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88bca53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
# Load model
tokenizer = AutoTokenizer.from_pretrained("./final_model")
model = AutoModelForSeq2SeqLM.from_pretrained("./final_model")
# Move to CPU if needed
device = torch.device('cpu')
model = model.to(device)
# Test function
def ask_question(question, context):
input_text = f"question: {question} context: {context}"
inputs = tokenizer(input_text, return_tensors="pt", max_length=384, truncation=True)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(**inputs, max_length=128, num_beams=3)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Test examples
test_cases = [
{
"context": "In the name of Allah, the Most Gracious, the Most Merciful.",
"question": "What are the attributes of Allah mentioned?"
},
{
"context": "And We have certainly made the Quran easy for remembrance, so is there any who will remember?",
"question": "What has Allah made easy?"
}
]
print("Testing model:\n")
for test in test_cases:
answer = ask_question(test["question"], test["context"])
print(f"Context: {test['context']}")
print(f"Question: {test['question']}")
print(f"Answer: {answer}")
print("-" * 80)
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