree(checkpoint)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00017-712df464-f1b8-4a19-851a-31958d24fe29",
"deepnote_cell_type": "markdown",
"id": "EEDdTJTqUwZJ"
},
"source": [
"## Build Model"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"cell_id": "00018-cbd90a4b-637d-405c-be7a-eb0b92d7db4b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 231,
"referenced_widgets": [
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},
"deepnote_cell_type": "code",
"id": "r2cE0fY5UHpz",
"outputId": "e9faf138-18e1-4cb8-d016-e6ef337bcc09"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8cb9dca20dab4d5fb7deb10a1a851fee",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "375df9c63e8a436e86b3c28e05101102",
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},
"metadata": {},
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},
{
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "cd6194aa62684fc4b15a34c49ce9051b",
"version_major": 2,
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/suyeon/.conda/envs/seoul/lib/python3.8/site-packages/transformers/models/auto/modeling_auto.py:1352: FutureWarning: The class AutoModelWithLMHead is deprecated and will be removed in a future version. Please use AutoModelForCausalLM for causal language models, AutoModelForMaskedLM for masked language models and AutoModelForSeq2SeqLM for encoder-decoder models.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7cec7b2cb12342dcb894287ad075f145",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/863M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3133475b854948a6868834fd5e8f3b42",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)neration_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer\n",
"import torch\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")\n",
"model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-medium")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"cell_id": "00019-fcab14c1-f4a7-4d89-bd9f-f999aacd63e9",
"deepnote_cell_type": "code",
"id": "ra2vsRp-UMXo"
},
"outputs": [],
"source": [
""""\n",
"Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).\n",
"GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned\n",
"using a masked language modeling (MLM) loss.\n",
""""\n",
"\n",
"# Configs\n",
"logger = logging.getLogger(__name)\n",
"\n",
"MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\n",
"MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"cell_id": "00020-566a709f-f62a-4209-91ee-773847714eb5",
"deepnote_cell_type": "code",
"id": "2OnASqJjUNJa"
},
"outputs": [],
"source": [
"# Args to allow for easy convertion of python script to notebook\n",
"class Args():\n",
" def init(self):\n",
" self.output_dir = './content/output-medium'\n",
" self.model_type = 'gpt2'\n",
" self.model_name_or_path = 'microsoft/DialoGPT-medium'\n",
" self.config_name = 'microsoft/DialoGPT-medium'\n",
" self.tokenizer_name = 'microsoft/DialoGPT-medium'\n",
" self.cache_dir = 'cached'\n",
" self.block_size = 512\n",
" self.do_train = True\n",
" self.do_eval = True\n",
" self.evaluate_during_training = False\n",
" self.per_gpu_train_batch_size = 4\n",
" self.per_gpu_eval_batch_size = 4\n",
" self.gradient_accumulation_steps = 1\n",
" self.learning_rate = 5e-5\n",
" self.weight_decay = 0.0\n",
" self.adam_epsilon = 1e-8\n",
" self.max_grad_norm = 1.0\n",
" self.num_train_epochs = 4\n",
" self.max_steps = -1\n",
" self.warmup_steps = 0\n",
" self.logging_steps = 1000\n",
" self.save_steps = 3500\n",
" self.save_total_limit = None\n",
" self.eval_all_checkpoints = False\n",
" self.no_cuda = False\n",
" self.overwrite_output_dir = True\n",
" self.overwrite_cache = True\n",
" self.should_continue = False\n",
" self.seed = 42\n",
" self.local_rank = -1\n",
" self.fp16 = False\n",
" self.fp16_opt_level = 'O1'\n",
"\n",
"args = Args()"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00021-72a2d478-6578-4d8b-a0bd-4310a765d649",
"deepnote_cell_type": "markdown",
"id": "9Q1dTFXxW9NE"
},
"source": [
"## Train and Evaluate"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"cell_id": "00022-4c5df9ab-1f7a-4d61-b577-763f2c8e284f",
"deepnote_cell_type": "code",
"id": "PaarIDZrW81h"
},
"outputs": [],
"source": [
"def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:\n",
" """ Train the model """\n",
" if args.local_rank in [-1, 0]:\n",
" tb_writer = SummaryWriter()\n",
"\n",
" args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n",
"\n",
" def collate(examples: List[torch.Tensor]):\n",
" if tokenizer._pad_token is None:\n",
" return pad_sequence(examples, batch_first=True)\n",
" return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
"\n",
" train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n",
" train_dataloader = DataLoader(\n",
" train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate, drop_last = True\n",
" )\n",
"\n",
" if args.max_steps > 0:\n",
" t_total = args.max_steps\n",
" args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n",
" else:\n",
" t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n",
"\n",
" model = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training\n",
" model.resize_token_embeddings(len(tokenizer))\n",
" # add_special_tokens(model, tokenizer)\n",
"\n",
"\n",
" # Prepare optimizer and schedule (linear warmup and decay)\n",
" no_decay = ["bias", "LayerNorm.weight"]\n",
" optimizer_grouped_parameters = [\n",
" {\n",
" "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
" "weight_decay": args.weight_decay,\n",
" },\n",
" {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},\n",
" ]\n",
" optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n",
" scheduler = get_linear_schedule_with_warmup(\n",
" optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n",
" )\n",
"\n",
" # Check if saved optimizer or scheduler states exist\n",
" if (\n",
" args.model_name_or_path\n",
" and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt"))\n",
" and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt"))\n",
" ):\n",
" # Load in optimizer and scheduler states\n",
" optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))\n",
" scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))\n",
"\n",
" if args.fp16:\n",
" try:\n",
" from apex import amp\n",
" except ImportError:\n",
" raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")\n",
" model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n",
"\n",
" # multi-gpu training (should be after apex fp16 initialization)\n",
" if args.n_gpu > 1:\n",
" model = torch.nn.DataParallel(model)\n",
"\n",
" # Distributed training (should be after apex fp16 initialization)\n",
" if args.local_rank != -1:\n",
" model = torch.nn.parallel.DistributedDataParallel(\n",
" model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True\n",
" )\n",
"\n",
" # Train!\n",
" logger.info("* Running training *")\n",
" logger.info(" Num examples = %d", len(train_dataset))\n",
" logger.info(" Num Epochs = %d", args.num_train_epochs)\n",
" logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)\n",
" logger.info(\n",
" " Total train batch size (w. parallel, distributed & accumulation) = %d",\n",
" args.train_batch_size\n",
" * args.gradient_accumulation_steps\n",
" * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),\n",
" )\n",
" logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)\n",
" logger.info(" Total optimization steps = %d", t_total)\n",
"\n",
" global_step = 0\n",
" epochs_trained = 0\n",
" steps_trained_in_current_epoch = 0\n",
" # Check if continuing training from a checkpoint\n",
" if args.model_name_or_path and os.path.exists(args.model_name_or_path):\n",
" try:\n",
" # set global_step to gobal_step of last saved checkpoint from model path\n",
" checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]\n",
" global_step = int(checkpoint_suffix)\n",
" epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)\n",
" steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)\n",
"\n",
" logger.info(" Continuing training from checkpoint, will skip to saved global_step")\n",
" logger.info(" Continuing training from epoch %d", epochs_trained)\n",
" logger.info(" Continuing training from global step %d", global_step)\n",
" logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)\n",
" except ValueError:\n",
" logger.info(" Starting fine-tuning.")\n",
"\n",
" tr_loss, logging_loss = 0.0, 0.0\n",
"\n",
" model.zero_grad()\n",
" train_iterator = trange(\n",
" epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]\n",
" )\n",
" set_seed(args) # Added here for reproducibility\n",
" for _ in train_iterator:\n",
" epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])\n",
" for step, batch in enumerate(epoch_iterator):\n",
"\n",
" # Skip past any already trained steps if resuming training\n",
" if steps_trained_in_current_epoch > 0:\n",
" steps_trained_in_current_epoch -= 1\n",
" continue\n",
"\n",
" inputs, labels = (batch, batch)\n",
" if inputs.shape[1] > 1024: continue\n",
" inputs = inputs.to(args.device)\n",
" labels = labels.to(args.device)\n",
" model.train()\n",
" outputs = model(inputs, labels=labels)\n",
" loss = outputs[0] # model outputs are always tuple in transformers (see doc)\n",
"\n",
" if args.n_gpu > 1:\n",
" loss = loss.mean() # mean() to average on multi-gpu parallel training\n",
" if args.gradient_accumulation_steps > 1:\n",
" loss = loss / args.gradient_accumulation_steps\n",
"\n",
" if args.fp16:\n",
" with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
" scaled_loss.backward()\n",
" else:\n",
" loss.backward()\n",
"\n",
" tr_loss += loss.item()\n",
" if (step + 1) % args.gradient_accumulation_steps == 0:\n",
" if args.fp16:\n",
" torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n",
" else:\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n",
" optimizer.step()\n",
" scheduler.step() # Update learning rate schedule\n",
" model.zero_grad()\n",
" global_step += 1\n",
"\n",
" if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n",
" # Log metrics\n",
" if (\n",
" args.local_rank == -1 and args.evaluate_during_training\n",
" ): # Only evaluate when single GPU otherwise metrics may not average well\n",
" results = evaluate(args, model, tokenizer)\n",
" for key, value in results.items():\n",
" tb_writer.add_scalar("eval_{}".format(key), value, global_step)\n",
" tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)\n",
" tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)\n",
" logging_loss = tr_loss\n",
"\n",
" if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n",
" checkpoint_prefix = "checkpoint"\n",
" # Save model checkpoint\n",
" output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step))\n",
" os.makedirs(output_dir, exist_ok=True)\n",
" model_to_save = (\n",
" model.module if hasattr(model, "module") else model\n",
" ) # Take care of distributed/parallel training\n",
" model_to_save.save_pretrained(output_dir)\n",
" tokenizer.save_pretrained(output_dir)\n",
"\n",
" torch.save(args, os.path.join(output_dir, "training_args.bin"))\n",
" logger.info("Saving model checkpoint to %s", output_dir)\n",
"\n",
" rotate_checkpoints(args, checkpoint_prefix)\n",
"\n",
" torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))\n",
" torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))\n",
" logger.info("Saving optimizer and scheduler states to %s", output_dir)\n",
"\n",
" if args.max_steps > 0 and global_step > args.max_steps:\n",
" epoch_iterator.close()\n",
" break\n",
" if args.max_steps > 0 and global_step > args.max_steps:\n",
" train_iterator.close()\n",
" break\n",
"\n",
" if args.local_rank in [-1, 0]:\n",
" tb_writer.close()\n",
"\n",
" return global_step, tr_loss / global_step\n",
"\n",
"# Evaluation of some model\n",
"\n",
"def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, df_trn, df_val, prefix="") -> Dict:\n",
" # Loop to handle MNLI double evaluation (matched, mis-matched)\n",
" eval_output_dir = args.output_dir\n",
"\n",
" eval_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=True)\n",
" os.makedirs(eval_output_dir, exist_ok=True)\n",
" args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n",
" # Note that DistributedSampler samples randomly\n",
"\n",
" def collate(examples: List[torch.Tensor]):\n",
" if tokenizer._pad_token is None:\n",
" return pad_sequence(examples, batch_first=True)\n",
" return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
"\n",
" eval_sampler = SequentialSampler(eval_dataset)\n",
" eval_dataloader = DataLoader(\n",
" eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate, drop_last = True\n",
" )\n",
"\n",
" # multi-gpu evaluate\n",
" if args.n_gpu > 1:\n",
" model = torch.nn.DataParallel(model)\n",
"\n",
" # Eval!\n",
" logger.info("* Running evaluation {} *".format(prefix))\n",
" logger.info(" Num examples = %d", len(eval_dataset))\n",
" logger.info(" Batch size = %d", args.eval_batch_size)\n",
" eval_loss = 0.0\n",
" nb_eval_steps = 0\n",
" model.eval()\n",
"\n",
" for batch in tqdm(eval_dataloader, desc="Evaluating"):\n",
" inputs, labels = (batch, batch)\n",
" inputs = inputs.to(args.device)\n",
" labels = labels.to(args.device)\n",
"\n",
" with torch.no_grad():\n",
" outputs = model(inputs, labels=labels)\n",
" lm_loss = outputs[0]\n",
" eval_loss += lm_loss.mean().item()\n",
" nb_eval_steps += 1\n",
"\n",
" eval_loss = eval_loss / nb_eval_steps\n",
" perplexity = torch.exp(torch.tensor(eval_loss))\n",
"\n",
" result = {"perplexity": perplexity}\n",
"\n",
" output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")\n",
" with open(output_eval_file, "w") as writer:\n",
" logger.info("* Eval results {} *".format(prefix))\n",
" for key in sorted(result.keys()):\n",
" logger.info(" %s = %s", key, str(result[key]))\n",
" writer.write("%s = %s\n" % (key, str(result[key])))\n",
"\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"cell_id": "00023-cfba104d-22eb-4328-8b57-239ed42d1360",
"deepnote_cell_type": "code",
"id": "SCnGAJWbXD9C"
},
"outputs": [],
"source": [
"# Main runner\n",
"\n",
"def main(df_trn, df_val):\n",
" args = Args()\n",
" \n",
" if args.should_continue:\n",
" sorted_checkpoints = _sorted_checkpoints(args)\n",
" if len(sorted_checkpoints) == 0:\n",
" raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.")\n",
" else:\n",
" args.model_name_or_path = sorted_checkpoints[-1]\n",
"\n",
" if (\n",
" os.path.exists(args.output_dir)\n",
" and os.listdir(args.output_dir)\n",
" and args.do_train\n",
" and not args.overwrite_output_dir\n",
" and not args.should_continue\n",
" ):\n",
" raise ValueError(\n",
" "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(\n",
" args.output_dir\n",
" )\n",
" )\n",
"\n",
" # Setup CUDA, GPU & distributed \n",
" # os.environ["CUDA_VISIBLE_DEVICES"] = "0" # GPU\n",
" device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n",
" args.n_gpu = torch.cuda.device_count()\n",
" args.device = device\n",
"\n",
" # Setup logging\n",
" logging.basicConfig(\n",
" format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",\n",
" datefmt="%m/%d/%Y %H:%M:%S",\n",
" level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n",
" )\n",
" logger.warning(\n",
" "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",\n",
" args.local_rank,\n",
" device,\n",
" args.n_gpu,\n",
" bool(args.local_rank != -1),\n",
" args.fp16,\n",
" )\n",
"\n",
" # Set seed\n",
" set_seed(args)\n",
"\n",
" config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)\n",
" tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)\n",
" model = AutoModelWithLMHead.from_pretrained(\n",
" args.model_name_or_path,\n",
" from_tf=False,\n",
" config=config,\n",
" cache_dir=args.cache_dir,\n",
" )\n",
" model.to(args.device)\n",
" \n",
" logger.info("Training/evaluation parameters %s", args)\n",
"\n",
" # Training\n",
" if args.do_train:\n",
" train_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False)\n",
"\n",
" global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n",
" logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)\n",
"\n",
" # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()\n",
" if args.do_train:\n",
" # Create output directory if needed\n",
" os.makedirs(args.output_dir, exist_ok=True)\n",
"\n",
" logger.info("Saving model checkpoint to %s", args.output_dir)\n",
" # Save a trained model, configuration and tokenizer using save_pretrained().\n",
" # They can then be reloaded using from_pretrained()\n",
" model_to_save = (\n",
" model.module if hasattr(model, "module") else model\n",
" ) # Take care of distributed/parallel training\n",
" model_to_save.save_pretrained(args.output_dir)\n",
" tokenizer.save_pretrained(args.output_dir)\n",
"\n",
" # Good practice: save your training arguments together with the trained model\n",
" torch.save(args, os.path.join(args.output_dir, "training_args.bin"))\n",
"\n",
" # Load a trained model and vocabulary that you have fine-tuned\n",
" model = AutoModelWithLMHead.from_pretrained(args.output_dir)\n",
" tokenizer = AutoTokenizer.from_pretrained(args.output_dir)\n",
" model.to(args.device)\n",
"\n",
" # Evaluation\n",
" results = {}\n",
" if args.do_eval and args.local_rank in [-1, 0]:\n",
" checkpoints = [args.output_dir]\n",
" if args.eval_all_checkpoints:\n",
" checkpoints = list(\n",
" os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))\n",
" )\n",
" logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging\n",
" logger.info("Evaluate the following checkpoints: %s", checkpoints)\n",
" for checkpoint in checkpoints:\n",
" global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""\n",
" prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""\n",
"\n",
" model = AutoModelWithLMHead.from_pretrained(checkpoint)\n",
" model.to(args.device)\n",
" result = evaluate(args, model, tokenizer, df_trn, df_val, prefix=prefix)\n",
" result = dict((k + "{}".format(global_step), v) for k, v in result.items())\n",
" results.update(result)\n",
"\n",
" return results"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00024-93a983e5-e32a-474e-8b40-a2a26cd4d168",
"deepnote_cell_type": "markdown",
"id": "7NWvkdR-XHeB"
},
"source": [
"## Run the Main Function"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"cell_id": "00025-280c69e2-0adf-408f-bba7-9075e92fd669",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 637,
"referenced_widgets": [
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"9d87baf8169345d09dfe71e42cdd2015",
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"c8a083f1bb494fcbb015b42e5e9f95ec",
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"f19e22f3971c4d8db24ba74f05973a71",
"2c844cfc647340108675be0a9f183f19",
"c6448112d0854a82a664eebb777ca3ac",
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"b2a222fb8b074c37a88196150c160358",
"932742e77f664ac8951f8ab0726a2ed3",
"5a7abe7e18f543e99633d0fc8703dece",
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]
},
"deepnote_cell_type": "code",
"id": "e61zo2JtXGNX",
"outputId": "07f1e741-9c0e-4662-806e-96ddb75e7ed6"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"05/11/2023 16:44:27 - WARNING - main - Process rank: -1, device: cuda, n_gpu: 4, distributed training: False, 16-bits training: False\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c2d200b9e5d14bf18a7591a66958c13c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/642 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f1b4dfc29b34622bb5fc6aa29eba9f1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2ad8a37732524090b55e7b964ff5d458",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)olve/main/vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"Downloading (…)olve/main/merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
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"Downloading pytorch_model.bin: 0%| | 0.00/863M [00:00<?, ?B/s]"
]
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"text/plain": [
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]
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{
"name": "stderr",
"output_type": "stream",
"text": [
"05/11/2023 16:48:36 - INFO - main - Training/evaluation parameters <main.Args object at 0x7efb15dc9130>\n",
"05/11/2023 16:48:36 - INFO - main - Creating features from dataset file at cached\n",
"05/11/2023 16:48:36 - INFO - main - Saving features into cached file cached/gpt2_cached_lm_512\n",
"/home/suyeon/.conda/envs/seoul/lib/python3.8/site-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set no_deprecation_warning=True to disable this warning\n",
" warnings.warn(\n",
"05/11/2023 16:48:36 - INFO - main - * Running training *\n",
"05/11/2023 16:48:36 - INFO - main - Num examples = 194\n",
"05/11/2023 16:48:36 - INFO - main - Num Epochs = 4\n",
"05/11/2023 16:48:36 - INFO - main - Instantaneous batch size per GPU = 4\n",
"05/11/2023 16:48:36 - INFO - main - Total train batch size (w. parallel, distributed & accumulation) = 16\n",
"05/11/2023 16:48:36 - INFO - main - Gradient Accumulation steps = 1\n",
"05/11/2023 16:48:36 - INFO - main - Total optimization steps = 48\n"
]
},
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"text/plain": [
"Epoch: 0%| | 0/4 [00:00<?, ?it/s]"
]
},
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"text/plain": [
"Iteration: 0%| | 0/12 [00:00<?, ?it/s]"
]
},
"metadata": {},
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/suyeon/.conda/envs/seoul/lib/python3.8/site-packages/torch/nn/parallel/functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
" warnings.warn('Was asked to gather along dimension 0, but all '\n"
]
},
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"Iteration: 0%| | 0/12 [00:00<?, ?it/s]"
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"text": [
"05/11/2023 16:49:40 - INFO - main - global_step = 48, average loss = 2.4101458912094436\n",
"05/11/2023 16:49:40 - INFO - main - Saving model checkpoint to ./content/output-medium\n",
"05/11/2023 16:49:45 - INFO - main - Evaluate the following checkpoints: ['./content/output-medium']\n",
"05/11/2023 16:49:47 - INFO - main - Creating features from dataset file at cached\n",
"05/11/2023 16:49:47 - INFO - main - Saving features into cached file cached/gpt2_cached_lm_512\n",
"05/11/2023 16:49:47 - INFO - main - * Running evaluation *\n",
"05/11/2023 16:49:47 - INFO - main - Num examples = 22\n",
"05/11/2023 16:49:47 - INFO - main - Batch size = 16\n"
]
},
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"text": [
"05/11/2023 16:49:48 - INFO - main - * Eval results *\n",
"05/11/2023 16:49:48 - INFO - main - perplexity = tensor(7.8732)\n"
]
},
{
"data": {
"text/plain": [
"{'perplexity': tensor(7.8732)}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main(trn_df, val_df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00026-8b7d6f57-8a8b-4edc-b612-2e6bace87775",
"deepnote_cell_type": "markdown",
"id": "YRpQ_n2zXQj-"
},
"source": [
"## Load the Trained Model"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"cell_id": "00027-e90f86fc-a5b4-41c5-a30b-70d09c519eac",
"colab": {
"base_uri": "https://localhost:8080/"
},
"deepnote_cell_type": "code",
"id": "HGw3qgfaXQHX",
"outputId": "8629c457-b8d7-40d3-9c28-6d23a31bde2a"
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium')\n",
"model = AutoModelWithLMHead.from_pretrained('./content/output-medium')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"cell_id": "00028-10e2eee2-c908-4363-a238-114ef84bd1ac",
"colab": {
"base_uri": "https://localhost:8080/"
},
"deepnote_cell_type": "code",
"id": "lAWsiAvNXbxd",
"outputId": "fa4083fe-189b-456c-b64b-e981f3908763"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set padding_side='left' when initializing the tokenizer.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
">> User: I'd like a general check-up.\n",
"Bot: I am currently at work so I will give you a checkup at lunch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set padding_side='left' when initializing the tokenizer.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
">> User: I have severe head pain.\n",
"Bot: Take a rest after the checkup. If you feel better, take a rest.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set padding_side='left' when initializing the tokenizer.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
">> User: it keeps me awake at night. it's a dull, heavy pain.\n",
"Bot: Are you taking a rest?\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set padding_side='left' when initializing the tokenizer.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
">> User: Yes\n",
"Bot: Take it as soon as you feel itchy.\n"
]
}
],
"source": [
"# Let's chat for 4 lines\n",
"for step in range(4):\n",
" Q = input(">> User:")\n",
" # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
" new_user_input_ids = tokenizer.encode(Q + tokenizer.eos_token, return_tensors='pt')\n",
" # print(new_user_input_ids)\n",
"\n",
" # append the new user input tokens to the chat history\n",
" bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
"\n",
" # generated a response while limiting the total chat history to 1000 tokens, \n",
" chat_history_ids = model.generate(\n",
" bot_input_ids, max_length=200,\n",
" pad_token_id=tokenizer.eos_token_id, \n",
" no_repeat_ngram_size=3, \n",
" do_sample=True, \n",
" top_k=100, \n",
" top_p=0.7,\n",
" temperature=0.8\n",
" )\n",
" \n",
" # pretty print last ouput tokens from bot\n",
" print(f">> User: {Q}")\n",
" print("Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00029-374b7bb4-c23b-47eb-8a1d-8dcedc2ce922",
"deepnote_cell_type": "markdown",
"id": "ANSQlQezXqwn"
},
"source": [
"## Push Model to Hugging Face"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cell_id": "00030-77ba7cd8-d44c-4428-99db-096858e0a2ed",
"colab": {
"base_uri": "https://localhost:8080/"
},
"deepnote_cell_type": "code",
"id": "VgnHRgHKXwDd",
"outputId": "b15594ac-7aca-4136-8110-b9ab4da83bb5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"git-lfs is already the newest version (2.3.4-1).\n",
"The following package was automatically installed and is no longer required:\n",
" libnvidia-common-460\n",
"Use 'sudo apt autoremove' to remove it.\n",
"0 upgraded, 0 newly installed, 0 to remove and 40 not upgraded.\n"
]
}
],
"source": [
"!sudo apt-get install git-lfs"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"cell_id": "00031-26784c7e-5e24-49f5-9ee4-44a34f5feaec",
"deepnote_cell_type": "code",
"id": "uhqMtvfmXei8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using tokenizers before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using tokenizers before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
}
],
"source": [
"!git config --global user.email "sy92160776@gmail.com"\n",
"# Tip: using the same email as your huggingface.co account will link your commits to your profile\n",
"!git config --global user.name "kangsuyeon01""
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"cell_id": "00032-f231ee5a-2498-4b7b-be97-395c83dd768f",
"deepnote_cell_type": "code",
"id": "tfUsrKR7YLT1"
},
"outputs": [],
"source": [
"MY_MODEL_NAME = 'DialoGPT-medium-HospitalBot'\n",
"with open('/home/suyeon/APIKEY/HuggingFace_API.txt', 'rt') as f:\n",
" HUGGINGFACE_API_KEY = f.read().strip()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"cell_id": "00033-c04acb90-874a-45bc-a722-b3af26bdaf79",
"deepnote_cell_type": "code",
"id": "_65nsiLcYNXI"
},
"outputs": [
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"version_major": 2,
"version_minor": 0
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"text/plain": [
"Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
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"text/plain": [
"pytorch_model.bin: 0%| | 0.00/1.44G [00:00<?, ?B/s]"
]
},
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{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/sillon/DialoGPT-medium-HospitalBot/commit/3a0577a28685b5a54c2714cc97d50e303f716331', commit_message='Upload tokenizer', commit_description='', oid='3a0577a28685b5a54c2714cc97d50e303f716331', pr_url=None, pr_revision=None, pr_num=None)"
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},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"model.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)\n",
"tokenizer.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_id": "00034-577d32f5-0b28-4ae4-9712-8c8dfd369e61",
"deepnote_cell_type": "markdown",
"id": "D_XfXTCrZKmO"
},
"source": [
"## All Done!"
]
},
{
"cell_type": "markdown",
"metadata": {
"created_in_deepnote_cell": true,
"deepnote_cell_type": "markdown",
"tags": []
},
"source": [
"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=40d91331-6c83-44f4-b569-6fa68d59a8f8' target="_blank">\n",
" \n",
"Created in Deepnote"
]
}
],
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