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import gradio as gr
import torch
from transformers import (
MT5ForConditionalGeneration,
MT5TokenizerFast,
pipeline,
)
MODEL_ID = "tacab/mt5-beero_somali"
# Load tokenizer & model
tokenizer = MT5TokenizerFast.from_pretrained(MODEL_ID)
model = MT5ForConditionalGeneration.from_pretrained(MODEL_ID)
device = 0 if torch.cuda.is_available() else -1
# Build pipeline
qa = pipeline(
"text2text-generation",
model=model,
tokenizer=tokenizer,
device=device,
max_new_tokens=200,
min_length=100,
num_beams=4,
length_penalty=0.7,
no_repeat_ngram_size=3,
early_stopping=False,
)
def answer(question: str) -> str:
prompt = f"Su'aal: {question}"
out = qa(prompt)
return out[0]["generated_text"]
demo = gr.Interface(
fn=answer,
inputs=gr.Textbox(lines=2, placeholder="Gali su'aashaada...", label="Su'aal"),
outputs=gr.Textbox(label="Jawaab"),
title="Beero Somali Q&A",
description="Su'aal–Jawaab module ku saleysan tacab/mt5-beero_somali",
)
if __name__ == "__main__":
demo.launch()
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