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| import streamlit as st | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| st.title("SpellCorrectorT5") | |
| st.markdown('SpellCorrectorT5 is a fine-tuned version of **pre-trained t5-small model** modelled on randomly selected 50000 sentences modified by [imputing random noises/errors](./random_noiser.py) and trained using transformers. It not only looks for _spelling errors but also looks for the semantics_ in the sentence and suggest other possible words for the incorrect word.') | |
| m_name = "vishnun/tinygram" | |
| ttokenizer = AutoTokenizer.from_pretrained(m_name) | |
| tmodel = AutoModelForSeq2SeqLM.from_pretrained(m_name) | |
| form = st.form("T5-form") | |
| examples = ["I will return it to yu once it is donr", | |
| "Iu is going to rain", | |
| "Wheir do you live?", | |
| "It wis great mieting with you all"] | |
| input_text = form.selectbox(label="Choose an example", | |
| options=examples) | |
| form.write("(or)") | |
| input_text = form.text_input(label='Enter your own sentence', value=input_text) | |
| submit = form.form_submit_button("Submit") | |
| if submit: | |
| input_ids = ttokenizer.encode(input_text, return_tensors='pt') | |
| # generate text until the output length (which includes the context length) reaches 50 | |
| outputs = tmodel.generate( | |
| input_ids, | |
| do_sample=True, | |
| max_length=50, | |
| top_p=0.999, | |
| top_k=45, | |
| num_return_sequences=2 | |
| ) | |
| st.subheader("Most probable: ") | |
| for y in outputs: | |
| out_text = ttokenizer.decode(y, skip_special_tokens=True) | |
| st.success(out_text.capitalize()) | |
| c_text = "" | |
| for x in out_text.lower().split(" "): | |
| if x in input_text.lower().split(" "): | |
| c_text = c_text + x + " " | |
| else: | |
| c_text = c_text + '<span style="font-weight:bold; color:rgb(150,255,100);">' + x + '</span>' + " " | |
| ct = c_text.capitalize() | |
| st.markdown(str(ct), unsafe_allow_html=True) | |
| st.markdown("***", unsafe_allow_html=True) |