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commonsense_judgments.py

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+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+import torch 
+import pandas as pd
+import numpy as np
+import streamlit as st
+from transformers import pipeline
+from transformers_interpret import SequenceClassificationExplainer
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+st.title('Ethics Classifier')
+st.write('This app uses a pre-trained Distilbert model fine-tuned on the Commonsense Ethics dataset from the Aligning AI With Shared Human Values project (https://github.com/hendrycks/ethics). It judges whether a given action of scenario is wrong or not wrong and shows how the words in the scenario affected the judgment.')
+
+loaded_model = DistilBertForSequenceClassification.from_pretrained('commonsense_ethics')
+model_name = 'distilbert-base-uncased'
+tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
+cls_explainer = SequenceClassificationExplainer(loaded_model, tokenizer)
+
+clf = pipeline("text-classification", model = loaded_model, tokenizer = tokenizer)
+
+text = st.text_input('Enter a scenario or action.')
+
+if text:
+    answer = clf(text)
+    label = 'wrong' if answer[0]['label'] == 'LABEL_1' else 'not wrong'
+    st.write(f'This action is {label} (confidence level {answer[0]["score"]*100:.2f}%).')
+    attributions = cls_explainer(text)
+    df = pd.DataFrame(attributions[1:-1])
+    df.rename(columns = {0: 'Token', 1: 'Contribution'}, inplace = True)
+    st.write(df.style.hide(axis = 'index'))
+    st.write(cls_explainer.visualize())