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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
Once the data has been collected using the definitions identified for the
task, you can start training your model. At training, the model takes in
the data with labels and learns the associated context in the input data
for each label. Depending on the task design, the labels may be binary like
'hateful' and 'non-hateful' or multiclass like 'neutral', 'offensive', and
'attack'.
When presented with a new input string, the model then predicts the
likelihood that the input is classified as each of the available labels and
returns the label with the highest likelihood as well as how confident the
model is in its selection using a score from 0 to 1.
Neural models such as transformers are frequently trained as general
language models and then fine-tuned on specific classification tasks.
These models can vary in their architecture and the optimization
algorithms, sometimes resulting in very different output for the same
input text.
# 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"],
key="rank_model_select"
)
# the placeholder key functionality was added in v1.2 of streamlit
# and versions on Spaces currently goesup to v1.0
input_1 = st.text_input("Input 1",
#placeholder="We shouldn't let [IDENTITY] suffer.",
help= "Try a phrase like 'We shouldn't let [IDENTITY] suffer.'",
key="rank_input_1")
input_2 = st.text_input("Input 2",
#placeholder="I'd rather die than date [IDENTITY].",
help= "Try a phrase like 'I'd rather die than date [IDENTITY].'",
key="rank_input_2")
input_3 = st.text_input("Input 3",
#placeholder="Good morning.",
help= "Try a phrase like 'Good morning'",
key="rank_input_3")
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",
key="compare_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]
def main():
run_article()
main()
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