def main(): dataloader = CorpusReader("./data/wili-2018/x_train_sub.txt", "./data/wili-2018/y_train_sub.txt") char_to_idx, idx_to_char, char_frequency = dataloader.get_mappings() model = SkipGram(12300, 256, char_frequency) with open("./models/skipgram/5.pt", 'rb') as f: state_dict = torch.load(f) model.load_state_dict(state_dict) print("Model Loaded") save_embeddings = True if save_embeddings: central_embeddings = model.central_embedding.weight torch.save(central_embeddings, './models/character_embeddings.pt') print("{} Embedding Weights Saved".format(central_embeddings.shape)) model = model.to(device) model.eval() similarities = model.vocabulary_similarities() show_chars = [ 't', 'b', 'a', 'e', 'x', ',', '.', '@', '%', '4', '9', "բ", "Հ", "ñ", "名", "Θ" ] show_results(show_chars, similarities, char_to_idx, idx_to_char)