README.md

---
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
```