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| import streamlit as st | |
| def app(): | |
| # st.title("About") | |
| st.markdown("<h1 style='text-align: center;'>About</h1>", unsafe_allow_html=True) | |
| st.markdown("""## Introduction""") | |
| st.markdown( | |
| """**RoBERTa-hindi** is one of the many projects in the Flax/JAX community week organized by HuggingFace in collaboration with Google to make compute-intensive projects more practicable.""" | |
| ) | |
| st.markdown( | |
| """It is a monolingual transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.""" | |
| ) | |
| st.markdown( | |
| ### How to use | |
| You can use this model directly with a pipeline for masked language modeling: | |
| ```python | |
| from transformers import pipeline | |
| unmasker = pipeline('fill-mask', model='flax-community/roberta-hindi') | |
| unmasker("मुझे उनसे बात करना <mask> अच्छा लगा") | |
| [{'score': 0.2096337080001831, | |
| 'sequence': 'मुझे उनसे बात करना एकदम अच्छा लगा', | |
| 'token': 1462, | |
| 'token_str': ' एकदम'}, | |
| {'score': 0.17915162444114685, | |
| 'sequence': 'मुझे उनसे बात करना तब अच्छा लगा', | |
| 'token': 594, | |
| 'token_str': ' तब'}, | |
| {'score': 0.15887945890426636, | |
| 'sequence': 'मुझे उनसे बात करना और अच्छा लगा', | |
| 'token': 324, | |
| 'token_str': ' और'}, | |
| {'score': 0.12024253606796265, | |
| 'sequence': 'मुझे उनसे बात करना लगभग अच्छा लगा', | |
| 'token': 743, | |
| 'token_str': ' लगभग'}, | |
| {'score': 0.07114479690790176, | |
| 'sequence': 'मुझे उनसे बात करना कब अच्छा लगा', | |
| 'token': 672, | |
| 'token_str': ' कब'}] | |
| ``` | |
| ) | |
| st.markdown("""## Datasets used""") | |
| st.markdown( | |
| """RoBERTa-Hindi has been pretrained on a huge corpus consisting of multiple datasets. The entire list of datasets used is mentioned below : """ | |
| ) | |
| st.markdown( | |
| """ | |
| 1. OSCAR | |
| 2. mC4 | |
| 3. Indic-glue | |
| 4. Hindi-wikipedia-articles-172k | |
| 5. Hindi-text-short-summarization corpus | |
| 6. Hindi-text-short-and-large-summarization corpus | |
| 7. Oldnewspaperhindi | |
| 8. Samanantar | |
| """ | |
| ) | |
| st.markdown( | |
| """ | |
| ***NOTE: Some of the datasets are readily available on the HuggingFace Datasets while the team developed the rest as per the docs.*** | |
| """ | |
| ) | |