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import gradio as gr
import torch
import os
from huggingface_hub import login
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
from transformers import AutoTokenizer
README = """
# Movie Review Score Discriminator
It is a program that classifies whether it is positive or negative by entering movie reviews.
You can choose between the Korean version and the English version.
## Usage
"""
model_name = "roberta-base"
learning_rate = 5e-5
batch_size_train = 64
step = 1900
login(token='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')
file_name = "model-{}.pt".format(step)
state_dict = torch.load(os.path.join(file_name))
id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}
title = "Movie Review Score Discriminator"
description = "It is a program that classifies whether it is positive or negative by entering movie reviews. You can choose between the Korean version and the English version."
examples = ["the greatest musicians ", "cold movie "]
def tokenized_data(tokenizer, inputs):
return tokenizer.batch_encode_plus(
inputs,
return_tensors="pt",
padding="max_length",
max_length=64,
truncation=True)
def greet(text):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
model_name, num_labels=2, id2label=id2label, label2id=label2id,
state_dict=state_dict
)
inputs = tokenized_data(tokenizer, text)
model.eval()
with torch.no_grad():
# logits.shape = torch.Size([ batch_size, 2 ])
logits = model(input_ids=inputs[0], attention_mask=inputs[1]).logits
return logits
demo1 = gr.Interface.load("models/cardiffnlp/twitter-roberta-base-sentiment", inputs="text", outputs="text",
title=title, theme="peach",
allow_flagging="auto",
description=description, examples=examples)
# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo2 = gr.Interface(fn=greet, inputs="text", outputs="text",
title=title, theme="peach",
allow_flagging="auto",
description=description, examples=examples)
if __name__ == "__main__":
demo1.launch() |