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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModel, pipeline
from torch import nn
st.markdown("### Articles classificator.")
@st.cache(allow_output_mutation=True)
def get_tokenizer():
model_name = 'microsoft/deberta-v3-small'
return AutoTokenizer.from_pretrained(model_name)
tokenizer = get_tokenizer()
class devops_model(nn.Module):
def __init__(self):
super(devops_model, self).__init__()
self.bert = None
self.fc = nn.Sequential(
nn.Linear(768, 768),
nn.ReLU(),
nn.Dropout(0.3),
nn.BatchNorm1d(768),
nn.Linear(768, 5),
nn.LogSoftmax(dim=-1)
)
def forward(self, train_batch):
emb = self.bert(**train_batch)['last_hidden_state'].mean(axis=1)
return self.fc(emb)
@st.cache
def LoadModel():
return torch.load('model_full.pt', map_location=torch.device('cpu'))
model = LoadModel()
classes = ['Computer Science', 'Mathematics', 'Physics', 'Quantitative Biology', 'Statistics']
def process(title, summary):
text = title + summary
if not text.strip():
return ''
model.eval()
lines = [text]
X = tokenizer(lines, padding=True, truncation=True, return_tensors="pt")
out = model(X)
probs = torch.exp(out[0])
sorted_indexes = torch.argsort(probs, descending=True)
probs_sum = idx = 0
res = []
while probs_sum < 0.95:
prob_idx = sorted_indexes[idx]
prob = probs[prob_idx]
res.append(f'{classes[prob_idx]}: {prob:.3f}')
idx += 1
probs_sum += prob
return res
title = st.text_area("Title", height=30)
summary = st.text_area("Summary", height=180)
for string in process(title, summary):
st.markdown(string) |