本仓库用于存放《自然语言处理：基于预训练模型的方法》（作者：车万翔、郭江、崔一鸣）一书各章节的示例代码。
2.1.2节（词的分布式表示），pmi计算函数代码中，以下语句注释中的“分子”应改为“分母”
expected = np.outer(row_totals, col_totals) / total # 获得每个元素的分母【数学符号】一节中，【线性代数】部分【矩阵A与矩阵B的Hardamard乘积】中，Hardamard的拼写应该为Hadamard。
书中2.1.2节：3.奇异值分解小节（17页）中，矩阵V的维度应为|C| x r，即$\bm{V} \in \mathbb{R}^{|\mathbb{C}| \times r}$。
书中3.4.3节convert_t2s.py：
f_in = open(sys.argv[0], "r")修正为
f_in = open(sys.argv[1], "r")书中3.4.3节wikidata_cleaning.py：
f_in = open(sys.argv[0], 'r')修正为
f_in = open(sys.argv[1], 'r')此外，为了兼容Python 3.7以上版本，将remove_control_chars函数修改为：
def remove_control_chars(in_str):
control_chars = ''.join(map(chr, list(range(0, 32)) + list(range(127, 160))))
control_chars = re.compile('[%s]' % re.escape(control_chars))
return control_chars.sub('', in_str)书中4.6.1节Vocab类的__init__与build方法有误，修正为：
class Vocab:
def __init__(self, tokens=None):
self.idx_to_token = list()
self.token_to_idx = dict()
if tokens is not None:
if "<unk>" not in tokens:
tokens = tokens + ["<unk>"]
for token in tokens:
self.idx_to_token.append(token)
self.token_to_idx[token] = len(self.idx_to_token) - 1
self.unk = self.token_to_idx['<unk>']
@classmethod
def build(cls, text, min_freq=1, reserved_tokens=None):
token_freqs = defaultdict(int)
for sentence in text:
for token in sentence:
token_freqs[token] += 1
uniq_tokens = ["<unk>"] + (reserved_tokens if reserved_tokens else [])
uniq_tokens += [token for token, freq in token_freqs.items() \
if freq >= min_freq and token != "<unk>"]
return cls(uniq_tokens)书中4.6.5节使用的MLP模型类是基于EmbeddingBag的MLP实现，与4.6.3节的MLP实现有所区别，具体如下：
class MLP(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class):
super(MLP, self).__init__()
self.embedding = nn.EmbeddingBag(vocab_size, embedding_dim)
self.linear1 = nn.Linear(embedding_dim, hidden_dim)
self.activate = F.relu
self.linear2 = nn.Linear(hidden_dim, num_class)
def forward(self, inputs, offsets):
embedding = self.embedding(inputs, offsets)
hidden = self.activate(self.linear1(embedding))
outputs = self.linear2(hidden)
log_probs = F.log_softmax(outputs, dim=1)
return log_probs书中6.2.3节ELMoLstmEncoder类的forward函数实现有误，修正为：
def forward(self, inputs, lengths):
batch_size, seq_len, input_dim = inputs.shape
rev_idx = torch.arange(seq_len).unsqueeze(0).repeat(batch_size, 1)
for i in range(lengths.shape[0]):
rev_idx[i,:lengths[i]] = torch.arange(lengths[i]-1, -1, -1)
rev_idx = rev_idx.unsqueeze(2).expand_as(inputs)
rev_idx = rev_idx.to(inputs.device)
rev_inputs = inputs.gather(1, rev_idx)
forward_inputs, backward_inputs = inputs, rev_inputs
stacked_forward_states, stacked_backward_states = [], []
for layer_index in range(self.num_layers):
# Transfer `lengths` to CPU to be compatible with latest PyTorch versions.
packed_forward_inputs = pack_padded_sequence(
forward_inputs, lengths.cpu(), batch_first=True, enforce_sorted=False)
packed_backward_inputs = pack_padded_sequence(
backward_inputs, lengths.cpu(), batch_first=True, enforce_sorted=False)
# forward
forward_layer = self.forward_layers[layer_index]
packed_forward, _ = forward_layer(packed_forward_inputs)
forward = pad_packed_sequence(packed_forward, batch_first=True)[0]
forward = self.forward_projections[layer_index](forward)
stacked_forward_states.append(forward)
# backward
backward_layer = self.backward_layers[layer_index]
packed_backward, _ = backward_layer(packed_backward_inputs)
backward = pad_packed_sequence(packed_backward, batch_first=True)[0]
backward = self.backward_projections[layer_index](backward)
# convert back to original sequence order using rev_idx
stacked_backward_states.append(backward.gather(1, rev_idx))
forward_inputs, backward_inputs = forward, backward
# stacked_forward_states: [batch_size, seq_len, projection_dim] * num_layers
# stacked_backward_states: [batch_size, seq_len, projection_dim] * num_layers
return stacked_forward_states, stacked_backward_states书中7.4.3节（199页）"句对文本分类"→"代码实现"中的tokenize()函数存在问题，请按如下进行修正。
def tokenize(examples):
return tokenizer(examples['hypothesis'], examples['premise'], truncation=True, padding='max_length')修正为
def tokenize(examples):
return tokenizer(examples['sentence1'], examples['sentence2'], truncation=True, padding='max_length')书中5.3.4节（143页）GloVe词向量训练部分代码在计算L2损失时存在问题，请按如下进行修正。
loss = (torch.sum(word_embeds * context_embeds, dim=1) + word_biases + context_biases - log_counts) ** 2修正为
loss = (torch.sum(word_embeds * context_embeds, dim=1, keepdim=True) + word_biases + context_biases - log_counts) ** 2