Konstantin's picture
Add spaces application
8d4c2d8
raw history blame
No virus
7.62 kB
import random
import streamlit as st
from bs4 import BeautifulSoup
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
from transformers_interpret import SequenceClassificationExplainer
# Map model names to URLs
model_names_to_URLs = {
'ml6team/distilbert-base-dutch-cased-toxic-comments':
'https://huggingface.co/ml6team/distilbert-base-dutch-cased-toxic-comments',
'ml6team/robbert-dutch-base-toxic-comments':
'https://huggingface.co/ml6team/robbert-dutch-base-toxic-comments',
}
about_page_markdown = f"""# 🀬 Dutch Toxic Comment Detection Space
Made by [ML6](https://ml6.eu/).
Token attribution is performed using [transformers-interpret](https://github.com/cdpierse/transformers-interpret).
"""
regular_emojis = [
'😐', 'πŸ™‚', 'πŸ‘Ά', 'πŸ˜‡',
]
undecided_emojis = [
'🀨', '🧐', 'πŸ₯Έ', 'πŸ₯΄', '🀷',
]
potty_mouth_emojis = [
'🀐', 'πŸ‘Ώ', '😑', '🀬', '☠️', '☣️', '☒️',
]
# Page setup
st.set_page_config(
page_title="Toxic Comment Detection Space",
page_icon="🀬",
layout="centered",
initial_sidebar_state="auto",
menu_items={
'Get help': None,
'Report a bug': None,
'About': about_page_markdown,
}
)
# Model setup
@st.cache(allow_output_mutation=True,
suppress_st_warning=True,
show_spinner=False)
def load_pipeline(model_name):
with st.spinner('Loading model (this might take a while)...'):
toxicity_pipeline = pipeline(
'text-classification',
model=model_name,
tokenizer=model_name)
cls_explainer = SequenceClassificationExplainer(
toxicity_pipeline.model,
toxicity_pipeline.tokenizer)
return toxicity_pipeline, cls_explainer
# Auxiliary functions
def format_explainer_html(html_string):
"""Extract tokens with attribution-based background color."""
inside_token_prefix = '##'
soup = BeautifulSoup(html_string, 'html.parser')
p = soup.new_tag('p',
attrs={'style': 'color: black; background-color: white;'})
# Select token elements and remove model specific tokens
current_word = None
for token in soup.find_all('td')[-1].find_all('mark')[1:-1]:
text = token.font.text.strip()
if text.startswith(inside_token_prefix):
text = text[len(inside_token_prefix):]
else:
# Create a new span for each word (sequence of sub-tokens)
if current_word is not None:
p.append(current_word)
p.append(' ')
current_word = soup.new_tag('span')
token.string = text
token.attrs['style'] = f"{token.attrs['style']}; padding: 0.2em 0em;"
current_word.append(token)
# Add last word
p.append(current_word)
# Add left and right-padding to each word
for span in p.find_all('span'):
span.find_all('mark')[0].attrs['style'] = (
f"{span.find_all('mark')[0].attrs['style']}; padding-left: 0.2em;")
span.find_all('mark')[-1].attrs['style'] = (
f"{span.find_all('mark')[-1].attrs['style']}; padding-right: 0.2em;")
return p
def classify_comment(comment, selected_model):
"""Classify the given comment and augment with additional information."""
toxicity_pipeline, cls_explainer = load_pipeline(selected_model)
result = toxicity_pipeline(comment)[0]
result['model_name'] = selected_model
# Add explanation
result['word_attribution'] = cls_explainer(comment, class_name="non-toxic")
result['visualitsation_html'] = cls_explainer.visualize()._repr_html_()
result['tokens_with_background'] = format_explainer_html(
result['visualitsation_html'])
# Choose emoji reaction
label, score = result['label'], result['score']
if label == 'toxic' and score > 0.1:
emoji = random.choice(potty_mouth_emojis)
elif label in ['non_toxic', 'non-toxic'] and score > 0.1:
emoji = random.choice(regular_emojis)
else:
emoji = random.choice(undecided_emojis)
result.update({'text': comment, 'emoji': emoji})
# Add result to session
st.session_state.results.append(result)
# Start session
if 'results' not in st.session_state:
st.session_state.results = []
# Page
st.title('🀬 Dutch Toxic Comment Detection')
st.markdown("""This demo showcases two Dutch toxic comment detection models.""")
# Introduction
st.markdown(f"""Both models were trained using a sequence classification task on a translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) which contains toxic online comments.
The first model is a fine-tuned multilingual [DistilBERT](https://huggingface.co/distilbert-base-multilingual-cased) model whereas the second is a fine-tuned Dutch RoBERTa-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base).""")
st.markdown(f"""For a more comprehensive overview of the models check out their model card on πŸ€— Model Hub: [distilbert-base-dutch-toxic-comments]({model_names_to_URLs['ml6team/distilbert-base-dutch-cased-toxic-comments']}) and [RobBERT-dutch-base-toxic-comments]({model_names_to_URLs['ml6team/robbert-dutch-base-toxic-comments']}).
""")
st.markdown("""Enter a comment that you want to classify below. The model will determine the probability that it is toxic and highlights how much each token contributes to its decision:
<font color="black">
<span style="background-color: rgb(250, 219, 219); opacity: 1;">r</span><span style="background-color: rgb(244, 179, 179); opacity: 1;">e</span><span style="background-color: rgb(238, 135, 135); opacity: 1;">d</span>
</font>
tokens indicate toxicity whereas
<font color="black">
<span style="background-color: rgb(224, 251, 224); opacity: 1;">g</span><span style="background-color: rgb(197, 247, 197); opacity: 1;">re</span><span style="background-color: rgb(121, 236, 121); opacity: 1;">en</span>
</font> tokens indicate the opposite.
Try it yourself! πŸ‘‡""",
unsafe_allow_html=True)
# Demo
with st.form("dutch-toxic-comment-detection-input", clear_on_submit=False):
selected_model = st.selectbox('Select a model:', model_names_to_URLs.keys(),
)#index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False)
text = st.text_area(
label='Enter the comment you want to classify below (in Dutch):')
_, rightmost_col = st.columns([6,1])
submitted = rightmost_col.form_submit_button("Classify",
help="Classify comment")
# Listener
if submitted:
if text:
with st.spinner('Analysing comment...'):
classify_comment(text, selected_model)
else:
st.error('**Error**: No comment to classify. Please provide a comment.')
# Results
if 'results' in st.session_state and st.session_state.results:
first = True
for result in st.session_state.results[::-1]:
if not first:
st.markdown("---")
st.markdown(f"Text:\n> {result['text']}")
col_1, col_2, col_3 = st.columns([1,2,2])
col_1.metric(label='', value=f"{result['emoji']}")
col_2.metric(label='Label', value=f"{result['label']}")
col_3.metric(label='Score', value=f"{result['score']:.3f}")
st.markdown(f"Token Attribution:\n{result['tokens_with_background']}",
unsafe_allow_html=True)
st.caption(f"Model: {result['model_name']}")
first = False