File size: 3,234 Bytes
e3d46c8 8158997 e3d46c8 0788ae6 56b99e8 0788ae6 e3d46c8 56b99e8 0788ae6 a85495d b28d4fd 093cd61 68d6aa3 8158997 68d6aa3 8158997 68d6aa3 b28d4fd 68d6aa3 b28d4fd bceabb4 68d6aa3 bceabb4 68d6aa3 bceabb4 0788ae6 ed6dd13 f7ac541 23bc4d7 bceabb4 68d6aa3 0788ae6 8158997 5e15a3b 8158997 0788ae6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
import streamlit as st
from transformers import pipeline
import torch
import matplotlib.pyplot as plt
#pipe = pipeline(model="RuudVelo/dutch_news_classifier_bert_finetuned")
#text = st.text_area('Please type/copy/paste the Dutch article')
#labels = ['Binnenland' 'Buitenland' 'Cultuur & Media' 'Economie' 'Koningshuis'
# 'Opmerkelijk' 'Politiek' 'Regionaal nieuws' 'Tech']
#if text:
# out = pipe(text)
# st.json(out)
# load tokenizer and model, create trainer
#model_name = "RuudVelo/dutch_news_classifier_bert_finetuned"
#tokenizer = AutoTokenizer.from_pretrained(model_name)
#model = AutoModelForSequenceClassification.from_pretrained(model_name)
#trainer = Trainer(model=model)
#print(filename, type(filename))
#print(filename.name)
from transformers import BertForSequenceClassification, BertTokenizer
model = BertForSequenceClassification.from_pretrained("RuudVelo/dutch_news_clf_bert_finetuned")
#from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("RuudVelo/dutch_news_clf_bert_finetuned")
# Title
st.title("Dutch news article classification")
text = st.text_area('Please type/copy/paste text of the Dutch article')
#if text:
# encoding = tokenizer(text, return_tensors="pt")
# outputs = model(**encoding)
# predictions = outputs.logits.argmax(-1)
# probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
## fig = plt.figure()
# ax = fig.add_axes([0,0,1,1])
# labels_plot = ['Binnenland', 'Buitenland' ,'Cultuur & Media' ,'Economie' ,'Koningshuis',
# 'Opmerkelijk' ,'Politiek', 'Regionaal nieuws', 'Tech']
# probs_plot = probabilities[0].cpu().detach().numpy()
# ax.barh(labels_plot,probs_plot )
# st.pyplot(fig)
#input = st.text_input('Context')
if st.button('Submit'):
with st.spinner('Generating a response...'):
encoding = tokenizer(text, return_tensors="pt")
outputs = model(**encoding)
predictions = outputs.logits.argmax(-1)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
labels_plot = ['Binnenland', 'Buitenland' ,'Cultuur & Media' ,'Economie' ,'Koningshuis',
'Opmerkelijk' ,'Politiek', 'Regionaal nieuws', 'Tech']
probs_plot = probabilities[0].cpu().detach().numpy()
ax.barh(labels_plot,probs_plot)
ax.set_title("Predicted article category probability")
ax.set_xlabel("Probability")
ax.set_ylabel("Predicted category")
st.pyplot(fig)
# output = genQuestion(option, input)
# print(output)
# st.write(output)
#encoding = tokenizer(text, return_tensors="pt")
#import numpy as np
st.write("The model for this app has been trained using data from Dutch news articles published by NOS. For more information see: https://www.kaggle.com/maxscheijen/dutch-news-articles")
st.write('\n')
st.write('The model performance details can be found at https://huggingface.co/RuudVelo/dutch_news_classifier_bert_finetuned')
#arr = np.random.normal(1, 1, size=100)
#fig, ax = plt.subplots()
#ax.hist(arr, bins=20)
#st.pyplot(fig)
# forward pass
#outputs = model(**encoding)
#predictions = outputs.logits.argmax(-1)
|