train models a little

app.py

@@ -5,10 +5,12 @@ import streamlit as st
 
 from transformers import BertTokenizer
 
-st.markdown("### Из какой серии статья")
+st.markdown("### Из какой области статья")
+link = 'https://www.clipartmax.com/png/middle/87-873210_akinator-with-transparent-background.png'
+st.markdown(f"<img width=200px src='{link}'>", unsafe_allow_html=True)
 # st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
 
-# from transformers import pipeline
+# from transformers import 
 
 # pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl")
 
@@ -34,7 +36,7 @@ class BERTClass(torch.nn.Module):
         out = self.l6(out)
         return out
 
-@st.cache
+@st.cache(suppress_st_warning=True,  allow_output_mutation=True)
 def load_bert():
     model = BERTClass(bert_path='bert_pretrained')
     model.load_state_dict(torch.load('bert_pretrained.pt'))
@@ -81,7 +83,7 @@ class TinyBERTClass(torch.nn.Module):
         return out
 
 
-@st.cache(suppress_st_warning=True)
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def load_tiny_bert():
     model = TinyBERTClass(path = 'tiny_bert_pretrained')
     model.load_state_dict(torch.load('tiny_bert.pt'))

bert_pretrained.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3e345e8a6369d704837929868411f22317571faf3ab99dbd69e502d24e929a8
+oid sha256:054376ff0ce0e179be7a13ca753df127732604e7e3c530fbed227be4214de814
 size 441192617