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| from pandas.io.formats.format import return_docstring | |
| import streamlit as st | |
| import pandas as pd | |
| from transformers import AutoTokenizer,AutoModelForMaskedLM | |
| from transformers import pipeline | |
| import os | |
| import json | |
| def load_model(masked_text,model_name): | |
| model = AutoModelForMaskedLM.from_pretrained(model_name, from_flax=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # tokenizer.save_pretrained('exported_pytorch_model') | |
| # model.save_pretrained('exported_pytorch_model') | |
| nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer) | |
| result_sentence = nlp(masked_text) | |
| return result_sentence | |
| def main(): | |
| st.title("RoBERTa Hindi") | |
| st.markdown( | |
| "This demo uses pretrained RoBERTa variants for Mask Language Modeling (MLM)" | |
| ) | |
| models = st.multiselect( | |
| "Choose models", | |
| ['flax-community/roberta-hindi','mrm8488/HindiBERTa','ai4bharat/indic-bert',\ | |
| 'neuralspace-reverie/indic-transformers-hi-bert', | |
| 'surajp/RoBERTa-hindi-guj-san'], | |
| ["flax-community/roberta-hindi"] | |
| ) | |
| target_text_path = './mlm_custom/mlm_targeted_text.csv' | |
| target_text_df = pd.read_csv(target_text_path) | |
| texts = target_text_df['text'] | |
| st.sidebar.title("Hindi MLM") | |
| masked_text = st.sidebar.selectbox('Select any of the following text', | |
| texts) | |
| st.write('You selected:', masked_text) | |
| selected_model = models[0] | |
| if st.button('Fill the Mask!'): | |
| with st.spinner("Filling the Mask..."): | |
| filled_sentence = load_model(masked_text,selected_model) | |
| st.write(filled_sentence) | |
| if __name__ == "__main__": | |
| main() |