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import numpy as np |
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import pandas as pd |
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import time |
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import streamlit as st |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import jax |
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import jax.numpy as jnp |
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import torch |
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import torch.nn.functional as F |
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from transformers import AlbertTokenizer, AlbertForMaskedLM |
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from skeleton_modeling_albert import SkeletonAlbertForMaskedLM |
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def wide_setup(): |
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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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@st.cache(show_spinner=True,allow_output_mutation=True) |
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def load_model(): |
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tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2') |
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model = AlbertForMaskedLM.from_pretrained('albert-xxlarge-v2') |
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return tokenizer,model |
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def clear_data(): |
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for key in st.session_state: |
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del st.session_state[key] |
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if __name__=='__main__': |
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wide_setup() |
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if 'page_status' not in st.session_state: |
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st.session_state['page_status'] = 'type_in' |
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if st.session_state['page_status']=='type_in': |
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tokenizer,model = load_model() |
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mask_id = tokenizer('[MASK]').input_ids[1:-1][0] |
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st.write('1. Type in the sentences and click "Tokenize"') |
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sent_1 = st.sidebar.text_input('Sentence 1',value='It is better to play a prank on Samuel than Craig because he gets angry less often.') |
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sent_2 = st.sidebar.text_input('Sentence 2',value='It is better to play a prank on Samuel than Craig because he gets angry more often.') |
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if st.sidebar.button('Tokenize'): |
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st.session_state['page_status'] = 'tokenized' |
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st.session_state['sent_1'] = sent_1 |
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st.session_state['sent_2'] = sent_2 |
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if st.session_state['page_status']=='tokenized': |
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tokenizer,model = load_model() |
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mask_id = tokenizer('[MASK]').input_ids[1:-1][0] |
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sent_1 = st.session_state['sent_1'] |
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sent_2 = st.session_state['sent_2'] |
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if 'masked_pos_1' not in st.session_state: |
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st.session_state['masked_pos_1'] = [] |
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if 'masked_pos_2' not in st.session_state: |
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st.session_state['masked_pos_2'] = [] |
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st.write('2. Select sites to mask out and click "Confirm"') |
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input_sent = tokenizer(sent_1).input_ids |
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decoded_sent = [tokenizer.decode([token]) for token in input_sent] |
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char_nums = [len(word)+2 for word in decoded_sent] |
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cols = st.columns(char_nums) |
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with cols[0]: |
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st.write(decoded_sent[0]) |
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with cols[-1]: |
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st.write(decoded_sent[-1]) |
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for word_id,(col,word) in enumerate(zip(cols[1:-1],decoded_sent[1:-1])): |
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with col: |
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if st.button(word,key=f'word_{word_id}'): |
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st.session_state['masked_pos_1'].append(word_id) |
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st.write(f'Masked words: {" ".join([decoded_sent[word_id+1] for word_id in st.session_state["masked_pos_1"])}') |
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''' |
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sent_1 = st.sidebar.text_input('Sentence 1',value='It is better to play a prank on Samuel than Craig because he gets angry less often.',on_change=clear_data) |
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sent_2 = st.sidebar.text_input('Sentence 2',value='It is better to play a prank on Samuel than Craig because he gets angry more often.',on_change=clear_data) |
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input_ids_1 = tokenizer(sent_1).input_ids |
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input_ids_2 = tokenizer(sent_2).input_ids |
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input_ids = torch.tensor([input_ids_1,input_ids_2]) |
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outputs = SkeletonAlbertForMaskedLM(model,input_ids,interventions = {0:{'lay':[(8,1,[0,1])]}}) |
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logprobs = F.log_softmax(outputs['logits'], dim = -1) |
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preds = [torch.multinomial(torch.exp(probs), num_samples=1).squeeze(dim=-1) for probs in logprobs[0]] |
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st.write([tokenizer.decode([token]) for token in preds]) |
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''' |
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