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import tokenize_uk
import torch
def get_word_predictions(model, tokenizer, texts, is_split_to_words=False, device='cpu'):
words_res = []
y_res = []
if not is_split_to_words:
texts = [tokenize_uk.tokenize_words(text) for text in texts]
for text in texts:
size = len(text)
idx_list = [idx + 1 for idx, val in enumerate(text) if val in ['.', '?', '!']]
if len(idx_list):
sents = [text[i: j] for i, j in zip([0] + idx_list, idx_list + ([size] if idx_list[-1] != size else []))]
else:
sents = [text]
y_res_x = []
words_res_x = []
for sent_tokens in sents:
tokenized_inputs = [101]
word_ids = [None]
for word_id, word in enumerate(sent_tokens):
word_tokens = tokenizer.encode(word)[1:-1]
tokenized_inputs += word_tokens
word_ids += [word_id]*len(word_tokens)
tokenized_inputs = tokenized_inputs[:(tokenizer.model_max_length-1)]
word_ids = word_ids[:(tokenizer.model_max_length-1)]
tokenized_inputs += [102]
word_ids += [None]
torch_tokenized_inputs = torch.tensor(tokenized_inputs).unsqueeze(0)
torch_attention_mask = torch.ones(torch_tokenized_inputs.shape)
predictions = model.forward(input_ids=torch_tokenized_inputs.to(device), attention_mask=torch_attention_mask.to(device))
predictions = torch.argmax(predictions.logits.squeeze(), axis=1).numpy()
predictions = [model.config.id2label[i] for i in predictions]
previous_word_idx = None
sent_words = []
predictions_words = []
word_tokens = []
first_pred = None
for i, word_idx in enumerate(word_ids):
if word_idx != previous_word_idx:
sent_words.append(tokenizer.decode(word_tokens))
word_tokens = [tokenized_inputs[i]]
predictions_words.append(first_pred)
first_pred = predictions[i]
else:
word_tokens.append(tokenized_inputs[i])
previous_word_idx = word_idx
words_res_x.extend(sent_words[1:])
y_res_x.extend(predictions_words[1:])
words_res.append(words_res_x)
y_res.append(y_res_x)
return words_res, y_res |