uncased

简明释义

英[ʌnˈkeɪst]美[ʌnˈkeɪst]

adj. 外露的；无套管的；未罩外壳的

v. 从盒子中取出；露出（uncase 的过去分词形式）

英英释义

单词用法

同义词

反义词

例句

1.Put the instrument into the uncased hole.

将仪器放进裸眼井中。

2.But these were uncased auger-drilled poured pilings, with the grout in direct contact with the native materials.

不过这些都是露出地面经钻孔绕灌而成的桩，灰浆同天然材料直接接触。

3.But these were uncased auger-drilled poured pilings, with the grout in direct contact with the native materials.

不过这些都是露出地面经钻孔绕灌而成的桩，灰浆同天然材料直接接触。

4.Using an uncased 未区分大小写的 tokenizer can simplify the preprocessing step.

使用未区分大小写的分词器可以简化预处理步骤。

5.When processing the text, make sure to convert it to uncased 未区分大小写的 format.

处理文本时，请确保将其转换为未区分大小写的格式。

6.The search algorithm is designed to handle uncased 未区分大小写的 queries efficiently.

该搜索算法旨在高效处理未区分大小写的查询。

7.The model performs better when trained on uncased 未区分大小写的 text data.

当模型在未区分大小写的文本数据上训练时，其表现更好。

8.For this task, you should use an uncased 未区分大小写的 version of the dataset.

对于这个任务，您应该使用数据集的未区分大小写的版本。

作文

In the realm of natural language processing (NLP), the term uncased refers to a specific approach in text processing where the distinction between uppercase and lowercase letters is ignored. This method has gained popularity due to its ability to simplify various NLP tasks, such as sentiment analysis, text classification, and named entity recognition. By treating words like 'Apple' and 'apple' as identical, models can focus on the semantic meaning of words rather than their casing. This can be particularly beneficial in languages where case does not affect meaning significantly.One of the most notable applications of uncased models is found in transformer-based architectures like BERT (Bidirectional Encoder Representations from Transformers). BERT has both cased and uncased versions, allowing researchers and developers to choose the most suitable model for their needs. The uncased version is often preferred for general-purpose tasks because it reduces the complexity of the input data, which can lead to faster training times and improved performance in certain scenarios.Moreover, using an uncased model can help mitigate issues related to capitalization inconsistencies in datasets. For instance, when analyzing social media posts or user-generated content, the text may contain a mix of upper and lower case letters due to the informal nature of communication. An uncased approach allows the model to treat all instances of a word uniformly, thereby enhancing its ability to learn from the data without being misled by variations in casing.However, it is essential to recognize that there are situations where casing carries significant meaning. For example, in proper nouns, acronyms, or specific brand names, the distinction between uppercase and lowercase can alter the interpretation of the text. In these cases, using a cased model might be more appropriate to retain the original context and meaning.As NLP continues to evolve, the choice between cased and uncased models will depend on the specific requirements of the task at hand. Researchers must carefully consider the nature of their data and the importance of capitalization in their analyses. By understanding the implications of using an uncased approach, practitioners can make informed decisions that enhance the effectiveness of their NLP applications.In conclusion, the concept of uncased processing plays a crucial role in simplifying and optimizing various NLP tasks. While it offers advantages in terms of efficiency and consistency, it is important to remain aware of contexts where casing is essential. As we continue to explore the capabilities of machine learning and artificial intelligence in language understanding, the balance between uncased and cased approaches will remain a vital consideration for researchers and developers alike.

在自然语言处理（NLP）的领域中，术语uncased指的是一种文本处理方法，其中忽略了大写字母和小写字母之间的区别。这种方法因其能够简化各种NLP任务而受到欢迎，例如情感分析、文本分类和命名实体识别。通过将“Apple”和“apple”视为相同的词，模型可以专注于词的语义意义，而不是它们的大小写。这在某些情况下尤其有益，尤其是在那些大小写对意义影响不大的语言中。uncased模型最显著的应用之一是在基于变换器的架构中，如BERT（双向编码器表示变换器）。BERT有带有大小写和uncased版本，允许研究人员和开发者根据需要选择最合适的模型。uncased版本通常被优先选择用于通用任务，因为它减少了输入数据的复杂性，这可以导致更快的训练时间和在某些场景下的性能提升。此外，使用uncased模型可以帮助缓解数据集中与大写字母相关的不一致性问题。例如，在分析社交媒体帖子或用户生成内容时，由于交流的非正式性质，文本可能包含大小写混合的字母。uncased方法允许模型将所有实例的单词统一处理，从而增强其从数据中学习的能力，而不会被大小写变化所误导。然而，重要的是要认识到，在某些情况下，大小写具有重要意义。例如，在专有名词、缩写或特定品牌名称中，大写和小写之间的区别可能会改变文本的解释。在这些情况下，使用带有大小写的模型可能更合适，以保留原始上下文和含义。随着NLP的不断发展，在选择带有大小写和uncased模型之间的选择将取决于具体任务的要求。研究人员必须仔细考虑其数据的性质以及大小写在其分析中的重要性。通过理解使用uncased方法的影响，实践者可以做出明智的决策，从而增强其NLP应用的有效性。总之，uncased处理的概念在简化和优化各种NLP任务中扮演着至关重要的角色。虽然它在效率和一致性方面提供了优势，但重要的是要保持对大小写在特定上下文中至关重要的意识。随着我们继续探索机器学习和人工智能在语言理解中的能力，uncased和带有大小写的方法之间的平衡将仍然是研究人员和开发者需要考虑的重要问题。