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en-ner-conll-2003
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fix

This commit is contained in:
wangobango 2021-06-28 11:48:51 +02:00
parent 6837f07f7e
commit 1e6e429e58
3 changed files with 170 additions and 42 deletions
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111
generate.py Normal file
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@ -0,0 +1,111 @@
import pandas as pd
import pickle
import torch
from sklearn.metrics import accuracy_score, f1_score, classification_report
from model import Model
from tqdm import tqdm
import csv
def process_output(lines):
result = []
for line in lines:
last_label = None
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)
x = (" ".join(new_line))
result.append(" ".join(new_line))
return result
def data_process(dt):
return [ torch.tensor([vocab[process_token(token)] for token in document.split(" ") ], dtype = torch.long) for document in dt]
def labels_process(dt):
return [ torch.tensor([labels_vocab[token] for token in document.split(" ") ], dtype = torch.long) for document in dt]
def process_document(document):
return [process_token(x) for x in document.split(" ")]
def save_file(path, obj):
with open(path, "w") as file:
file.write(obj)
def process_token(token):
return token.lower()
data = pd.read_csv("dev-0/in.tsv", sep="\t", names=['data'])
ex_data = pd.read_csv("dev-0/expected.tsv", sep="\t", names=['labels'])
in_data = data['data']
target = ex_data['labels']
num_tags = 9
seq_length = 5
save_path = "dev-0/out.tsv"
with open("vocab.pickle", "rb") as file:
vocab = pickle.load(file)
labels_vocab = {
'O': 0,
'B-PER': 1,
'B-LOC': 2,
'I-PER': 3,
'B-MISC': 4,
'I-MISC': 5,
'I-LOC': 6,
'B-ORG': 7,
'I-ORG': 8
}
inv_labels_vocab = {v: k for k, v in labels_vocab.items()}
train_tokens_ids = data_process(in_data)
train_labels = labels_process(target)
model = Model(num_tags, seq_length, vocab)
device = torch.device("cuda")
model.to(device)
model.cuda(0)
model.eval()
model.eval_mode()
predicted = []
correct = []
model.load_state_dict(torch.load("model.torch"))
for i in tqdm(range(0, len(train_tokens_ids))):
last_idx = 0
for k in range(0, len(train_tokens_ids[i]) - seq_length + 1, seq_length):
batch_tokens = train_tokens_ids[i][k: k + seq_length].unsqueeze(0)
tags = train_labels[i][k: k + seq_length].unsqueeze(1)
predicted_tags = model.decode(batch_tokens.to(device))
predicted += predicted_tags[0]
correct += [x[0] for x in tags.numpy().tolist()]
last_idx = k
l = len(train_tokens_ids[i])
rest = l - int(l/seq_length) * seq_length
if rest != 0:
batch_tokens = train_tokens_ids[i][last_idx: last_idx + rest].unsqueeze(0)
tags = train_labels[i][last_idx: last_idx + rest].unsqueeze(1)
predicted_tags = model.decode(batch_tokens.to(device))
predicted += predicted_tags[0]
correct += [x[0] for x in tags.numpy().tolist()]
print(classification_report(correct, predicted))
print(accuracy_score(correct, predicted))
print(f1_score(correct, predicted, average="micro"))
save_file("correct.txt", '\n'.join([str(x) for x in correct]))
save_file("predicted.txt", '\n'.join([str(x) for x in predicted]))
predicted = list(map(lambda x: inv_labels_vocab[x], predicted))
slices = [len(x.split(" ")) for x in in_data]
with open(save_path, "w") as save:
writer = csv.writer(save, delimiter='\t', lineterminator='\n')
accumulator = 0
output = []
for slice in slices:
output.append(predicted[accumulator: accumulator + slice])
accumulator += slice
for line in process_output(output):
writer.writerow([line])

67
main.py
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@ -13,38 +13,10 @@ import numpy as np
from sklearn.metrics import accuracy_score, f1_score, classification_report
import csv
import pickle
from model import Model
class Model(torch.nn.Module):
def __init__(self, num_tags, seq_length):
super(Model, self).__init__()
self.emb = torch.nn.Embedding(len(vocab.get_itos()), 100)
self.gru = torch.nn.GRU(100, 256, 1, batch_first=True)
self.hidden2tag = torch.nn.Linear(256, 9)
self.crf = CRF(num_tags, batch_first=True)
self.relu = torch.nn.ReLU()
self.fc1 = torch.nn.Linear(1, seq_length)
self.softmax = torch.nn.Softmax(dim=0)
self.sigm = torch.nn.Sigmoid()
nlp = spacy.load("en_core_web_sm")
def forward(self, data, tags):
emb = self.relu(self.emb(data))
out, h_n = self.gru(emb)
out = self.hidden2tag(out)
out = self.crf(out, tags.T)
return -out
def decode(self, data):
emb = self.relu(self.emb(data))
out, h_n = self.gru(emb)
out = self.hidden2tag(out)
out = self.crf.decode(out)
return out
def train_mode(self):
self.crf.train()
def eval_mode(self):
self.crf.eval()
def process_output(lines):
result = []
@ -64,7 +36,14 @@ def process_output(lines):
return result
def process_document(document):
return document.split(" ")
return [process_token(x) for x in document.split(" ")]
def save_file(path, obj):
with open(path, "w") as file:
file.write(obj)
def process_token(token):
return token.lower()
def build_vocab(dataset):
counter = Counter()
@ -78,26 +57,28 @@ def build_vocab(dataset):
return v
def data_process(dt):
return [ torch.tensor([vocab[token] for token in document.split(" ") ], dtype = torch.long) for document in dt]
return [ torch.tensor([vocab[process_token(token)] for token in document.split(" ") ], dtype = torch.long) for document in dt]
def labels_process(dt):
return [ torch.tensor([labels_vocab[token] for token in document.split(" ") ], dtype = torch.long) for document in dt]
# mode = "train"
# mode = "eval"
mode = "generate"
mode = "eval"
# mode = "generate"
save_path = "dev-0/out.tsv"
data = pd.read_csv("dev-0/in.tsv", sep="\t", names=['0'])
data = pd.read_csv("dev-0/in.tsv", sep="\t", names=['data'])
# data.columns = ["labels", "text"]
# train_target = pd.read_csv("train/train.tsv", sep = '\t', names = ['labels', 'data'])
ex_data = pd.read_csv("dev-0/expected.tsv", sep="\t", names=['labels'])
in_data = data["0"]
target = ex_data["labels"]
in_data = data['data']
target = ex_data['labels']
# in_data = data["0"]
# target = ex_data["labels"]
# test_data = pd.read_csv("test-A/in.tsv", sep = '\t', names=['0'])
# test_data.columns = ['0']
@ -137,10 +118,10 @@ num_tags = 9
NUM_EPOCHS = 5
seq_length = 5
model = Model(num_tags, seq_length)
device = torch.device("cpu")
model = Model(num_tags, seq_length, vocab)
device = torch.device("cuda")
model.to(device)
# model.cuda(0)
model.cuda(0)
if mode == "train":
criterion = torch.nn.CrossEntropyLoss()
@ -191,8 +172,10 @@ if mode == "eval" or mode == "generate":
if mode == "eval":
print(classification_report(correct, predicted))
print(accuracy_score(correct, predicted))
print(f1_score(correct, predicted, average="weighted"))
print(f1_score(correct, predicted, average="micro"))
save_file("correct.txt", '\n'.join([str(x) for x in correct]))
save_file("predicted.txt", '\n'.join([str(x) for x in predicted]))
predicted = list(map(lambda x: inv_labels_vocab[x], predicted))
slices = [len(x.split(" ")) for x in in_data]
with open(save_path, "w") as save:

34
model.py Normal file
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@ -0,0 +1,34 @@
import torch
from torchcrf import CRF
class Model(torch.nn.Module):
def __init__(self, num_tags, seq_length, vocab):
super(Model, self).__init__()
self.emb = torch.nn.Embedding(len(vocab.get_itos()), 100)
self.gru = torch.nn.GRU(100, 256, 1, batch_first=True)
self.hidden2tag = torch.nn.Linear(256, 9)
self.crf = CRF(num_tags, batch_first=True)
self.relu = torch.nn.ReLU()
self.fc1 = torch.nn.Linear(1, seq_length)
self.softmax = torch.nn.Softmax(dim=0)
self.sigm = torch.nn.Sigmoid()
def forward(self, data, tags):
emb = self.relu(self.emb(data))
out, h_n = self.gru(emb)
out = self.hidden2tag(out)
out = self.crf(out, tags.T)
return -out
def decode(self, data):
emb = self.relu(self.emb(data))
out, h_n = self.gru(emb)
out = self.hidden2tag(out)
out = self.crf.decode(out)
return out
def train_mode(self):
self.crf.train()
def eval_mode(self):
self.crf.eval()