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import numpy as np
import pandas as pd
import transformers
import torch
import tokenizers
import streamlit as st
NUM_LABELS = 15
labels_names = {
0: 'Astrophysics',
1: 'Condensed Matter',
2: 'Computer Science',
3: 'Economics',
4: 'Electrical Engineering and Systems Science',
5: 'General Relativity and Quantum Cosmology',
6: 'High Energy Physics',
7: 'Mathematics',
8: 'Nonlinear Sciences',
9: 'Nuclear Theory',
10: 'General Physics',
11: 'Quantitative Biology',
12: 'Quantitative Finance',
13: 'Quantum Physics',
14: 'Statistics',
}
@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None}, suppress_st_warning=True)
def get_model(model_name, model_path):
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=NUM_LABELS)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
return model, tokenizer
@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None}, suppress_st_warning=True)
def predict(text, tokenizer, model, temperature = 1):
tokens = tokenizer.encode(text)
with torch.no_grad():
logits = model.cpu()(torch.as_tensor([tokens]))[0]
probs = torch.softmax(logits[-1, :] / temperature, dim=-1).data.cpu().numpy()
indexes_descending = np.argsort(probs)[::-1]
percents = 0
preds = []
pred_probs = []
for index in indexes_descending:
preds.append(labels_names[index])
pred_prob = 100 * probs[index]
pred_probs.append(f"{pred_prob:.1f}%")
percents += pred_prob
if percents >= 95:
break
result = pd.DataFrame({'Probability': pred_probs})
result.index = preds
return result
model, tokenizer = get_model('distilbert-base-cased', 'distilbert-checkpoint-10983.bin')
st.title("Yandex School of Data Analysis. ML course")
st.title("Laboratory work 2: classifier of categories of scientific papers")
st.markdown("<img width=200px src='https://m.media-amazon.com/images/I/71XOMSKx8NL._AC_SL1500_.jpg'>", unsafe_allow_html=True)
st.markdown("\n")
st.markdown("Enter the title of the article and its abstract (although, if you really don't want to, you can do with just the title)")
title = st.text_area(label='Title of the article', height=100)
abstract = st.text_area(label='Abstract of the article', height=200)
button = st.button('Go')
if button:
try:
text = ' [ABSTRACT] '.join([title, abstract])
result = predict(text, tokenizer, model)
if len(text) > 10:
st.subheader('Bumblebee thinks, this paper related to')
st.write(result)
else:
st.error("Enter some more info please")
except Exception:
st.error("Ooooops, something went wrong. Try again please and report to me, tg: @vladyur")
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