| 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 |
