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---
license: mit
language:
- zh
pipeline_tag: text-classification
---
# Bert Chinese Text Classification Model
this a Bert Model that train for customer service of logistics companies
### data(with noise since it from ASR text)
train: 10878 rows
dev:2720 rows
total: 13598 rows
### param
embed_dim: 128
batch size: 64
contextsize: 20
n_head: 2
epoches: 100
## Word Label(word, index, number of occurences)
```sh
我 1 18719
个 2 12236
快 3 8152
一 4 8097
递 5 7295
那 6 7118
了 7 6923
的 8 6684
是 9 6632
到 10 6434
你 11 5144
没 12 4989
有 13 4664
下 14 4433
这 15 4219
在 16 4219
么 17 4010
查 18 3964
就 19 3570
好 20 3524
```
## Tokenizer
```python
label_dict, label_n2w = read_labelFile(labelFile)
word2ind, ind2word = get_worddict(wordLabelFile)
stoplist = read_stopword(stopwordFile)
cla_dict = {}
# train data to vec
traindataTxt = open(trainDataVecFile, 'w')
datas = open(trainFile, 'r', encoding='utf_8').readlines()
datas = list(filter(None, datas))
random.shuffle(datas)
for line in tqdm(datas, desc="traindata to vec"):
line = line.replace('\n', '').split(':')
# line = line.replace('\n','').split('\t')
cla = line[1]
# if cla in [21, 13, 9, 24, 23, 19, 14]:
# continue
if cla in cla_dict:
cla_dict[cla] += 1
else:
cla_dict[cla] = 1
cla_ind = label_dict[cla]
title_seg = ['我', '要', '下', '单']
title_seg = [i for i in line[0]]
# title_seg = jieba.cut(line[0], cut_all=False)
title_ind = [cla_ind]
for w in title_seg:
if w in stoplist:
continue
title_ind.append(word2ind[w])
length = len(title_ind)
if length > maxLen + 1:
title_ind = title_ind[0:21]
if length < maxLen + 1:
title_ind.extend([0] * (maxLen - length + 1))
for n in title_ind:
traindataTxt.write(str(n) + ',')
traindataTxt.write('\n')
```
## Trainer
```python
print('init net...')
model = my_model()
model.to(device)
print(model)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0005)
criterion = nn.CrossEntropyLoss()
print("training...")
best_dev_acc = 0
# embed.train()
for epoch in range(100):
model.train()
for i, (clas, sentences) in enumerate(train_dataLoader):
# sentences: batch size 64 x sentence length 20 x embed dimension 128
# 一个字是个128维vector 一句话是个 20x128的2D tensor 一个batch有64句话是个 64x20x128的3D tensor
out = model(sentences.to(
device)) # out: batch size 64 x word vector 4 (after my_linear)
try:
loss = criterion(out, clas.to(device))
except:
print(out.size(), out)
print(clas.size(), clas)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 10 == 0:
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
model.eval()
dev_acc = validation(model=model, val_dataLoader=val_dataLoader,
device=device)
if best_dev_acc < dev_acc:
best_dev_acc = dev_acc
print("save model...")
torch.save(model.state_dict(), "model.bin")
print("epoch:", epoch + 1, "step:", i + 1, "loss:", loss.item())
print("best dev acc %.4f dev acc %.4f" % (best_dev_acc, dev_acc))
```
## Testing
```python
def validation(model, val_dataLoader, device):
model.eval()
total = 0
correct = 0
with torch.no_grad():
for i, (clas, sentences) in enumerate(val_dataLoader):
try:
# sentences = sentences.type(torch.LongTensor).to(device)
# clas = clas.type(torch.LongTensor).to(device)
out = model(
sentences.to(
device)) # out: batch size 64 x sentences length 20 x word dimension 4(after my_linear)
# out = F.relu(out.squeeze(-3))
# out = F.max_pool1d(out, out.size(2)).squeeze(2)
# softmax = nn.Softmax(dim=1)
pred = torch.argmax(out, dim=1) # 64x4 -> 64x1
correct += (pred == clas.to(device)).sum()
total += clas.size()[0]
except IndexError as e:
print(i)
print('clas', clas)
print('clas size', clas.size())
print('sentence', sentences)
print('sentences size', sentences.size())
print(e)
print(e.__traceback__)
exit()
acc = correct / total
return acc
``` |