| 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") | |
| #text = ["this is one sentence", "this is another sentence"] | |
| text = st.text_area('Please type/copy/paste 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) | |
| st.json(predictions) | |
| #plt.show() | |
| #out = pipe(text) | |
| #st.json(predictions) | |
| #encoding = tokenizer(text, return_tensors="pt") | |
| #import numpy as np | |
| #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) | |