Added v1 of srl demo

app.py

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+import streamlit as st
+import transformers as tr
+import spacy as sp
+
+@st.cache
+def load_pipeline(name: str):
+    return tr.pipeline('token-classification', model=name)
+
+pipeline = load_pipeline('Rexhaif/rubert-base-srl-seqlabeling')
+
+def convert_to_spacy(text, result):
+    output = {
+        'text': text,
+        'title': None
+    }
+    ents = []
+    for res in result:
+        if not res['word'].startswith("##"):
+            ents.append({
+                'start': res['start'],
+                'end': res['end'],
+                'label': res['entity'].replace("B-", "")
+            })
+        else:
+            ents[-1]['end'] = res['end']
+            
+    output['ents'] = ents
+    return output
+
+
+colors = {
+    'PREDICATE': "#80bdff",
+    'КАУЗАТИВ': "#73ffbe",
+    'КАУЗАТОР': "#ff5b5e",
+    'ЭКСПЕРИЕНЦЕР': "#efff42",
+    'ДРУГОЕ': "#924fff",
+    'ИНСТРУМЕНТ': "#28fff1"
+}
+options = {
+    'ents': list(colors.keys()), 'colors': colors
+}
+
+st.title("Semantic Role Labeling for Russian Language")
+
+st.header("Type your sentence to see predicate, arguments and their roles")
+
+text = st.text_input('Sentence', 'представители силовых ведомств удивлены такой наглости')
+
+result = pipeline(text)
+html = sp.displacy.render(
+    convert_to_spacy(text, result=result),
+    style='ent',
+    manual=True,
+    options=options,
+    jupyter=False
+)
+st.markdown(html, unsafe_allow_html=True)

requirements.txt

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+spacy==3.2.0
+transformers>="4.0.0"