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# Streamlit app to highlight NER entities
import random
import streamlit as st
from datasets import load_dataset
from annotated_text import annotated_text
# Load data
ds = load_dataset("hs-knowledge/hateval_enriched")
# Show highlighted ner entities in a tweet
def display_text(example):
# Use annotated_text to show entities
text = example["text"]
# Sort entities by start
entities = sorted(example["entities"], key=lambda x: x["start"])
for entity in entities:
entity_text = entity["text"]
# find in text
start = text.find(entity_text)
end = start + len(entity_text)
entity["start"] = start
entity["end"] = end
# Chunk text
if len(entities) == 0:
annotated_text(*[text])
return
chunks = []
last_index = 0
for i in range(len(entities)):
entity = entities[i]
start, end = entity["start"], entity["end"]
if last_index < start:
chunk_before_entity = text[last_index : entity["start"]]
chunks.append((chunk_before_entity, None))
chunks.append((entity["text"], entity["type"]))
last_index = end
if last_index < len(text):
chunks.append((text[last_index:], None))
# description = entity["kg_result"]["detailedDescription"]["articleBody"]
chunks = [(c, t) if t is not None else c for c, t in chunks]
annotated_text(*chunks)
# Get first 1000 examples
elements = random.choices(range(len(ds["train"])), k=50)
ds["train"] = ds["train"].select(elements)
for ex in ds["train"]:
st.write("=" * 80)
display_text(ex)
with st.expander("Show entities"):
for ent in ex["entities"]:
entity_name = ent["text"]
entity_type = ent["type"]
entity_description = ent["kg_result"]["detailedDescription"]["articleBody"]
st.write(f"{entity_name} ({entity_type}): {entity_description}")
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