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)
