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How to use test_no_label method in lettuce_webdriver

Best Python code snippet using lettuce_webdriver_python

evaluate.py

Source:evaluate.py

1import pickle23import numpy as np4import pandas as pd5from tqdm import tqdm67import torch8from torch.nn.utils.rnn import pad_sequence910# from dump_models import load_model11from transformers import (12    BertForSequenceClassification,13    BertTokenizer,14    XLNetForSequenceClassification,15    XLNetTokenizer16)171819###################################################################20'''21evaluate new data22< test_model >23input: model, tokenizer, mean_val_acc, mean_val_loss, file_name='test_no_label', device='cuda'24output: make submission25'''26###################################################################272829def collate_fn_style_test(samples):30    input_ids = samples31    max_len = max(len(input_id) for input_id in input_ids)3233    attention_mask = torch.tensor([[1] * len(input_id) + [0] * (max_len - len(input_id)) for input_id in input_ids])34    input_ids = pad_sequence([torch.tensor(input_id) for input_id in input_ids], batch_first=True)35    token_type_ids = torch.tensor([[0] * len(input_id) for input_id in input_ids])36    position_ids = torch.tensor([list(range(len(input_id))) for input_id in input_ids])3738    return input_ids, attention_mask, token_type_ids, position_ids3940def test_model(model, tokenizer, mean_val_acc, mean_val_loss, file_name='test_no_label', device='cuda'):41    test_df = pd.read_csv('./datasets/' + file_name + '.csv')42    test_df_ = test_df['Id']4344    test = [sent.lower() for sent in test_df_]454647    """48    ì ì²ë¦¬49    """5051    test_dataset = [np.array(tokenizer.encode(line)) for line in test]525354    test_batch_size = 6455    test_loader = torch.utils.data.DataLoader(test_dataset,56                                            batch_size=test_batch_size,57                                            shuffle=False, 58                                            collate_fn=collate_fn_style_test,59                                            num_workers=2)60    # for idx, i in enumerate(test_loader):61    #     print(i)62    #     if idx == 1:63    #         break6465    model.eval()66    with torch.no_grad():67        predictions = []68        for input_ids, attention_mask, token_type_ids, position_ids in tqdm(test_loader, desc='Test', position=1, leave=None):69            input_ids = input_ids.to(device)70            attention_mask = attention_mask.to(device)71            token_type_ids = token_type_ids.to(device)72            position_ids = position_ids.to(device)7374            output = model(input_ids=input_ids,75                        attention_mask=attention_mask,76                        token_type_ids=token_type_ids,77                        position_ids=position_ids)78            79            logits = output.logits80            batch_predictions = [0 if example[0] > example[1] else 1 for example in logits]81            predictions += batch_predictions82    print(predictions)8384    test_df['Category'] = predictions85    test_df.to_csv('./submissions/sub' + str(int(mean_val_acc*100)) + str(int(mean_val_loss*1000)) + '.csv', index=False)868788if __name__ == '__main__':89    MODEL_NAME = 'bert-base-uncased'90    # try:91    #     with open('./dump_models_toknizer/' + MODEL_NAME + '.p', 'rb') as f:92    #         print('model exist => just load model')93    #         model = pickle.load(f)94    #         tokenizer = pickle.load(f)95    # except:96    #     print('exeption occur => download model')97    #     model, tokenizer = load_model(MODEL_NAME)9899    100    tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)101    MODEL_NAME = './best_models/model9850'102    model = BertForSequenceClassification.from_pretrained(MODEL_NAME)103    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')104    model.to(device)105    106    # test_model(model, tokenizer, 0, 0, file_name='test_no_label', device='cuda')
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