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import torch
import json
import numpy as np
from transformers import (BertForMaskedLM, BertTokenizer)
modelpath = 'bert-large-uncased-whole-word-masking/'
tokenizer = BertTokenizer.from_pretrained(modelpath)
model = BertForMaskedLM.from_pretrained(modelpath)
model.eval()
id_of_mask = 103
def get_embeddings(sentence):
with torch.no_grad():
processed_sentence = '' + sentence + ''
tokenized = tokenizer.encode(processed_sentence)
input_ids = torch.tensor(tokenized).unsqueeze(0) # Batch size 1
outputs = model(input_ids)
index_of_mask = tokenized.index(id_of_mask)
# batch, tokens, vocab_size
prediction_scores = outputs[0]
return prediction_scores[0][index_of_mask].cpu().numpy().tolist()
def get_embedding_group(tokens):
print(tokens)
mutated = []
for i, v in enumerate(tokens):
array = tokens.copy()
array[i] = id_of_mask
mutated.append(array)
print('Running model')
output = model(torch.tensor(mutated))[0]
print('Converting to list')
array = output.detach().numpy().tolist()
print('Constructing out array')
# only grab mask embedding
# can probaby do this in torch? not sure how
out = []
for i, v in enumerate(array):
out.append(v[i])
return out
def get_embedding_group_top(tokens):
sents = get_embedding_group(tokens)
out = []
print('get_embedding_group done')
for sent_i, sent in enumerate(sents):
all_tokens = []
for i, v in enumerate(sent):
all_tokens.append({'i': i, 'v': float(v)})
all_tokens.sort(key=lambda d: d['v'], reverse=True)
topTokens = all_tokens[:90]
sum = np.sum(np.exp(sent))
for i, token in enumerate(topTokens):
token['p'] = float(np.exp(token['v'])/sum)
out.append(all_tokens[:90])
return out
# Runs one token at a time to stay under memory limit
def get_embedding_group_low_mem(tokens):
print(tokens)
out = []
for index_of_mask, v in enumerate(tokens):
array = tokens.copy()
array[index_of_mask] = id_of_mask
input_ids = torch.tensor(array).unsqueeze(0)
prediction_scores = model(input_ids)[0]
out.append(prediction_scores[0][index_of_mask].detach().numpy())
return out
def get_embedding_group_top_low_mem(tokens):
sents = get_embedding_group_low_mem(tokens)
out = []
print('get_embedding_group done')
for sent_i, sent in enumerate(sents):
all_tokens = []
for i, v in enumerate(sent):
all_tokens.append({'i': i, 'v': float(v)})
all_tokens.sort(key=lambda d: d['v'], reverse=True)
topTokens = all_tokens[:90]
sum = np.sum(np.exp(sent))
for i, token in enumerate(topTokens):
token['p'] = float(np.exp(token['v'])/sum)
out.append(all_tokens[:90])
return out
import os
import shutil
# Free up memory
if os.environ.get('REMOVE_WEIGHTS') == 'TRUE':
print('removing bert-large-uncased-whole-word-masking from filesystem')
shutil.rmtree('bert-large-uncased-whole-word-masking', ignore_errors=True)
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