Ken Gu kenqgu
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| from transformers import Trainer, TrainingArguments | |
| training_args = TrainingArguments( | |
| output_dir="./logs/model_name", | |
| logging_dir="./logs/runs", | |
| overwrite_output_dir=True, | |
| do_train=True, | |
| per_device_train_batch_size=32, | |
| num_train_epochs=1, | |
| evaluate_during_training=True, |
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| from multimodal_transformers.model import AutoModelWithTabular, TabularConfig | |
| from transformers import AutoConfig | |
| num_labels = len(np.unique(torch_dataset, labels)) | |
| config = AutoConfig.from_pretrained('bert-base-uncased') | |
| tabular_config = TabularConfig( | |
| num_labels=num_labels, | |
| cat_feat_dim=torch_dataset.cat_feats.shape[1], | |
| numerical_feat_dim=torch_dataset.numerical_feats.shape[1], | |
| combine_feat_method='weighted_feature_sum_on_transformer_cat_and_numerical_feats', |
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| import pandas as pd | |
| from multimodal_transformers.data import load_data | |
| from transformers import AutoTokenizer | |
| data_df = pd.read_csv('Womens Clothing E-Commerce Reviews.csv') | |
| text_cols = ['Title', 'Review Text'] | |
| # The label col is expected to contain integers from 0 to N_classes - 1 | |
| label_col = 'Recommended IND' | |
| categorical_cols = ['Clothing ID', 'Division Name', 'Department Name', 'Class Name'] | |
| numerical_cols = ['Rating', 'Age', 'Positive Feedback Count'] |
kenqgu
/ dataloader.py
Created
July 19, 2020 19:10
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| self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item() |
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| import scipy.sparse as sp | |
| import numpy as np | |
| def init_node_feats(self, type, device): | |
| if type == 'one_hot_init': | |
| num_nodes = self.graph.shape[0] | |
| identity = sp.identity(num_nodes) | |
| ind0, ind1, values = sp.find(identity) | |
| inds = np.stack((ind0, ind1), axis=0) | |
| self.node_feats = torch.sparse_coo_tensor(inds, values, device=device, dtype=torch.float) |