96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
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import os
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from util import Model
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import spacy
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import torch
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import numpy as np
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import tqdm
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def clean_string(str):
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str = str.replace('\n', '')
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return str
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def extract_word(line):
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return line.split(" ")[1]
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def line2word(line):
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word = extract_word(line)
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word = clean_string(word)
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return word
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def find_interpunction(line, classes):
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result = [x for x in classes if x in line]
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if len(result) > 0:
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return result[0]
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else:
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return ['']
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def words_to_vecs(list_of_words):
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return [nlp(x).vector for x in list_of_words]
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def softXEnt(input, target):
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m = torch.nn.LogSoftmax(dim = 1)
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logprobs = m(input)
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return -(target * logprobs).sum() / input.shape[0]
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def compute_class_vector(mark, classes):
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result = np.zeros(len(classes))
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for x in range(len(classes)):
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if classes[x] == mark[0]:
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result[x] == 1
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return torch.tensor(result, dtype=torch.long)
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data_dir = "./fa/poleval_final_dataset/train"
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data_nopunc_dir = "./fa/poleval_final_dataset1/train"
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data_paths = os.listdir(data_dir)
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data_paths = [data_dir + "/" + x for x in data_paths]
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classes = [',', '.', '?', '!', '-', ':', '...']
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nlp = spacy.load("pl_core_news_sm")
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context_size = 5
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model = Model()
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epochs = 5
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output_prefix = "model"
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hidden_state = torch.randn((2, 1, 300), requires_grad=True)
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cell_state = torch.randn((2, 1, 300), requires_grad=True)
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model.train()
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optimizer = torch.optim.AdamW(model.parameters(), lr=0.02)
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loss_function = softXEnt
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for epoch in range(epochs):
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for path in tqdm.tqdm(data_paths):
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with open(path, "r") as file:
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list = file.readlines()[:-1]
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for i in range(0, len(list) - context_size - 1):
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model.zero_grad()
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x = list[i: i + context_size]
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x = [line2word(y) for y in x]
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x_1 = [line2word(list[i + context_size + 1])]
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x = x + x_1
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x = words_to_vecs(x)
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mark = find_interpunction(x, classes)
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mark = words_to_vecs(mark)
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x = torch.tensor(x, dtype=torch.float)
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mark = torch.tensor(mark, dtype=torch.float)
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output, (hidden_state, cell_state) = model.forward(x, hidden_state, cell_state)
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output = output.squeeze(1)
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loss = loss_function(output, compute_class_vector(mark, classes))
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loss.backward()
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optimizer.step()
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hidden_state = hidden_state.detach()
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cell_state = cell_state.detach()
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"""
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vector -> (96,), np nadarray
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"""
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print("Epoch: {}".format(epoch))
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torch.save(
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model.state_dict(),
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os.path.join("./", f"{output_prefix}-{epoch}.pt"),
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)
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