Upload test_t5.py

test_t5.py

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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}')