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from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer, DataCollatorForSeq2Seq
import numpy as np
import torch
from huggingface_hub import login
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def preprocesser(tokenizer):
def preprocess_function(examples):
inputs = [f"{examples['question_text'][i]}\n{doc}" for i,
doc in enumerate(examples["document_text"])]
model_inputs = tokenizer(inputs, truncation=True)
labels = tokenizer(
text_target=examples["summarization_text"], truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
return preprocess_function
def training(output='seonglae/resrer', dataset_id='seonglae/resrer-nq', checkpoint='google/pegasus-x-base',
token=None):
if token is not None:
login(token=token)
# Load model
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# Load dataset
dataset = load_dataset(dataset_id, split='train')
splited_dataset = dataset.train_test_split(test_size=0.2)
tokenized_dataset = splited_dataset.map(
preprocesser(tokenizer), batched=True)
print(tokenized_dataset["train"][0])
# Train
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
training_args = Seq2SeqTrainingArguments(
output_dir=output,
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=2,
optim='adamw_hf',
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=4,
push_to_hub=True,
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()
# Push
if token is not None:
tokenizer.push_to_hub(f"{output}", token=token)
model.push_to_hub(f"{output}", token=token)
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