| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer | |
| from datasets import load_dataset | |
| tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') | |
| dataset = load_dataset("Balaji576/car_diagnostics", split="train") | |
| encoded_dataset = dataset.map(lambda examples: tokenizer(examples['text']), batched=True) | |
| encoded_dataset.column_names | |
| pipe = pipeline("sentiment-analysis") | |
| text = st.text_area('enter some text!') | |
| if text: | |
| out = pipe(text) | |
| st.json(out) | |