52 KiB
52 KiB
def read_data(path):
with open(path, 'r') as f:
dataset = [line.strip().split('\t') for line in f]
return datasetdataset = read_data('train/train.tsv')train_x = [x[1] for x in dataset]
train_y = [y[0] for y in dataset]import torchtext.vocab
from collections import Counterdef build_vocab(dataset):
counter = Counter()
for document in dataset:
counter.update(document)
vocab = torchtext.vocab.vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])
vocab.set_default_index(0)
return vocabtrain_x = [x.split() for x in train_x]vocab = build_vocab(train_x)def data_process(dt):
return [ torch.tensor([vocab['<bos>']] +[vocab[token] for token in document ] + [vocab['<eos>']], dtype = torch.long) for document in dt]
def labels_process(dt):
labels = []
for document in dt:
temp = []
temp.append(0)
temp.append(document)
# print(document)
temp.append(0)
labels.append(torch.tensor(temp, dtype = torch.long))
return labels
#return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]
ner_tags = {'O': 0, 'B-ORG': 1, 'I-ORG': 2, 'B-PER': 3, 'I-PER': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}import torch
train_tokens_ids = data_process(train_x)dev_x = read_data('dev-0/in.tsv')
dev_y = read_data('dev-0/expected.tsv')
test_x = read_data('test-A/in.tsv')
dev_x = [x[0].split() for x in dev_x]
dev_y = [y[0].split() for y in dev_y]
test_x = [x[0].split() for x in test_x]train_y = [y[0] for y in dataset]
print(train_y[0])
train_y = [[ner_tags.get(tag) for y in train_y for tag in y.split()]]
train_y[0]B-ORG O B-MISC O O O B-MISC O O O B-PER I-PER O B-LOC O O O B-ORG I-ORG O O O O O O B-MISC O O O O O B-MISC O O O O O O O O O O O O O O O B-LOC O O O O B-ORG I-ORG O O O B-PER I-PER O O O O O O O O O O O B-LOC O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG O O O B-PER I-PER I-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-ORG I-ORG O O O O O O O O O B-ORG O O B-PER I-PER O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-PER O B-MISC O O O O B-LOC O B-LOC O O O O O O O B-MISC I-MISC I-MISC O B-MISC O O O O O O O O B-PER O O O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O B-PER I-PER I-PER O O O B-PER O O B-ORG O O O O O O O O O O O O O O O O O O B-LOC O B-LOC O B-PER O O O O O B-ORG O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O O B-MISC O O O O O O B-MISC O O O O O B-LOC O O O O O O O O O O O O O O O O O O O B-LOC O O O O B-ORG I-ORG I-ORG I-ORG I-ORG O B-ORG O O B-PER I-PER I-PER O O B-ORG I-ORG O O B-LOC O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O O O O O O O O O B-LOC O O O O B-LOC O O O O O O O O O O O O O O O O B-MISC O O O O O O O O O O
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dev_y = [ner_tags.get(tag) for y in dev_y for tag in y]
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test_tokens_ids = data_process(dev_x)
train_labels = labels_process(train_y[0])
test_labels = labels_process(dev_y)class NERModel(torch.nn.Module):
def __init__(self,):
super(NERModel, self).__init__()
self.emb = torch.nn.Embedding(23627, 200)
self.fc1 = torch.nn.Linear(2400, 9)
#self.softmax = torch.nn.Softmax(dim=1)
# nie trzeba, bo używamy https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
# jako kryterium
def forward(self, x):
x = self.emb(x)
x = x.reshape(2400)
x = self.fc1(x)
#x = self.softmax(x)
return xner_model = NERModel()criterion = torch.nn.CrossEntropyLoss()optimizer = torch.optim.Adam(ner_model.parameters())import string
def add_features(tens, tokens):
array = [0, 0, 0, 0, 0, 0, 0, 0, 0]
if len(tokens) >= 2:
if len(tokens[1]) >= 1:
word = tokens[1]
if word[0].isupper():
array[0] = 1
if word.isalnum():
array[1] = 1
for i in word:
# checking whether the char is punctuation.
if i in string.punctuation:
# Printing the punctuation values
array[2] = 1
if word.isnumeric():
array[3] = 1
if word.isupper():
array[4] = 1
if '-' in word:
array[5] = 1
if '/' in word:
array[6] = 1
if len(word) > 3:
array[7] = 1
if len(word) > 6:
array[8] = 1
x = torch.tensor(array)
new_tensor = torch.cat((tens, x), 0)
return new_tensorfor epoch in range(50):
loss_score = 0
acc_score = 0
prec_score = 0
selected_items = 0
recall_score = 0
relevant_items = 0
items_total = 0
ner_model.train()
#for i in range(len(train_labels)):
for i in range(100):
for j in range(1, len(train_labels[i]) - 1):
X_base = train_tokens_ids[i][j-1: j+2]
X_add = train_x[i][j-1: j+2]
X_final = add_features(X_base, X_add)
Y = train_labels[i][j: j+1]
Y_predictions = ner_model(X_final)
acc_score += int(torch.argmax(Y_predictions) == Y)
if torch.argmax(Y_predictions) != 0:
selected_items +=1
if torch.argmax(Y_predictions) != 0 and torch.argmax(Y_predictions) == Y.item():
prec_score += 1
if Y.item() != 0:
relevant_items +=1
if Y.item() != 0 and torch.argmax(Y_predictions) == Y.item():
recall_score += 1
items_total += 1
optimizer.zero_grad()
loss = criterion(Y_predictions.unsqueeze(0), Y)
loss.backward()
optimizer.step()
loss_score += loss.item()
precision = prec_score / selected_items
recall = recall_score / relevant_items
#f1_score = (2*precision * recall) / (precision + recall)
print('epoch: ', epoch, end='\t')
print('loss: ', loss_score / items_total, end='\t')
print('acc: ', acc_score / items_total, end='\t')
print('prec: ', precision, end='\t')
print('recall: : ', recall)
#display('f1: ', f1_score)epoch: 0 loss: 0.4997586368946457 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 1 loss: 0.518995374645849 acc: 0.9 prec: 0.6666666666666666 recall: : 0.7058823529411765 epoch: 2 loss: 0.6272036719378185 acc: 0.87 prec: 0.625 recall: : 0.5882352941176471 epoch: 3 loss: 0.5379423921279067 acc: 0.9 prec: 0.7058823529411765 recall: : 0.7058823529411765 epoch: 4 loss: 0.6458101376151467 acc: 0.88 prec: 0.6470588235294118 recall: : 0.6470588235294118 epoch: 5 loss: 0.5032455809084422 acc: 0.9 prec: 0.7692307692307693 recall: : 0.5882352941176471 epoch: 6 loss: 0.5464647812097837 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 7 loss: 0.5818069439918144 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 8 loss: 0.6688040642930787 acc: 0.86 prec: 0.5789473684210527 recall: : 0.6470588235294118 epoch: 9 loss: 0.47163703395596485 acc: 0.9 prec: 0.7058823529411765 recall: : 0.7058823529411765 epoch: 10 loss: 0.6080643151845471 acc: 0.87 prec: 0.625 recall: : 0.5882352941176471 epoch: 11 loss: 0.6119919324012835 acc: 0.86 prec: 0.5789473684210527 recall: : 0.6470588235294118 epoch: 12 loss: 0.5809223624372385 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 13 loss: 0.5410229888884214 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 14 loss: 0.5213326926458853 acc: 0.88 prec: 0.631578947368421 recall: : 0.7058823529411765 epoch: 15 loss: 0.5297116384661035 acc: 0.89 prec: 0.7142857142857143 recall: : 0.5882352941176471 epoch: 16 loss: 0.5681106116262435 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 17 loss: 0.49915451315861675 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 18 loss: 0.5361382347030667 acc: 0.88 prec: 0.6470588235294118 recall: : 0.6470588235294118 epoch: 19 loss: 0.4398948981850981 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 20 loss: 0.587098065932239 acc: 0.86 prec: 0.5789473684210527 recall: : 0.6470588235294118 epoch: 21 loss: 0.4703573033369526 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 22 loss: 0.33882861601850434 acc: 0.9 prec: 0.6666666666666666 recall: : 0.7058823529411765 epoch: 23 loss: 0.6288586365318634 acc: 0.86 prec: 0.5789473684210527 recall: : 0.6470588235294118 epoch: 24 loss: 0.4446407145373905 acc: 0.9 prec: 0.7692307692307693 recall: : 0.5882352941176471 epoch: 25 loss: 0.47516126279861737 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 26 loss: 0.47878878450462253 acc: 0.87 prec: 0.6 recall: : 0.7058823529411765 epoch: 27 loss: 0.406448530066898 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 28 loss: 0.5326147545382947 acc: 0.88 prec: 0.6470588235294118 recall: : 0.6470588235294118 epoch: 29 loss: 0.35017394204057384 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 30 loss: 0.5492841665070227 acc: 0.85 prec: 0.5555555555555556 recall: : 0.5882352941176471 epoch: 31 loss: 0.45283244484153784 acc: 0.9 prec: 0.7058823529411765 recall: : 0.7058823529411765 epoch: 32 loss: 0.40580460080429476 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 33 loss: 0.5504078443901653 acc: 0.86 prec: 0.5789473684210527 recall: : 0.6470588235294118 epoch: 34 loss: 0.45548378403755124 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 35 loss: 0.4666948410707255 acc: 0.89 prec: 0.6875 recall: : 0.6470588235294118 epoch: 36 loss: 0.3942578120598796 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 37 loss: 0.395962362795658 acc: 0.9 prec: 0.7692307692307693 recall: : 0.5882352941176471 epoch: 38 loss: 0.44939344771950573 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 39 loss: 0.38211571767803887 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 40 loss: 0.48910969563327855 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 41 loss: 0.3446516449950968 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 42 loss: 0.4679804835932646 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 43 loss: 0.33552404487287274 acc: 0.9 prec: 0.7058823529411765 recall: : 0.7058823529411765 epoch: 44 loss: 0.4151001131474459 acc: 0.87 prec: 0.625 recall: : 0.5882352941176471 epoch: 45 loss: 0.36344730574960066 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118 epoch: 46 loss: 0.36800105327266464 acc: 0.88 prec: 0.6470588235294118 recall: : 0.6470588235294118 epoch: 47 loss: 0.3511931332464837 acc: 0.89 prec: 0.7142857142857143 recall: : 0.5882352941176471 epoch: 48 loss: 0.4371468522066334 acc: 0.87 prec: 0.6111111111111112 recall: : 0.6470588235294118 epoch: 49 loss: 0.3572919689995433 acc: 0.9 prec: 0.7333333333333333 recall: : 0.6470588235294118
(2*precision * recall) / (precision + recall)0.6875
Y_predictionstensor([-2.1121, 6.8412, -5.5392, -2.9573, -2.2702, -4.7000, -8.2250, -4.4908,
-8.5875], grad_fn=<AddBackward0>)ner_tags_re = {
0: 'O',
1: 'B-PER',
2: 'B-LOC',
3: 'I-PER',
4: 'B-MISC',
5: 'I-MISC',
6: 'I-LOC',
7: 'B-ORG',
8: 'I-ORG'
}
def generate_out(folder_path):
ner_model.eval()
ner_model.cpu()
print('Generating out')
X_dev = []
with open(f"{folder_path}/in.tsv", 'r') as file:
for line in file:
line = line.strip()
X_dev.append(line.split(' '))
test_tokens_ids = data_process(X_dev)
predicted_values = []
# for i in range(100):
for i in range(len(test_tokens_ids)):
pred_string = ''
for j in range(1, len(test_tokens_ids[i]) - 1):
X = test_tokens_ids[i][j - 1: j + 2]
X_raw_single = X_dev[i][j - 1: j + 2]
X = add_features(X, X_raw_single)
# X = X.to(device)
# print('train is cuda?', X.is_cuda)
try:
Y_predictions = ner_model(X)
id = torch.argmax(Y_predictions)
val = ner_tags_re[int(id)]
pred_string += val + ' '
except Exception as e:
print('Error', e)
predicted_values.append(pred_string[:-1])
lines = []
for line in predicted_values:
last_label = None
line = line.split(' ')
new_line = []
for label in line:
if (label != "O" and label[0:2] == "I-"):
if last_label == None or last_label == "O":
label = label.replace('I-', 'B-')
else:
label = "I-" + last_label[2:]
last_label = label
new_line.append(label)
lines.append(" ".join(new_line))
with open(f"{folder_path}/out.tsv", "w") as f:
for line in lines:
f.write(str(line) + "\n")
f.close()generate_out('dev-0')
generate_out('test-A')Generating out Error index out of range in self Error index out of range in self Error index out of range in self Generating out