DataLoaderfrom transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmupfrom tqdm import tqdm, trangeimport torch.nn.functional as Fimport csv### Prepare datalyrics = pd.read_csv('lyrics-data.csv'
def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_scheduler, config): """Single training step.""" args = get_args() timers = get_timers() # Set grad to zero. for model_chunk in model: model_chunk.zero_grad_buffer() optimizer.zero_grad() # Forward pass....
iinenumerate(self.X):try:self.X[idx]="<startofstring> "+i+" <bot>: "+self.X[idx+1]+" <endofstring>"except:breakforiinself.data:forjini['dialog
Training_GPT_2_Using_TPUs.ipynb Adding fire dependency Nov 11, 2019 decode.py Don't print an extra newline in decode.py Dec 24, 2019 download_model.py Adding Server component and project description May 3, 2020 encode.py Fix encode.py Mar 22, 2019 prepare_dataset.py Adding Server compon...
Getting Started Prepare the Data and Vocabulary We have provided the relevant datasets, which can be downloaded from the following links: Vocabulary JSON Merges File Binary Data File Index Data ...
2Tokenizerimportnumpy as npimportrandomimporttorchfromtorch.utils.dataimportDataset, DataLoaderfromtransformersimportGPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmupfromtqdmimporttqdm, trangeimporttorch.nn.functional as Fimportcsv### Prepare datalyrics = pd.read_csv('lyrics-data....
# step 2: 设置8bit训练pretrained_model=prepare_model_for_int8_training(pretrained_model,output_embedding_layer_name="lm_head")# for name, param in pretrained_model.named_parameters():# # freeze base model's layers# param.requires_grad = False# if getattr(pretrained_model, "is_loaded_...
importpandasaspdfromtransformersimportGPT2LMHeadModel,GPT2Tokenizerimportnumpyasnpimportrandomimporttorchfromtorch.utils.dataimportDataset,DataLoaderfromtransformersimportGPT2Tokenizer,GPT2LMHeadModel,AdamW,get_linear_schedule_with_warmupfromtqdmimporttqdm,trangeimporttorch.nn.functionalasFimportcsv###Preparedatalyr...
import torch, os, re, pandasaspd, jsonfromsklearn.model_selection import train_test_splitfromtransformers import DataCollatorForLanguageModeling, DataCollatorWithPadding, GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments, AutoConfigfromdatasets import Dataset ...
# Step 5: Prepare DataLoader seq_length = 10 batch_size = 8 dataset = MultiStockDataset(df, seq_length=seq_length) train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) # Step 6: Set up Model and Optimizer device = torch.device("cuda:0" if torch.cuda.is_available(...