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from transformers import T5ForConditionalGeneration, T5TokenizerFast, pipeline
from transformers.models.f_t5.modeling_t5 import \
T5ForConditionalGeneration as FT5ForConditionalGeneration
from transformers.models.f_t5.tokenization_t5_fast import \
T5TokenizerFast as FT5TokenizerFast
import json
with open('examples.json') as f:
examples = json.load(f)['article']
model_name = "flax-community/ft5-cnn-dm"
ft5_model = FT5ForConditionalGeneration.from_pretrained(model_name)
ft5_tokenizer = FT5TokenizerFast.from_pretrained(model_name)
ft5_summarizer = pipeline(
"summarization", model=ft5_model, tokenizer=ft5_tokenizer, framework="pt"
)
#model_name = 'flax-community/t5-base-cnn-dm'
#t5_model = T5ForConditionalGeneration.from_pretrained(model_name)
#t5_tokenizer = T5TokenizerFast.from_pretrained(model_name)
#predict_t5 = get_predict(t5_model, t5_tokenizer)
def fn(text, do_sample, min_length, max_length,temperature, top_p):
out = ft5_summarizer(text, do_sample=do_sample, min_length=min_length,
max_length=max_length, temperature=temperature, top_p=top_p,
truncation=True)
return out[0]['summary_text']
import gradio as gr
interface = gr.Interface(
fn,
inputs=[
gr.inputs.Textbox(lines=10, label='text'),
gr.inputs.Checkbox(label='do_sample'),
gr.inputs.Slider(1, 128, step=1, default=64, label='min_length'),
gr.inputs.Slider(1, 128, step=1, default=64, label='max_length'),
gr.inputs.Slider(0.0, 1.0, step=0.1, default=1, label='temperature'),
gr.inputs.Slider(0.0, 1.0, step=0.1, default=1, label='top_p'),
],
outputs=gr.outputs.Textbox(),
#server_port=8080,
#server_name='0.0.0.0',
examples=[[ex] for ex in examples],
title='F-T5 News Summarization',
description="""
F-T5 is a hybrid encoder-decoder model based on T5 and FNet.
The model architecture is based on T5, except the encoder self attention is replaced by fourier transform as in FNet.
The model is pre-trained on openwebtext, fine-tuned on CNN/DM.
"""
)
interface.launch()
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