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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')
finetuned_model = AutoModelForSeq2SeqLM.from_pretrained("finetuned_model_2_epoch")
finetuned_model = finetuned_model.to('cuda')
data = pd.read_csv("text-to-sql_from_spider.csv")
question = data["question"][0] #dataset['test'][index]['question']
context = "CREATE TABLE table_name_11 (date VARCHAR, away_team VARCHAR)" #dataset['test'][index]['schema']
answer = data["sql"][0] #dataset['test'][index]['sql']
prompt = f"""Tables:
{context}
Question:
{question}
Answer:
"""
inputs = tokenizer(prompt, return_tensors='pt')
inputs = inputs.to('cuda')
output = tokenizer.decode(
finetuned_model.generate(
inputs["input_ids"],
max_new_tokens=200,
)[0],
skip_special_tokens=True
)
dash_line = '-'*100
print(dash_line)
print(f'INPUT PROMPT:\n{prompt}')
print(dash_line)
print(f'BASELINE HUMAN ANSWER:\n{answer}\n')
print(dash_line)
print(f'MODEL GENERATION - ZERO SHOT:\n{output}') |