commit from

app.py

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+import streamlit as st
+import numpy as np
+import torch
+
+my_model_name = "istassiy/ysda_2022_ml2_hw3_distilbert_base_uncased"
+
+arxiv_code_to_topic = {
+  'cs' : 'computer science',
+
+  'q-bio' : 'biology',
+
+  'q-fin' : 'finance',
+
+  'astro-ph' : 'physics',
+  'cond-mat' : 'physics',
+  'gr-qc' : 'physics',
+  'hep-ex' : 'physics',
+  'hep-lat' : 'physics',
+  'hep-ph' : 'physics',
+  'hep-th' : 'physics',
+  'math-ph' : 'physics',
+  'nlin' : 'physics',
+  'nucl-ex' : 'physics',
+  'nucl-th' : 'physics',
+  'quant-ph' : 'physics',
+  'physics' : 'physics',
+
+  'eess' : 'electrical engineering',
+
+  'econ' : 'economics',
+
+  'math' : 'mathematics',
+
+  'stat' : 'statistics',
+}
+
+sorted_arxiv_topics = sorted(set(arxiv_code_to_topic.values()))
+
+NUM_LABELS = len(sorted_arxiv_topics)
+
+@st.cache
+def load_model():
+  from transformers import AutoTokenizer, AutoModel
+  tokenizer = AutoTokenizer.from_pretrained(my_model_name)
+  model = AutoModel.from_pretrained(my_model_name)
+  return tokenizer, model
+
+def get_top_predictions(predictions):
+  probs = (torch.sigmoid(predictions) > 0).int().numpy()
+  probs = probs / np.sum(probs)
+
+  res = {}
+  total_prob = 0
+  for topic, prob in zip(sorted_arxiv_topics, probs):
+    total_prob += prob
+    res[topic] = prob
+    if total_prob > 0.95:
+      break
+  return res
+
+tokenizer, model = load_model()
+
+st.markdown("# Scientific paper classificator")
+st.markdown(
+    "Fill in paper summary and / or title below:",
+    unsafe_allow_html=False
+)
+
+paper_title = st.text_area("Paper title")
+paper_summary = st.text_area("Paper abstract")
+
+if not paper_title and not paper_summary:
+  st.markdown(f"Must have non-empty title or summary")
+else:
+  raw_predictions = model(**tokenizer(paper_title + "." + paper_summary))
+  results = get_top_predictions(raw_predictions)
+  st.markdown(f"{results}")