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import streamlit as st
# from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
title = "Model Exploration"
description = "Comparison of hate speech detection models"
date = "2022-01-26"
thumbnail = "images/huggingface_logo.png"
# Creates the forms for receiving multiple inputs to compare for a single
# model or one input to compare for two models
def run_article():
st.markdown("""
# Making a Hate Speech Detection Model
This is where design choices will go.
# Model Output Ranking
For now, here's a link to the [space](https://huggingface.co/spaces/aymm/ModelOutputRankingTool).""")
with st.expander("Model Output Ranking Tool", expanded=False):
with st.form(key='ranking'):
model_name = st.selectbox("Select a model to test",
["classla/roberta-base-frenk-hate",
"cardiffnlp/twitter-roberta-base-hate",
"Hate-speech-CNERG/dehatebert-mono-english"],
)
input_1 = st.text_input("Input 1",
placeholder="We shouldn't let [IDENTITY] suffer.")
input_2 = st.text_input("Input 2",
placeholder="I'd rather die than date [IDENTITY].")
input_3 = st.text_input("Input 3",
placeholder="Good morning.")
inputs = [input_1, input_2, input_3]
if st.form_submit_button(label="Rank inputs"):
results = run_ranked(model_name, inputs)
st.dataframe(results)
st.markdown("""
# Model Comparison
For now, here's a link to the [space](https://huggingface.co/spaces/aymm/ModelComparisonTool).
""")
with st.expander("Model Comparison Tool", expanded=False):
with st.form(key='comparison'):
model_name_1 = st.selectbox("Select a model to compare",
["cardiffnlp/twitter-roberta-base-hate",
"Hate-speech-CNERG/dehatebert-mono-english",
],
key='compare_model_1'
)
model_name_2 = st.selectbox("Select another model to compare",
["cardiffnlp/twitter-roberta-base-hate",
"Hate-speech-CNERG/dehatebert-mono-english",
],
key='compare_model_2'
)
input_text = st.text_input("Comparison input")
if st.form_submit_button(label="Compare models"):
results = run_compare(model_name_1, model_name_2, input_text)
st.dataframe(results)
# Runs the received input strings through the given model and returns the
# output ranked by label and score (does not assume binary labels so the
# highest score for each label is at the top)
def run_ranked(model, input_list):
classifier = pipeline("text-classification", model=model)
output = []
labels = {}
for inputx in input_list:
result = classifier(inputx)
curr = {}
curr['Input'] = inputx
label = result[0]['label']
curr['Label'] = label
score = result[0]['score']
curr['Score'] = score
if label in labels:
labels[label].append(curr)
else:
labels[label] = [curr]
for label in labels:
sort_list = sorted(labels[label], key=lambda item:item['Score'], reverse=True)
output += sort_list
return output
# Takes in two model names and returns the output of both models for that
# given input string
def run_compare(name_1, name_2, text):
classifier_1 = pipeline("text-classification", model=name_1)
result_1 = classifier_1(text)
out_1 = {}
out_1['Model'] = name_1
out_1['Label'] = result_1[0]['label']
out_1['Score'] = result_1[0]['score']
classifier_2 = pipeline("text-classification", model=name_2)
result_2 = classifier_2(text)
out_2 = {}
out_2['Model'] = name_2
out_2['Label'] = result_2[0]['label']
out_2['Score'] = result_2[0]['score']
return [out_1, out_2]
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