怎么更改self.tokenizer的参数
时间: 2024-05-09 07:17:05 浏览: 11
要更改self.tokenizer的参数,您可以通过以下步骤进行:
1. 从transformers库中导入适当的tokenizer类。例如,如果您正在使用GPT-2模型,则可以使用GPT2Tokenizer。
2. 创建一个新的tokenizer实例,并使用要更改的参数来初始化它。例如,如果您想更改GPT2Tokenizer的max_length参数,则可以使用以下代码:
```
from transformers import GPT2Tokenizer
new_tokenizer = GPT2Tokenizer.from_pretrained('gpt2', max_length=1024)
```
3. 将新tokenizer实例分配给self.tokenizer。例如:
```
self.tokenizer = new_tokenizer
```
这样,您就可以更改self.tokenizer的参数,以便在模型中使用所需的tokenizer。
相关问题
import torch import torch.nn as nn from torchtext.datasets import AG_NEWS from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator # 数据预处理 tokenizer = get_tokenizer('basic_english') train_iter = AG_NEWS(split='train') counter = Counter() for (label, line) in train_iter: counter.update(tokenizer(line)) vocab = build_vocab_from_iterator([counter], specials=["<unk>"]) word2idx = dict(vocab.stoi) # 设定超参数 embedding_dim = 64 hidden_dim = 128 num_epochs = 10 batch_size = 64 # 定义模型 class RNN(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim): super(RNN, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, 4) def forward(self, x): x = self.embedding(x) out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out # 初始化模型、优化器和损失函数 model = RNN(len(vocab), embedding_dim, hidden_dim) optimizer = torch.optim.Adam(model.parameters()) criterion = nn.CrossEntropyLoss() # 定义数据加载器 train_iter = AG_NEWS(split='train') train_data = [] for (label, line) in train_iter: label = torch.tensor([int(label)-1]) line = torch.tensor([word2idx[word] for word in tokenizer(line)]) train_data.append((line, label)) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) # 开始训练 for epoch in range(num_epochs): total_loss = 0.0 for input, target in train_loader: model.zero_grad() output = model(input) loss = criterion(output, target.squeeze()) loss.backward() optimizer.step() total_loss += loss.item() * input.size(0) print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))改错
在代码中出现了一个错误:缺少 `Counter` 的导入语句。需要在开头添加 `from collections import Counter`。
另外,在模型训练时,需要将模型设置为训练模式(`model.train()`),以启用 dropout 和 batch normalization 等功能。同时,还需要将输入和目标数据转移到 GPU 上进行计算,以加速训练过程。具体修改如下:
```
import torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from collections import Counter
# 数据预处理
tokenizer = get_tokenizer('basic_english')
train_iter = AG_NEWS(split='train')
counter = Counter()
for (label, line) in train_iter:
counter.update(tokenizer(line))
vocab = build_vocab_from_iterator([counter], specials=["<unk>"])
word2idx = dict(vocab.stoi)
# 设定超参数
embedding_dim = 64
hidden_dim = 128
num_epochs = 10
batch_size = 64
# 定义模型
class RNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim):
super(RNN, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim, 4)
def forward(self, x):
x = self.embedding(x)
out, _ = self.rnn(x)
out = self.fc(out[:, -1, :])
return out
# 初始化模型、优化器和损失函数
model = RNN(len(vocab), embedding_dim, hidden_dim)
optimizer = torch.optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()
# 将模型设置为训练模式
model.train()
# 将数据转移到 GPU 上
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
train_iter = AG_NEWS(split='train')
train_data = []
for (label, line) in train_iter:
label = torch.tensor([int(label)-1]).to(device)
line = torch.tensor([word2idx[word] for word in tokenizer(line)]).to(device)
train_data.append((line, label))
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)
# 开始训练
for epoch in range(num_epochs):
total_loss = 0.0
for input, target in train_loader:
optimizer.zero_grad()
output = model(input)
loss = criterion(output, target.squeeze())
loss.backward()
optimizer.step()
total_loss += loss.item() * input.size(0)
print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))
```
如果数据为一个二维数组,第一行为数据,第二行为时间,该如何去修改上述代码
如果数据为一个二维数组,第一行为数据,第二行为时间,你需要对代码进行如下修改:
```python
import torch
import torch.nn.functional as F
from transformers import GPT2Tokenizer, GPT2LMHeadModel
class ChitChat:
def __init__(self):
self.tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium')
self.model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium')
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model.to(self.device)
self.model.eval()
self.chat_history = []
def get_response(self, user_input):
input_ids = self.tokenizer.encode(user_input + self.tokenizer.eos_token, return_tensors='pt').to(self.device)
chat_history_ids = self.model.generate(
input_ids=input_ids,
max_length=1000,
pad_token_id=self.tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=10,
top_p=0.7,
temperature=0.8,
)
response = self.tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
self.chat_history.append((user_input, response))
return response
```
在修改后的代码中,`get_response` 函数的输入参数为用户的输入,第一行数据和第二行时间都不需要作为参数传递。如果你需要在 `ChitChat` 类中保存时间信息,你可以在 `chat_history` 列表中保存每次用户输入的时间戳,例如:
```python
import time
class ChitChat:
def __init__(self):
self.tokenizer = GPT2Tokenizer.from_pretrained('microsoft/DialoGPT-medium')
self.model = GPT2LMHeadModel.from_pretrained('microsoft/DialoGPT-medium')
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model.to(self.device)
self.model.eval()
self.chat_history = []
def get_response(self, user_input):
input_ids = self.tokenizer.encode(user_input + self.tokenizer.eos_token, return_tensors='pt').to(self.device)
chat_history_ids = self.model.generate(
input_ids=input_ids,
max_length=1000,
pad_token_id=self.tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=10,
top_p=0.7,
temperature=0.8,
)
response = self.tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
self.chat_history.append((user_input, time.time(), response))
return response
```
在上述代码中,`chat_history` 列表中每个元素包含三个值:用户输入、时间戳和机器人回复。
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