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import streamlit as st
import numpy as np
import pandas as pd
import torch
import transformers
from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
@st.cache(suppress_st_warning=True, hash_funcs={transformers.AutoTokenizer: lambda _: None})
def load_tok_and_model():
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(".")
return tokenizer, model
CATEGORIES = ["Computer Science", "Economics", "Electrical Engineering", "Mathematics",
"Q. Biology", "Q. Finances", "Statistics" , "Physics"]
@st.cache(suppress_st_warning=True, hash_funcs={transformers.AutoTokenizer: lambda _: None})
def forward_pass(title, abstract, tokenizer, model):
title_tensor = torch.tensor(tokenizer(title, padding="max_length", truncation=True, max_length=32)['input_ids'])
abstract_tensor = torch.tensor(tokenizer(abstract, padding="max_length", truncation=True, max_length=480)['input_ids'])
embeddings = torch.cat((title_tensor, abstract_tensor))
assert embeddings.shape == (512,)
with torch.no_grad():
logits = model(embeddings[None])['logits'][0]
assert logits.shape == (8,)
probs = torch.softmax(logits).data.cpu().numpy()
return probs
st.title("Classification of arXiv articles' main topic")
st.markdown("Please provide both summary and title when possible")
tokenizer, model = load_tok_and_model()
title = st.text_area(label='Title', height=200)
abstract = st.text_area(label='Abstract', height=200)
button = st.button('Run classifier')
if button:
probs = forward_pass(title, abstract, tokenizer, model)
st.write(probs)
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