Elron Bandel
commited on
Commit
•
2df020e
1
Parent(s):
3411193
update code
Browse files- app.py +6 -51
- requirements.txt +1 -1
app.py
CHANGED
@@ -12,31 +12,6 @@ st.set_page_config(
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initial_sidebar_state="expanded",
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# st.markdown(
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# """
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# <style>
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# .sidebar .sidebar-content {
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# background-image: linear-gradient(#3377ff, #80aaff);
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# }
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# footer {
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# color:white;
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# visibility: hidden;
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# }
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# input {
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# direction: rtl;
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# }
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# .stTextInput .instructions {
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# color: grey;
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# font-size: 9px;}
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# </style>
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# <div style="color:white; font-size:13px; font-family:monospace;position: fixed; z-index: 1; bottom: 0; right:0; background-color: #f63766;margin:3px;padding:8px;border-radius: 5px;"><a href="https://huggingface.co/onlplab/alephbert-base" target="_blank" style="text-decoration: none;color: white;">Use aleph-bert in your project </a></div>
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# """,
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# unsafe_allow_html=True,
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# )
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models = {
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"AlephBERT-base": {
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"name_or_path":"onlplab/alephbert-base",
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@@ -116,16 +91,8 @@ if mode == 'Models':
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st.markdown(''.join([f'<span style="color:white; font-size:13px; font-family:monospace; background-color: #f63766;margin:3px;padding:8px;border-radius: 5px;">{tag}</span>' for tag in model_tags]),unsafe_allow_html=True)
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st.markdown('___')
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#prepare the model
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####
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unmasker, tokenize = load_model(model)
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####
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# get inputs
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####
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input_text = st.text_input('Insert text you want to mask', '')
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if input_text:
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@@ -136,9 +103,9 @@ if mode == 'Models':
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if masking_level == 'Tokens':
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tokens = str(input_text).split()
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if
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input_masked = ' '.join(token if
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display_input = input_masked
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if masking_level == 'SubWords':
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tokens = subwords
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@@ -157,25 +124,13 @@ if mode == 'Models':
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unsafe_allow_html=True,
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)
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st.markdown('#### Outputs:')
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if res:
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res = [{'Prediction':r['token_str'], 'Completed Sentence':r['sequence'].replace('[SEP]', '').replace('[CLS]', ''), 'Score':r['score']} for r in res]
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res_table = pd.DataFrame(res)
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st.table(res_table)
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# cols = st.beta_columns(len(tokens))
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# genre = st.radio(
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# 'Select token to mask:', tokens)
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# for col, token in zip(cols, reversed(tokens)):
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# col.text(token)
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# st.text(tokens)
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# res = unmasker(input_text)
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# res_table = pd.DataFrame(res)
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# st.table(res_table)
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# st.text(res)
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initial_sidebar_state="expanded",
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)
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models = {
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"AlephBERT-base": {
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"name_or_path":"onlplab/alephbert-base",
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st.markdown(''.join([f'<span style="color:white; font-size:13px; font-family:monospace; background-color: #f63766;margin:3px;padding:8px;border-radius: 5px;">{tag}</span>' for tag in model_tags]),unsafe_allow_html=True)
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st.markdown('___')
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unmasker, tokenize = load_model(model)
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input_text = st.text_input('Insert text you want to mask', '')
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if input_text:
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if masking_level == 'Tokens':
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tokens = str(input_text).split()
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mask_idx = st.selectbox('Select token to mask:', [None] + list(range(len(tokens))), format_func=lambda i: tokens[i] if i else '')
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if mask_idx is not None:
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input_masked = ' '.join(token if i != mask_idx else '[MASK]' for i, token in enumerate(tokens))
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display_input = input_masked
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if masking_level == 'SubWords':
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tokens = subwords
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unsafe_allow_html=True,
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)
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st.markdown('#### Outputs:')
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with st.spinner('Running model...'):
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res = unmasker(input_masked, tokenized=masking_level == 'SubWords', top_k=n_res)
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if res:
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res = [{'Prediction':r['token_str'], 'Completed Sentence':r['sequence'].replace('[SEP]', '').replace('[CLS]', ''), 'Score':r['score']} for r in res]
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res_table = pd.DataFrame(res)
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st.table(res_table)
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requirements.txt
CHANGED
@@ -1,5 +1,5 @@
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1 |
torch
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sentencepiece
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transformers==4.
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tokenizers
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pandas
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torch
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sentencepiece
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transformers==4.6.1
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tokenizers
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pandas
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