token.batch_encode_plus怎么处理单个句子
时间: 2023-07-31 18:10:16 浏览: 50
如果你想使用`token.batch_encode_plus`处理单个句子,你可以将该句子作为一个列表中的唯一元素传递给该函数。例如,假设你有一个句子 `text = "Hello world!"`,你可以使用以下代码将其编码为模型可以处理的格式:
```
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("model_name")
text = "Hello world!"
encoded_input = tokenizer.batch_encode_plus([text], padding=True, truncation=True, return_tensors="pt")
```
在这个例子中,我们将文本作为一个列表中的唯一元素传递给了`batch_encode_plus`函数。我们还设置了`padding`和`truncation`参数来确保文本被正确地处理。最后,我们指定了`return_tensors="pt"`,以便将输出转换为PyTorch张量。
相关问题
token.texts_to_sequences
`token.texts_to_sequences` is a method in the Keras Tokenizer class that converts a list of texts into a list of sequences (i.e., lists of integers). Each integer represents a word in the text, and the list of integers represents the sequence of words in the text. The method takes in a list of texts as its argument and returns a list of sequences.
For example, suppose we have a list of text documents:
```
texts = [
"the cat in the hat",
"the dog chased the cat",
"the cat ran away from the dog"
]
```
We can use the Tokenizer class to tokenize these texts and convert them into sequences:
```python
from keras.preprocessing.text import Tokenizer
# create tokenizer object
token = Tokenizer()
# fit tokenizer on the texts
token.fit_on_texts(texts)
# convert texts to sequences
sequences = token.texts_to_sequences(texts)
print(sequences)
```
This will output:
```
[
[1, 2, 3, 4],
[1, 5, 6, 1, 2],
[1, 2, 7, 8, 9, 1, 5]
]
```
In this example, the word "the" is assigned the integer value 1, "cat" is assigned 2, "in" is assigned 3, and so on. The first sequence ([1, 2, 3, 4]) corresponds to the first text ("the cat in the hat"), where "the" is the first word, "cat" is the second word, and so on.
async def check_connect(office_site_id: str, end_user_id: str, password: str, desktop_type: str = None, desktop_group_id: str = None, desktop_id: str = None): """ 验证桌面连接 """ from fvtdesktop.ad_user_api import AdUserApi client_id = uuid.uuid4().hex token = await AdUserApi.get_login_token(client_id=client_id, office_site_id=office_site_id, end_user_id=end_user_id, password=password, current_stage=get_current_stage()) if desktop_type == 'Desktopgroup': describe = await AdUserApi.describe_desktops(login_token=token.login_token, protocol_type='BOTH', client_id=client_id, session_id=token.session_id, office_site_id=office_site_id) desktop = list(filter(lambda x: 'desktop_group_id' in x.keys() and x['desktop_group_id'] == desktop_group_id, describe)) result = await get_ticket(desktop_id=desktop[0].desktop_id, client_id=client_id, login_token=token.login_token, session_id=token.session_id) if result == 'Again': await sleep(30) result = await get_ticket(desktop_id=desktop[0].desktop_id, client_id=client_id, login_token=token.login_token, session_id=token.session_id) assert result == 'True' else: result = await get_ticket(desktop_id=desktop_id, client_id=client_id, login_token=token.login_token, session_id=token.session_id) if result == 'Again': await sleep(30) result = await get_ticket(desktop_id=desktop_id, client_id=client_id, login_token=token.login_token, session_id=token.session_id) assert result == 'True'
这是一段 Python 代码,它定义了一个名为 "check_connect" 的异步函数(async function)。该函数的目的是验证桌面连接,它接受以下参数:office_site_id(办公地点 ID)、end_user_id(终端用户 ID)、password(密码)、desktop_type(桌面类型,默认为 None)、desktop_group_id(桌面组 ID,默认为 None)和 desktop_id(桌面 ID,默认为 None)。
函数通过调用 "AdUserApi" 中的 "get_login_token" 方法来获取登录 token。如果 "desktop_type" 等于 "Desktopgroup",则调用 "describe_desktops" 方法,获取桌面的详细信息,并过滤出 "desktop_group_id" 等于 "desktop_group_id" 参数的桌面。然后,调用 "get_ticket" 方法来获取桌面连接的票据。如果返回值为 "Again",则等待 30 秒后再次尝试获取票据。最后,使用 "assert" 语句来断言结果是否为 "True"。
如果 "desktop_type" 不等于 "Desktopgroup",则直接调用 "get_ticket" 方法来获取桌面连接的票据,并执行与上述相同的操作。