vocab_size = 98635 max_len=200 hidden_dim=32这3段代码啥意思

这三段代码应该是指在构建模型时使用的一些参数。

- `vocab_size = 98635`：这是指模型使用的词汇表（Vocabulary）的大小，也就是模型中可以使用的不同词汇的数量。这个值通常是根据语料库中的词汇数量来确定的。

- `max_len=200`：这是指模型处理的输入文本的最大长度，超过这个长度的部分将会被截断。这个值通常是根据语料库中文本的长度分布来确定的。

- `hidden_dim=32`：这是指模型中隐藏层的维度大小，也就是模型在处理文本时使用的特征的数量。这个值通常是根据实验调整得到的，一般来说，更大的值可以提高模型的表达能力，但会增加模型的复杂度和计算量。

相关问题

写一段transformer代码

### 回答1：
好的，以下是一个简单的 Transformer 代码示例：

import tensorflow as tf

class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, rate=0.1):
        super(Transformer, self).__init__()

        self.encoder = Encoder(num_layers, d_model, num_heads, dff, input_vocab_size, rate)

        self.decoder = Decoder(num_layers, d_model, num_heads, dff, target_vocab_size, rate)

        self.final_layer = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inp, tar, training, enc_padding_mask, look_ahead_mask, dec_padding_mask):
        enc_output = self.encoder(inp, training, enc_padding_mask)

        dec_output, attention_weights = self.decoder(
            tar, enc_output, training, look_ahead_mask, dec_padding_mask)

        final_output = self.final_layer(dec_output)

        return final_output, attention_weights


class Encoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, rate=0.1):
        super(Encoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(input_vocab_size, self.d_model)

        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
                           for _ in range(num_layers)]

        self.dropout = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask):
        seq_len = tf.shape(x)[1]

        # adding embedding and position encoding.
        x = self.embedding(x)  # (batch_size, input_seq_len, d_model)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]

        x = self.dropout(x, training=training)

        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training, mask)

        return x


class EncoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(EncoderLayer, self).__init__()

        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = point_wise_feed_forward_network(d_model, dff)

        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask):
        attn_output, _ = self.mha(x, x, x, mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(x + attn_output)

        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)

        return out2


class Decoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size, rate=0.1):
        super(Decoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers  

### 回答2：
Transformer是一种用于自然语言处理的深度学习模型。以下是一个使用Python编写的简单Transformer代码段，用于进行文本分类任务：

import torch
import torch.nn as nn
import torch.optim as optim

class Transformer(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads):
super(Transformer, self).__init__()

self.embedding = nn.Embedding(input_dim, hidden_dim)
self.positional_encoding = PositionalEncoding(hidden_dim)
self.transformer_encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers)

self.fc = nn.Linear(hidden_dim, output_dim)

def forward(self, x):
embedded = self.embedding(x)
encoded = self.positional_encoding(embedded)
transformed = self.transformer_encoder(encoded)
pooled = torch.mean(transformed, dim=1)
output = self.fc(pooled)

return output

class PositionalEncoding(nn.Module):
def __init__(self, hidden_dim, max_seq_len=300):
super(PositionalEncoding, self).__init__()

self.hidden_dim = hidden_dim
self.dropout = nn.Dropout(p=0.1)

pe = torch.zeros(max_seq_len, hidden_dim)
position = torch.arange(0, max_seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_dim, 2) * -(math.log(10000.0) / hidden_dim))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)

self.register_buffer('pe', pe.unsqueeze(0))

def forward(self, x):
x = x * math.sqrt(self.hidden_dim)
x = x + self.pe[:, :x.size(1)]
x = self.dropout(x)

return x

以上代码定义了一个Transformer模型类，包括一个词嵌入层、位置编码层、Transformer编码层和一个全连接层。其中，位置编码层使用来自论文《Attention is All You Need》中提出的方法，用于为序列中的词汇位置添加信息。模型的前向传播过程首先对输入的文本进行词嵌入，然后进行位置编码，接着使用Transformer编码层进行特征提取和表示学习，将输出进行平均池化后再通过全连接层进行分类预测。这段代码可以用于文本分类任务中，输入是一个整数序列，输出是每个类别的预测概率。  

### 回答3：
Transformer是一种深度学习模型架构，适用于自然语言处理任务，例如机器翻译、文本生成等。下面是一个简单的Transformer代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerEncoder(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_heads):
super(TransformerEncoder, self).__init__()

self.embedding = nn.Embedding(input_size, hidden_size)
self.position_encoding = PositionalEncoding(hidden_size)
self.encoder_layer = TransformerEncoderLayer(hidden_size, num_heads)

def forward(self, inputs):
embeddings = self.embedding(inputs)
encoded = self.position_encoding(embeddings)
output = self.encoder_layer(encoded)
return output

class PositionalEncoding(nn.Module):
def __init__(self, hidden_size, max_sequence_length=1000):
super(PositionalEncoding, self).__init__()

position_encoding = torch.zeros(max_sequence_length, hidden_size)
position = torch.arange(0, max_sequence_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_size, 2).float() * (-math.log(10000.0) / hidden_size))

position_encoding[:, 0::2] = torch.sin(position * div_term)
position_encoding[:, 1::2] = torch.cos(position * div_term)

self.register_buffer('position_encoding', position_encoding)

def forward(self, inputs):
seq_length = inputs.size(1)
position_encoding = self.position_encoding[:seq_length, :]
return inputs + position_encoding

class TransformerEncoderLayer(nn.Module):
def __init__(self, hidden_size, num_heads, dropout=0.1):
super(TransformerEncoderLayer, self).__init__()

self.self_attention = MultiHeadAttention(hidden_size, num_heads)
self.feed_forward = FeedForward(hidden_size)
self.dropout = nn.Dropout(dropout)

def forward(self, inputs):
attended = self.self_attention(inputs)
attended = self.dropout(attented)
output = attended + inputs
output = self.feed_forward(output)
output = self.dropout(output)
return output

class MultiHeadAttention(nn.Module):
def __init__(self, hidden_size, num_heads):
super(MultiHeadAttention, self).__init__()

self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_size = hidden_size // num_heads

self.W_q = nn.Linear(hidden_size, hidden_size)
self.W_k = nn.Linear(hidden_size, hidden_size)
self.W_v = nn.Linear(hidden_size, hidden_size)
self.W_o = nn.Linear(hidden_size, hidden_size)

def forward(self, inputs):
batch_size, seq_length, _ = inputs.size()

query = self.W_q(inputs)
key = self.W_k(inputs)
value = self.W_v(inputs)

query = query.view(batch_size, seq_length, self.num_heads, self.head_size).transpose(1, 2)
key = key.view(batch_size, seq_length, self.num_heads, self.head_size).transpose(1, 2)
value = value.view(batch_size, seq_length, self.num_heads, self.head_size).transpose(1, 2)

attention_scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.head_size)
attention_probs = F.softmax(attention_scores, dim=-1)
attended = torch.matmul(attention_probs, value).transpose(1, 2).contiguous().view(batch_size, seq_length, self.hidden_size)

output = self.W_o(attended)
return output

class FeedForward(nn.Module):
def __init__(self, hidden_size, dropout=0.1):
super(FeedForward, self).__init__()

self.linear1 = nn.Linear(hidden_size, 4 * hidden_size)
self.linear2 = nn.Linear(4 * hidden_size, hidden_size)
self.dropout = nn.Dropout(dropout)

def forward(self, inputs):
output = self.linear1(inputs)
output = F.relu(output)
output = self.dropout(output)
output = self.linear2(output)
return output

以上是一个简单的Transformer模型代码示例，包括了TransformerEncoder、PositionalEncoding、TransformerEncoderLayer、MultiHeadAttention和FeedForward等模块的实现。这段代码可以作为一个Transformer模型的基本框架，但是其中的具体实现可能需要根据具体任务需求进行调整和优化。

写一段NER任务的完整代码

下面是一个基于PyTorch的NER任务代码示例，其中使用了BiLSTM-CRF模型：

import torch
import torch.nn as nn
import torch.optim as optim

class BiLSTM_CRF(nn.Module):

    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
        super(BiLSTM_CRF, self).__init__()
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.tagset_size = len(tag_to_ix)

        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
                            num_layers=1, bidirectional=True)

        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)

        self.transitions = nn.Parameter(
            torch.randn(self.tagset_size, self.tagset_size))

        self.transitions.data[tag_to_ix[START_TAG], :] = -10000
        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000

        self.hidden = self.init_hidden()

    def init_hidden(self):
        return (torch.randn(2, 1, self.hidden_dim // 2),
                torch.randn(2, 1, self.hidden_dim // 2))

    def _forward_alg(self, feats):
        init_alphas = torch.full((1, self.tagset_size), -10000.)
        init_alphas[0][self.tag_to_ix[START_TAG]] = 0.

        forward_var = init_alphas

        for feat in feats:
            alphas_t = []
            for next_tag in range(self.tagset_size):
                emit_score = feat[next_tag].view(
                    1, -1).expand(1, self.tagset_size)
                trans_score = self.transitions[next_tag].view(1, -1)
                next_tag_var = forward_var + trans_score + emit_score
                alphas_t.append(torch.logsumexp(next_tag_var, dim=1).view(1))
            forward_var = torch.cat(alphas_t).view(1, -1)
        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
        alpha = torch.logsumexp(terminal_var, dim=1)[0]
        return alpha

    def _get_lstm_features(self, sentence):
        self.hidden = self.init_hidden()
        embeds = self.word_embeddings(sentence).view(len(sentence), 1, -1)
        lstm_out, self.hidden = self.lstm(embeds, self.hidden)
        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
        lstm_feats = self.hidden2tag(lstm_out)
        return lstm_feats

    def _score_sentence(self, feats, tags):
        score = torch.zeros(1)
        tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
        for i, feat in enumerate(feats):
            score = score + \
                self.transitions[tags[i+1], tags[i]] + feat[tags[i+1]]
        score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]
        return score

    def _viterbi_decode(self, feats):
        backpointers = []

        init_vvars = torch.full((1, self.tagset_size), -10000.)
        init_vvars[0][self.tag_to_ix[START_TAG]] = 0

        forward_var = init_vvars
        for feat in feats:
            bptrs_t = []  
            viterbivars_t = []  

            for next_tag in range(self.tagset_size):
                next_tag_var = forward_var + self.transitions[next_tag]
                best_tag_id = argmax(next_tag_var)
                bptrs_t.append(best_tag_id)
                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
            backpointers.append(bptrs_t)

        terminal_var = forward_var + \
            self.transitions[self.tag_to_ix[STOP_TAG]]
        best_tag_id = argmax(terminal_var)
        path_score = terminal_var[0][best_tag_id]

        best_path = [best_tag_id]
        for bptrs_t in reversed(backpointers):
            best_tag_id = bptrs_t[best_tag_id]
            best_path.append(best_tag_id)
        start = best_path.pop()
        assert start == self.tag_to_ix[START_TAG]
        best_path.reverse()
        return path_score, best_path

    def neg_log_likelihood(self, sentence, tags):
        lstm_feats = self._get_lstm_features(sentence)
        forward_score = self._forward_alg(lstm_feats)
        gold_score = self._score_sentence(lstm_feats, tags)
        return forward_score - gold_score

    def forward(self, sentence):  
        lstm_feats = self._get_lstm_features(sentence)
        score, tag_seq = self._viterbi_decode(lstm_feats)
        return score, tag_seq

START_TAG = "<START>"
STOP_TAG = "<STOP>"
EMBEDDING_DIM = 5
HIDDEN_DIM = 4

training_data = [(
    "the wall street journal reported today that apple corporation made money".split(),
    "B I I I O O O B I O O".split()
), (
    "georgia tech is a university in georgia".split(),
    "B I O O O O B".split()
)]

word_to_ix = {}
for sentence, tags in training_data:
    for word in sentence:
        if word not in word_to_ix:
            word_to_ix[word] = len(word_to_ix)

tag_to_ix = {"B": 0, "I": 1, "O": 2, START_TAG: 3, STOP_TAG: 4}

model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)

for epoch in range(300):
    for sentence, tags in training_data:
        model.zero_grad()

        sentence_in = prepare_sequence(sentence, word_to_ix)
        targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)

        loss = model.neg_log_likelihood(sentence_in, targets)

        loss.backward()
        optimizer.step()

with torch.no_grad():
    precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
    print(model(precheck_sent))

这份代码实现了一个简单的NER任务的训练和预测，其中BiLSTM-CRF模型结合了BiLSTM和CRF两种技术，用于标注每个单词的实体类型。可以通过调整模型超参数和优化器参数来改进模型性能。