设计一段html和css代码,其中至少包含三个div,分别展示三种css+引入方式？
Posted
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了设计一段html和css代码,其中至少包含三个div,分别展示三种css+引入方式？相关的知识，希望对你有一定的参考价值。
参考技术Ahtml:
<!DOCTYPE html>
<html>
<head>
<title>My webpage</title>
</head>
<body>
<div>
<h1>My first div</h1>
<p>This div uses inline CSS to style its content.</p>
<style>
h1
color: red;
p
font-size: 16px;
text-align: center;
</style>
</div>
<div>
<h1>My second div</h1>
<p>This div uses an external CSS file to style its content.</p>
<link rel="stylesheet" type="text/css" href="styles.css">
</div>
<div>
<h1>My third div</h1>
<p>This div uses an embedded style sheet to style its content.</p>
<style>
/* This is an embedded style sheet */
h1
color: blue;
p
font-size: 14px;
text-align: left;
</style>
</div>
</body>
</html>
style.css:
/* This is an external style sheet */
h1
color: green;
p
font-size: 18px;
text-align: right;
这段代码中，第一个div使用内联CSS来样式其内容，第二个div使用外部CSS文件来样式其内容，第三个div使用嵌入式样式表来样式其内容。
ValueError：您必须包含至少一个标签和至少一个序列
【中文标题】ValueError：您必须包含至少一个标签和至少一个序列【英文标题】：ValueError: You must include at least one label and at least one sequence 【发布时间】：2022-01-13 14:24:15 【问题描述】：我正在使用这个Notebook，其中Apply DocumentClassifier部分更改如下。
Jupyter 实验室，内核：conda_mxnet_latest_p37。
错误似乎是 ML 标准实践响应。但是，我传递/创建与原始代码相同的参数和变量名称。所以这与他们在我的代码中的值有关。
我的代码：
with open('filt_gri.txt', 'r') as filehandle:
tags = [current_place.rstrip() for current_place in filehandle.readlines()]
doc_classifier = TransformersDocumentClassifier(model_name_or_path="cross-encoder/nli-distilroberta-base",
task="zero-shot-classification",
labels=tags,
batch_size=16)
# convert to Document using a fieldmap for custom content fields the classification should run on
docs_to_classify = [Document.from_dict(d) for d in docs_sliding_window]
# classify using gpu, batch_size makes sure we do not run out of memory
classified_docs = doc_classifier.predict(docs_to_classify)
# let's see how it looks: there should be a classification result in the meta entry containing labels and scores.
print(classified_docs[0].to_dict())
all_docs = convert_files_to_dicts(dir_path=doc_dir)
preprocessor_sliding_window = PreProcessor(split_overlap=3,
split_length=10,
split_respect_sentence_boundary=False,
split_by='passage')
输出：
INFO - haystack.modeling.utils - Using devices: CUDA
INFO - haystack.modeling.utils - Number of GPUs: 1
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-77eb98038283> in <module>
14
15 # classify using gpu, batch_size makes sure we do not run out of memory
---> 16 classified_docs = doc_classifier.predict(docs_to_classify)
17
18 # let's see how it looks: there should be a classification result in the meta entry containing labels and scores.
~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/haystack/nodes/document_classifier/transformers.py in predict(self, documents)
137 batches = self.get_batches(texts, batch_size=self.batch_size)
138 if self.task == 'zero-shot-classification':
--> 139 batched_predictions = [self.model(batch, candidate_labels=self.labels, truncation=True) for batch in batches]
140 elif self.task == 'text-classification':
141 batched_predictions = [self.model(batch, return_all_scores=self.return_all_scores, truncation=True) for batch in batches]
~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/haystack/nodes/document_classifier/transformers.py in <listcomp>(.0)
137 batches = self.get_batches(texts, batch_size=self.batch_size)
138 if self.task == 'zero-shot-classification':
--> 139 batched_predictions = [self.model(batch, candidate_labels=self.labels, truncation=True) for batch in batches]
140 elif self.task == 'text-classification':
141 batched_predictions = [self.model(batch, return_all_scores=self.return_all_scores, truncation=True) for batch in batches]
~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/transformers/pipelines/zero_shot_classification.py in __call__(self, sequences, candidate_labels, hypothesis_template, multi_label, **kwargs)
151 sequences = [sequences]
152
--> 153 outputs = super().__call__(sequences, candidate_labels, hypothesis_template)
154 num_sequences = len(sequences)
155 candidate_labels = self._args_parser._parse_labels(candidate_labels)
~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/transformers/pipelines/base.py in __call__(self, *args, **kwargs)
758
759 def __call__(self, *args, **kwargs):
--> 760 inputs = self._parse_and_tokenize(*args, **kwargs)
761 return self._forward(inputs)
762
~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/transformers/pipelines/zero_shot_classification.py in _parse_and_tokenize(self, sequences, candidate_labels, hypothesis_template, padding, add_special_tokens, truncation, **kwargs)
92 Parse arguments and tokenize only_first so that hypothesis (label) is not truncated
93 """
---> 94 sequence_pairs = self._args_parser(sequences, candidate_labels, hypothesis_template)
95 inputs = self.tokenizer(
96 sequence_pairs,
~/anaconda3/envs/mxnet_latest_p37/lib/python3.7/site-packages/transformers/pipelines/zero_shot_classification.py in __call__(self, sequences, labels, hypothesis_template)
25 def __call__(self, sequences, labels, hypothesis_template):
26 if len(labels) == 0 or len(sequences) == 0:
---> 27 raise ValueError("You must include at least one label and at least one sequence.")
28 if hypothesis_template.format(labels[0]) == hypothesis_template:
29 raise ValueError(
ValueError: You must include at least one label and at least one sequence.
原始代码：
doc_classifier = TransformersDocumentClassifier(model_name_or_path="cross-encoder/nli-distilroberta-base",
task="zero-shot-classification",
labels=["music", "natural language processing", "history"],
batch_size=16
)
# ----------
# convert to Document using a fieldmap for custom content fields the classification should run on
docs_to_classify = [Document.from_dict(d) for d in docs_sliding_window]
# ----------
# classify using gpu, batch_size makes sure we do not run out of memory
classified_docs = doc_classifier.predict(docs_to_classify)
# ----------
# let's see how it looks: there should be a classification result in the meta entry containing labels and scores.
print(classified_docs[0].to_dict())
请让我知道是否还有其他需要添加的内容/澄清。
【问题讨论】：
【参考方案1】： 阅读官方docs，分析是调用.predict(docs_to_classify)时产生的错误建议你尝试做基本测试，比如使用参数labels = ["negative", "positive"] ，如果是字符串引起的，请纠正 外部文件的 值 并且您还应该选择检查它指示使用 管道 的位置。
pipeline = Pipeline()
pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
pipeline.add_node(component=doc_classifier, name='DocClassifier', inputs=['Retriever'])
【讨论】：
啊！我的tags 变量是空的，因为文件也是如此。不一定是您的代码，而是您的思维过程有所帮助。干杯人以上是关于设计一段html和css代码,其中至少包含三个div,分别展示三种css+引入方式？的主要内容，如果未能解决你的问题，请参考以下文章