forked from kubapok/en-ner-conll-2003
1.9 MiB
1.9 MiB
import numpy as np
import gensim
import torch
import pandas as pd
import csv
import seaborn as sns
from sklearn.model_selection import train_test_split
from torchtext.vocab import Vocab
from collections import Counter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_scoreC:\Users\p_ulanicki\anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning. warnings.warn(msg)
train_set = pd.read_table('train/train.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)train_set| 0 | 1 | |
|---|---|---|
| 0 | B-ORG O B-MISC O O O B-MISC O O O B-PER I-PER ... | EU rejects German call to boycott British lamb... |
| 1 | O B-PER O O O O O O O O O B-LOC O O O O O O O ... | Rare Hendrix song draft sells for almost $ 17,... |
| 2 | B-LOC O B-LOC O O O O O O B-LOC O O B-LOC O O ... | China says Taiwan spoils atmosphere for talks ... |
| 3 | B-LOC O O O O B-LOC O O O B-LOC O O B-LOC O O ... | China says time right for Taiwan talks . </S> ... |
| 4 | B-MISC O O O O O O O O O O O B-LOC O O B-MISC ... | German July car registrations up 14.2 pct yr /... |
| ... | ... | ... |
| 940 | O O B-PER O O O O O O O B-PER I-PER O B-LOC O ... | CYCLING - BALLANGER KEEPS SPRINT TITLE IN STYL... |
| 941 | O O O O O O O O B-LOC O B-LOC O O O O O O O O ... | CYCLING - WORLD TRACK CHAMPIONSHIP RESULTS . <... |
| 942 | O O B-MISC O B-PER O B-ORG O O B-LOC O B-LOC O... | SOCCER - FRENCH DEFENDER KOMBOUARE JOINS ABERD... |
| 943 | O O B-LOC I-LOC B-MISC I-MISC O O O O B-LOC O ... | MOTORCYCLING - SAN MARINO GRAND PRIX PRACTICE ... |
| 944 | O O B-MISC I-MISC O O O O O B-LOC O B-LOC O O ... | GOLF - BRITISH MASTERS THIRD ROUND SCORES . </... |
945 rows × 2 columns
train = pd.read_table('train/train.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
def split(x):
return x.split()
def replace(x):
newList = []
for word in x:
if word == 'O':
newList.append(0)
if word == 'B-LOC':
newList.append(1)
if word == 'I-LOC':
newList.append(2)
if word == 'B-MISC':
newList.append(3)
if word == 'B-ORG':
newList.append(4)
if word == 'I-ORG':
newList.append(5)
if word == 'B-PER':
newList.append(6)
if word == 'I-PER':
newList.append(7)
return newList
train[0] = train[0].map(split)
train[1] = train[1].map(split)
train[0] = train[0].map(replace)def build_vocab(dataset):
counter = Counter()
for document in dataset:
counter.update(document)
return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])
def labels_process(dt):
return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]
def data_process(dt):
return [ torch.tensor([vocab['<bos>']] +[vocab[token] for token in document ] + [vocab['<eos>']], dtype = torch.long) for document in dt]class NeuralNetwork(torch.nn.Module):
def __init__(self, output_size):
super(NeuralNetwork, self).__init__()
self.fc1 = torch.nn.Linear(10_000,len(train_tokens_ids))
self.softmax = torch.nn.Softmax(dim=0)
def forward(self, x):
x = self.fc1(x)
x = self.softmax(x)
return x
class NERModel(torch.nn.Module):
def __init__(self,):
super(NERModel, self).__init__()
self.emb = torch.nn.Embedding(23627,200)
self.fc1 = torch.nn.Linear(600,9)
def forward(self, x):
x = self.emb(x)
x = x.reshape(600)
x = self.fc1(x)
return xvocab = build_vocab(train[1])
train_labels = labels_process(train[0])
train_tokens_ids = data_process(train[1])ner_model = NERModel()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(ner_model.parameters())
nn_model = NeuralNetwork(len(train_tokens_ids))train_tokens_ids[tensor([ 2, 967, 22410, 239, 774, 10, 4588, 213, 7687, 5,
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11, 1767, 9, 421, 5768, 8, 4249, 16, 93, 5,
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6, 2576, 15157, 12, 13740, 14, 3579, 13, 5, 4,
430, 33, 1799, 4, 3]),
tensor([ 2, 130, 21, 19037, 1387, 449, 9658, 2076, 472, 10358,
1965, 5, 4, 14858, 6, 3309, 2759, 4, 3309, 60,
704, 9705, 2076, 12, 841, 736, 13, 9, 11, 231,
8075, 279, 150, 16, 95, 5, 4, 1025, 33, 4,
3309, 21, 448, 16370, 12, 3554, 6, 3579, 14, 4198,
277, 13, 6, 1252, 3869, 12, 2760, 13, 4, 430,
33, 8198, 4, 3]),
tensor([ 2, 1687, 21, 14852, 5308, 10104, 3841, 472, 18287, 5,
4, 482, 8814, 4, 1704, 6, 133, 2759, 4, 6657,
3613, 8, 134, 862, 99, 2238, 34, 7, 194, 18,
278, 76, 765, 6088, 82, 171, 7912, 99, 874, 27,
7, 194, 2627, 1616, 16, 95, 5, 4, 3613, 60,
118, 18, 6425, 5422, 736, 9, 7, 19569, 223, 296,
10, 2361, 7, 194, 874, 10, 7, 559, 897, 3031,
171, 104, 9, 195, 5, 4, 134, 84, 270, 173,
456, 9, 7, 2462, 6, 6895, 5208, 7460, 20562, 310,
7178, 5185, 8, 568, 736, 5, 4, 3613, 6, 308,
6, 48, 43, 4555, 10, 1036, 11, 1475, 1198, 82,
171, 4628, 99, 2263, 246, 117, 1059, 874, 16, 137,
5, 4, 22, 128, 296, 8, 7, 527, 6, 7,
478, 18, 13207, 122, 383, 6, 84, 401, 9, 2735,
19, 7, 4814, 310, 5, 4, 227, 18, 2925, 7099,
2654, 151, 11, 22892, 2172, 30, 314, 5291, 7030, 10,
259, 7, 874, 19, 11, 83, 68, 5, 4, 227,
6, 7, 139, 1099, 10, 45, 56, 4825, 9, 7,
2652, 6, 182, 942, 822, 8, 58, 21784, 23058, 5,
4, 9336, 9244, 1884, 7099, 10, 2374, 44, 11866, 122,
276, 113, 22836, 7, 3912, 3031, 5, 4, 7030, 6,
11, 167, 4032, 9, 7, 122, 1538, 6, 628, 7,
383, 27, 245, 3948, 5, 15, 4, 64, 540, 290,
345, 71, 117, 6, 50, 1042, 56, 4825, 3349, 390,
7, 2904, 44, 2159, 6, 15, 171, 17, 5, 4,
22, 51, 157, 4825, 21331, 7, 2652, 6, 61, 322,
167, 194, 310, 9466, 9383, 8, 7, 307, 155, 9,
1559, 456, 777, 1042, 7, 83, 3992, 122, 2053, 5,
4, 559, 310, 2882, 9603, 8, 134, 84, 3447, 7,
610, 601, 14, 1543, 11, 2635, 1225, 5, 4, 3]),
tensor([ 2, 1687, 21, 1331, 18669, 1576, 199, 5, 4, 1704,
6, 133, 2759, 4, 244, 27, 7, 194, 1475, 2627,
1616, 16, 95, 33, 4, 399, 18, 2462, 6062, 12,
620, 8, 89, 13, 33, 4, 6425, 5422, 12, 118,
13, 60, 6895, 5208, 12, 134, 13, 736, 12, 12543,
14, 12544, 218, 19, 7, 72, 1164, 246, 13, 4,
6657, 3613, 12, 134, 13, 60, 7178, 5185, 12, 568,
13, 736, 6, 12, 12535, 80, 12534, 13, 4, 18122,
19, 173, 456, 33, 4, 5208, 60, 5185, 736, 12,
12533, 80, 12542, 13, 4, 2524, 33, 4, 3613, 60,
5422, 736, 12, 12473, 80, 12541, 13, 4, 399, 18,
194, 122, 383, 467, 12, 13206, 13, 33, 4, 211,
2925, 7099, 12, 227, 13, 480, 122, 12, 9, 1008,
4, 277, 13485, 218, 13, 4, 207, 5291, 7030, 12,
52, 13, 517, 122, 4, 208, 9336, 9244, 12, 227,
13, 375, 4, 324, 18718, 18573, 12, 589, 13, 146,
4, 328, 3225, 14785, 12, 118, 13, 146, 4, 329,
18724, 18992, 12, 522, 13, 49, 4, 802, 1546, 389,
33, 4, 411, 9466, 9383, 12, 307, 13, 191, 4,
444, 10126, 15129, 12, 126, 13, 101, 4, 613, 8742,
16198, 12, 741, 13, 110, 4, 694, 2882, 9603, 12,
134, 13, 110, 4, 3]),
tensor([ 2, 130, 21, 1811, 15557, 16747, 9475, 14552, 5, 4,
14551, 6, 1131, 2759, 4, 301, 474, 1344, 5068, 6847,
38, 896, 11, 6310, 1053, 3515, 12, 54, 13783, 13,
1358, 10, 2773, 30, 973, 622, 2144, 6, 7, 2139,
1102, 229, 622, 17, 16, 95, 5, 4, 6847, 38,
583, 11, 6145, 807, 14, 48, 382, 40, 2014, 79,
6930, 9, 7, 2139, 397, 209, 16, 85, 5, 4,
111, 35, 48, 43, 7655, 19, 7, 1284, 8, 2773,
18, 2594, 209, 602, 34, 35, 38, 473, 132, 19,
2144, 9, 71, 256, 18, 209, 3866, 115, 209, 5,
4, 2773, 667, 7068, 14672, 17, 33, 15, 87, 18,
4139, 19, 673, 29, 5068, 2171, 326, 9, 587, 50,
35, 48, 647, 673, 4547, 1563, 9, 1197, 1266, 5,
4, 15, 64, 45, 53, 6168, 164, 19, 477, 513,
255, 14, 45, 102, 5774, 35, 7354, 2224, 3477, 10,
7, 582, 5, 4, 86, 38, 11, 986, 371, 8,
3964, 14, 64, 914, 4876, 35, 48, 2048, 4650, 987,
9, 359, 7, 225, 14, 7, 19320, 8, 317, 2023,
5, 15, 4, 22, 6317, 1344, 132, 312, 4836, 28,
2125, 14, 23, 28, 638, 379, 3203, 19, 161, 4836,
5, 4, 86, 17, 167, 4447, 225, 4029, 446, 16178,
6, 47, 255, 3051, 19, 558, 1102, 331, 2322, 6,
23, 21172, 9, 40, 1358, 10, 1131, 5, 4, 15,
64, 914, 11, 339, 402, 2655, 8, 446, 14, 2237,
10, 164, 1556, 96, 861, 10, 2773, 14, 35, 23,
339, 1363, 96, 39, 6, 15, 6847, 17, 5, 4,
15, 86, 17, 64, 62, 1157, 10924, 1356, 145, 14,
29, 64, 62, 4846, 9, 9, 1473, 8, 4671, 34,
305, 5, 4, 772, 6, 14, 7, 1539, 35, 32,
139, 11, 548, 508, 2398, 546, 6, 21171, 454, 534,
10, 750, 10, 2773, 5, 15, 4, 3]),
tensor([ 2, 6892, 21, 449, 9658, 2296, 2565, 9917, 3840, 5,
4, 16503, 6, 198, 2759, 4, 17881, 873, 250, 16,
95, 4, 19, 109, 18, 704, 9705, 8593, 21651, 884,
1838, 33, 4, 211, 398, 15708, 12, 123, 13, 1178,
76, 316, 13932, 4, 207, 9494, 14989, 12, 134, 13,
1992, 12914, 4, 208, 17545, 14617, 12, 215, 13, 1992,
12915, 4, 324, 17061, 15265, 12, 198, 13, 1178, 12917,
4, 328, 1064, 15508, 12, 163, 13, 1178, 12918, 4,
329, 1252, 1414, 12, 169, 350, 13, 10281, 12919, 4,
411, 18689, 17614, 12, 215, 13, 1178, 12920, 4, 444,
1390, 15360, 12, 163, 13, 1178, 12921, 4, 613, 6414,
8852, 12, 504, 13, 1178, 12923, 4, 694, 18433, 16607,
12, 215, 13, 1178, 12924, 4, 3]),
tensor([ 2, 1502, 21, 6451, 6889, 2588, 1839, 1715, 5, 4,
5417, 6, 133, 2759, 4, 680, 1558, 46, 4, 7,
173, 144, 8, 7, 213, 6912, 16, 95, 33, 4,
8396, 742, 4220, 12, 123, 13, 220, 238, 238, 4,
1926, 1975, 3739, 12, 163, 13, 367, 427, 330, 4,
13136, 2316, 2495, 482, 12, 163, 13, 394, 180, 238,
6, 3648, 3808, 4, 12, 198, 13, 238, 427, 367,
4, 13139, 5068, 9614, 12, 134, 13, 412, 427, 180,
6, 1071, 3313, 180, 794, 238, 6, 4, 1313, 4271,
12, 163, 13, 180, 238, 794, 6, 5233, 6871, 12,
149, 4, 320, 13, 319, 394, 794, 4, 6279, 1985,
9722, 427, 794, 220, 6, 9505, 9372, 12, 18618, 13,
238, 964, 4, 180, 6, 1072, 6540, 12, 770, 13,
220, 1931, 238, 6, 436, 4294, 394, 238, 367, 6,
4, 6842, 3193, 12, 254, 13, 238, 394, 367, 6,
1593, 5148, 12, 123, 13, 4, 220, 794, 427, 6,
368, 3274, 220, 238, 1931, 4, 8405, 6620, 6583, 12,
615, 13, 412, 794, 220, 6, 1303, 386, 12, 52,
13, 412, 394, 180, 6, 4, 436, 3735, 367, 394,
367, 6, 2316, 2495, 9502, 12, 163, 13, 412, 367,
4, 427, 6, 740, 3242, 412, 238, 394, 6, 1977,
10429, 12, 615, 13, 238, 412, 4, 412, 6, 740,
9842, 12, 123, 13, 238, 427, 394, 4, 4176, 4240,
6859, 427, 964, 180, 6, 1423, 5487, 12, 123, 13,
238, 1931, 238, 6, 4, 482, 9314, 238, 964, 367,
6, 15132, 16471, 12, 123, 13, 427, 394, 367, 6,
4, 740, 6739, 12, 254, 13, 180, 394, 394, 6,
5483, 5241, 12, 149, 4, 320, 13, 238, 412, 394,
6, 446, 6694, 220, 412, 964, 5, 4, 3])]for epoch in range(2):
loss_score = 0
acc_score = 0
prec_score = 0
selected_items = 0
recall_score = 0
relevant_items = 0
items_total = 0
nn_model.train()
for i in range(100):
for j in range(1, len(train_labels[i]) - 1):
X = train_tokens_ids[i][j-1: j+2]
Y = train_labels[i][j: j+1]
Y_predictions = ner_model(X)
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)
display('loss: ', loss_score / items_total)
display('acc: ', acc_score / items_total)
display('prec: ', precision)
display('recall: : ', recall)
display('f1: ', f1_score)'loss: '
0.5651189693641464
'acc: '
0.860387657861868
'prec: '
0.5599708348523514
'recall: : '
0.4143512274076072
'f1: '
0.4762790697674419
dev_in = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
dev_ex = pd.read_table('dev-0/expected.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)dev_in[0] = dev_in[0].map(split)
dev_ex[0] = dev_ex[0].map(split)
dev_ex[0] = dev_ex[0].map(replace)dev_ex[0]0 [0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...
1 [0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...
2 [0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 3, ...
3 [0, 0, 6, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...
4 [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...
...
210 [1, 0, 6, 0, 1, 0, 6, 0, 0, 1, 0, 0, 3, 0, 0, ...
211 [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 4, 5, ...
212 [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...
213 [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 3, 0, ...
214 [0, 4, 0, 0, 0, 1, 0, 0, 0, 6, 7, 0, 0, 0, 1, ...
Name: 0, Length: 215, dtype: objectdev_labels = labels_process(dev_ex[0])
dev_tokens_ids = data_process(dev_in[0])result = []
nn_model.eval()
for i in range(len(dev_tokens_ids)):
result.append([])
for j in range(1, len(dev_labels[i]) - 1):
X = dev_tokens_ids[i][j-1: j+2]
Y = dev_labels[i][j: j+1]
Y_predictions = ner_model(X)
loss = criterion(Y_predictions.unsqueeze(0), Y)
result[i].append(int(torch.argmax(Y_predictions)))def generate_result(result):
features = ['O', 'B-LOC', 'I-LOC', 'B-MISC', 'B-ORG', 'I-ORG', 'B-PER', 'B-PER', 'I-PER']
final_result = []
for i in range(len(result)):
final_result.append([])
for j in range(len(result[i])):
final_result[i].append(features[result[i][j]])
f = open("dev-0/out.tsv", "a")
for i in final_result:
f.write(' '.join(i) + '\n')
f.close()
generate_result(result)test_in = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
test_in[0] = test_in[0].map(split)
test_tokens_ids = data_process(test_in[0])result_test = []
nn_model.eval()
for i in range(len(test_tokens_ids)):
result_test.append([])
for j in range(1, len(test_tokens_ids[i]) - 1):
X = test_tokens_ids[i][j-1: j+2]
Y_predictions = ner_model(X)
loss = criterion(Y_predictions.unsqueeze(0), Y)
result_test[i].append(int(torch.argmax(Y_predictions)))def generate_result(result):
features = ['O', 'B-LOC', 'I-LOC', 'B-MISC', 'B-ORG', 'I-ORG', 'B-PER', 'B-PER', 'I-PER']
final_result = []
for i in range(len(result)):
final_result.append([])
for j in range(len(result[i])):
final_result[i].append(features[result[i][j]])
f = open("test-A/out.tsv", "a")
for i in final_result:
f.write(' '.join(i) + '\n')
f.close()
generate_result(result_test)