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#mT5 Deployment OtherLanguage2ID
# library
import gradio as gr
import tensorflow as tf
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# function to run
def run_model(input_text,
min_length):
#mT5 Transformer
model_name = "csebuetnlp/mT5_m2m_crossSum_enhanced"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
#get id language
get_lang_id = lambda lang: tokenizer._convert_token_to_id(
model.config.task_specific_params["langid_map"][lang][1])
target_lang = "indonesian" # for a list of available language names see below / defaul define
# preprocessing text input
input_text = str(input_text)
input_text = ' '.join(input_text.split()) # hapus white space dan
#encode input to vector
input_ids = tokenizer(input_text,
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512)["input_ids"]
#generate input
output_ids = model.generate(
input_ids=input_ids,
decoder_start_token_id=get_lang_id(target_lang),
min_length=min_length,
max_length=512,
no_repeat_ngram_size=2,
repetition_penalty=1.5,
temperature= 0.5, # [0.8 if temper == "Creative" else 0.2 if temper == "Better" else 0.2][0],
early_stopping=True,
num_beams=4)[0]
#decode output to text
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
return ' '.join(summary.split(' ')[1:])# get output to str
# end
#example
# contoh = [["TAMPAN"]]
#judul
title = "Text Summarization ID"
#deskripsi
description = "Demo for Text Summarization ID. Models are mT5"
#footer
# article = "<p style='text-align: center'><a href='https://github.com/sultanbst123/Text_summarization-id2id' target='_blank'><u>Untuk penjelasan lihat di repo ku</u> 😁</a></p>"
#run gradio
gr.Interface(
fn=run_model,
#input text
inputs=[
gr.inputs.Textbox(
lines=3,
placeholder="Ketik disini...",
label="Text",
),
gr.inputs.Slider(
minimum=100,
maximum=400,
step=10,
default=150,
label="Max Length(Maximal Sentence Length)",
),
],
#output text
outputs=
gr.outputs.Textbox(
label="Output text",
),
title=title,
description=description,
# article=article,
# examples=contoh
).launch(debug = True)
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