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import streamlit as st |
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from transformers import pipeline |
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from transformers.tokenization_utils import TruncationStrategy |
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import tokenizers |
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import pandas as pd |
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import requests |
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st.set_page_config( |
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page_title='AlephBERT Demo', |
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page_icon="🥙", |
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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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"description":"AlephBERT base model", |
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}, |
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"HeBERT-base-TAU": { |
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"name_or_path":"avichr/heBERT", |
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"description":"HeBERT model created by TAU" |
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}, |
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"mBERT-base-multilingual-cased": { |
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"name_or_path":"bert-base-multilingual-cased", |
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"description":"Multilingual BERT model" |
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} |
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} |
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@st.cache(show_spinner=False) |
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def get_json_from_url(url): |
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return models |
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return requests.get(url).json() |
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@st.cache(show_spinner=False, hash_funcs={tokenizers.Tokenizer: str}) |
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def load_model(model): |
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pipe = pipeline('fill-mask', models[model]['name_or_path']) |
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def do_tokenize(inputs): |
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return pipe.tokenizer( |
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inputs, |
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add_special_tokens=True, |
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return_tensors=pipe.framework, |
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padding=True, |
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truncation=TruncationStrategy.DO_NOT_TRUNCATE, |
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) |
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def _parse_and_tokenize( |
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inputs, tokenized=False, **kwargs |
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): |
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if not tokenized: |
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inputs = do_tokenize(inputs) |
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return inputs |
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pipe._parse_and_tokenize = _parse_and_tokenize |
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return pipe, do_tokenize |
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st.title('AlephBERT🥙') |
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st.sidebar.markdown( |
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"""<div><a target="_blank" href="https://nlp.biu.ac.il/~rtsarfaty/onlp#"><img src="https://nlp.biu.ac.il/~rtsarfaty/static/landing_static/img/onlp_logo.png" style="filter: invert(100%);display: block;margin-left: auto;margin-right: auto; |
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width: 70%;"></a> |
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<p style="color:white; font-size:13px; font-family:monospace; text-align: center">AlephBERT Demo • <a href="https://nlp.biu.ac.il/~rtsarfaty/onlp#" style="text-decoration: none;color: white;" target="_blank">ONLP Lab</a></p></div> |
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<br>""", |
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unsafe_allow_html=True, |
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) |
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mode = 'Models' |
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if mode == 'Models': |
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model = st.sidebar.selectbox( |
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'Select Model', |
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list(models.keys())) |
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masking_level = st.sidebar.selectbox('Masking Level:', ['Tokens', 'SubWords']) |
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n_res = st.sidebar.number_input( |
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'Number Of Results', |
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format='%d', |
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value=5, |
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min_value=1, |
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max_value=100) |
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model_tags = model.split('-') |
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model_tags[0] = 'Model:' + model_tags[0] |
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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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input_masked = None |
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tokenized = tokenize(input_text) |
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ids = tokenized['input_ids'].tolist()[0] |
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subwords = unmasker.tokenizer.convert_ids_to_tokens(ids) |
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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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idx = st.selectbox('Select token to mask:', list(range(0,len(tokens)-1)), format_func=lambda i: tokens[i] if i else '') |
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tokenized['input_ids'][0][idx] = unmasker.tokenizer.mask_token_id |
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ids = tokenized['input_ids'].tolist()[0] |
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display_input = ' '.join(unmasker.tokenizer.convert_ids_to_tokens(ids[1:-1])) |
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if idx: |
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input_masked = tokenized |
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if input_masked: |
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st.markdown('#### Input:') |
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ids = tokenized['input_ids'].tolist()[0] |
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subwords = unmasker.tokenizer.convert_ids_to_tokens(ids) |
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st.markdown(f'<p dir="rtl">{display_input}</p>', |
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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(f'Running {model_tags[0]} (may take a minute)...'): |
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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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