Delete commonsense_judgments.py

commonsense_judgments.py

@@ -1,30 +0,0 @@
-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())