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from sklearn.model_selection import train_test_split
from datasets import Dataset, DatasetDict, load_dataset, interleave_datasets, load_from_disk
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig, TrainingArguments, Trainer
import torch
import time
import evaluate
import pandas as pd
import numpy as np
model_name = 't5-small'
tokenizer = AutoTokenizer.from_pretrained(model_name)
original_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
original_model = original_model.to('cuda')
data = pd.read_csv("text-to-sql_from_spider.csv")
# print(data)
dataset = load_dataset("csv", data_files="text-to-sql_from_spider.csv")
dataset = dataset["train"].train_test_split(test_size=0.4)
test_dataset = dataset["test"].train_test_split(test_size=0.5)
print(dataset["train"])
dataset = DatasetDict({"train": dataset["train"],
"test": test_dataset["test"],
"validation": test_dataset["train"]})
def tokenize_function(example):
# print(len(example["question"]))
start_prompt = "Tables:\n"
middle_prompt = "\n\nQuestion:\n"
end_prompt = "\n\nAnswer:\n"
data_zip = zip(example['schema'], example['question'])
prompt = [start_prompt + context + middle_prompt + question + end_prompt for context, question in data_zip]
example['input_ids'] = tokenizer(prompt, padding="max_length", truncation=True, return_tensors="pt").input_ids
example['labels'] = tokenizer(example['sql'], padding="max_length", truncation=True, return_tensors="pt").input_ids
# print(prompt[0])
# print()
return example
try:
tokenized_datasets = load_from_disk("tokenized_datasets")
print("Loaded Tokenized Dataset")
except:
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets = tokenized_datasets.remove_columns(['sql', 'question', 'schema'])
tokenized_datasets.save_to_disk("tokenized_datasets")
print("Tokenized and Saved Dataset")
# tokenized_datasets = dataset.map(tokenize_function, batched=True)
# print(tokenized_datasets["train"][0]["input_ids"])
try:
finetuned_model = AutoModelForSeq2SeqLM.from_pretrained("finetuned_model_2_epoch")
finetuned_model = finetuned_model.to('cuda')
to_train = False
except:
to_train = True
finetuned_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
finetuned_model = finetuned_model.to('cuda')
tokenizer = AutoTokenizer.from_pretrained(model_name)
if to_train:
output_dir = f'./sql-training-{str(int(time.time()))}'
training_args = TrainingArguments(
output_dir=output_dir,
learning_rate=5e-3,
num_train_epochs=2,
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=16, # batch size for evaluation
weight_decay=0.01,
logging_steps=50,
evaluation_strategy='steps', # evaluation strategy to adopt during training
eval_steps=500, # number of steps between evaluation
)
trainer = Trainer(
model=finetuned_model,
args=training_args,
train_dataset=tokenized_datasets['train'],
eval_dataset=tokenized_datasets['validation'],
)
trainer.train()
finetuned_model.save_pretrained("finetuned_model_2_epoch")
questions = dataset['test']['question']
contexts = dataset['test']['schema']
human_baseline_answers = dataset['test']['sql']
original_model_answers = []
finetuned_model_answers = []
for idx, question in enumerate(questions):
prompt = f"""Tables:
{contexts[idx]}
Question:
{question}
Answer:
"""
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
input_ids = input_ids.to('cuda')
human_baseline_text_output = human_baseline_answers[idx]
original_model_outputs = original_model.generate(input_ids=input_ids,
generation_config=GenerationConfig(max_new_tokens=300))
original_model_text_output = tokenizer.decode(original_model_outputs[0], skip_special_tokens=True)
original_model_answers.append(original_model_text_output)
finetuned_model_outputs = finetuned_model.generate(input_ids=input_ids,
generation_config=GenerationConfig(max_new_tokens=300))
finetuned_model_text_output = tokenizer.decode(finetuned_model_outputs[0], skip_special_tokens=True)
finetuned_model_answers.append(finetuned_model_text_output)
zipped_summaries = list(zip(human_baseline_answers, original_model_answers, finetuned_model_answers))
df = pd.DataFrame(zipped_summaries,
columns=['human_baseline_answers', 'original_model_answers', 'finetuned_model_answers'])
rouge = evaluate.load('rouge')
original_model_results = rouge.compute(
predictions=original_model_answers,
references=human_baseline_answers[0:len(original_model_answers)],
use_aggregator=True,
use_stemmer=True,
)
print('ORIGINAL MODEL:')
print(original_model_results)
finetuned_model_results = rouge.compute(
predictions=finetuned_model_answers,
references=human_baseline_answers[0:len(finetuned_model_answers)],
use_aggregator=True,
use_stemmer=True,
)
print('FINE-TUNED MODEL:')
print(finetuned_model_results)
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