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import pandas as pd |
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import streamlit as st |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import torch |
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import torch.nn.functional as F |
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from transformers import AlbertTokenizer |
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import time |
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if __name__=='__main__': |
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max_width = 1500 |
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padding_top = 0 |
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padding_right = 2 |
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padding_bottom = 0 |
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padding_left = 2 |
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define_margins = f""" |
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<style> |
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.appview-container .main .block-container{{ |
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max-width: {max_width}px; |
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padding-top: {padding_top}rem; |
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padding-right: {padding_right}rem; |
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padding-left: {padding_left}rem; |
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padding-bottom: {padding_bottom}rem; |
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}} |
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</style> |
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""" |
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hide_table_row_index = """ |
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<style> |
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tbody th {display:none} |
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.blank {display:none} |
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</style> |
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""" |
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st.markdown(define_margins, unsafe_allow_html=True) |
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st.markdown(hide_table_row_index, unsafe_allow_html=True) |
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from custom_modeling_albert_flax import CustomFlaxAlbertForMaskedLM |
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model = CustomFlaxAlbertForMaskedLM.from_pretrained('albert-base-v2') |
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