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Runtime error
| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing") | |
| st.title('Question Generator by Eddevs') | |
| left_column, right_column = st.columns(2) | |
| left_column.selectbox('Type', ['Question Generator', 'Paraphrasing']) | |
| right_column.selectbox('Question Generator', ['T5', 'GPT Neo-X']) | |
| input = st.text_area("Input Text") | |
| if st.button('Generate'): | |
| st.write(input) | |
| st.success("We have generated 105 Questions for you") | |
| st.snow() | |
| ##else: | |
| ##nothing here | |
| def paraphrase(text, max_length=128): | |
| input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True) | |
| generated_ids = model.generate(input_ids=input_ids, num_return_sequences=5, num_beams=5, max_length=max_length, no_repeat_ngram_size=2, repetition_penalty=3.5, length_penalty=1.0, early_stopping=True) | |
| preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids] | |
| return preds | |
| preds = paraphrase("paraphrase: What is the best framework for dealing with a huge text dataset?") | |
| for pred in preds: | |
| st.write(pred) | |