|
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
|
|
|
def get_pipe(name): |
|
tokenizer = AutoTokenizer.from_pretrained(name) |
|
model = AutoModelForSeq2SeqLM.from_pretrained(name) |
|
pipe = pipeline( |
|
"summarization", model=model, tokenizer=tokenizer, framework="pt" |
|
) |
|
return pipe |
|
model_names = ['bigscience/T0_3B'] |
|
|
|
pipes = [get_pipe(name) for name in model_names] |
|
def _fn(text, do_sample, min_length, max_length, temperature, top_p, pipe): |
|
out = pipe( |
|
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"] |
|
def fn(*args): |
|
return [_fn(*args, pipe=pipe) for pipe in pipes] |
|
import gradio as gr |
|
interface = gr.Interface( |
|
fn, |
|
inputs=[ |
|
gr.inputs.Textbox(lines=10, label="input text"), |
|
gr.inputs.Checkbox(label="do_sample", default=True), |
|
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(label=f"output by {name}") for name in model_names |
|
], |
|
|
|
title="T0 playground", |
|
description=""" |
|
This is a playground for playing around with T0 models. |
|
See https://huggingface.co/bigscience/T0 for more details |
|
""", |
|
) |
|
interface.launch() |
