In this article, we propose using deep learning and transformer architectures
combined with classical machine learning algorithms to detect and identify text
anomalies in texts. Deep learning model provides a very crucial context
information about the textual data which all textual context are converted to a
numerical representation. We used multiple machine learning methods such as
Sentence Transformers, Auto Encoders, Logistic Regression and Distance
calculation methods to predict anomalies. The method are tested on the texts
data and we used syntactic data from different source injected into the
original text as anomalies or use them as target. Different methods and
algorithm are explained in the field of outlier detection and the results of
the best technique is presented. These results suggest that our algorithm could
potentially reduce false positive rates compared with other anomaly detection
methods that we are testing.

使用深度学习和变换器架构结合经典机器学习算法以检测和识别文本中的异常，将所有文本上下文转换为数字表示。

文本数据深度学习异常检测方法

A Deep Learning Anomaly Detection Method in Textual Data

As soon as abstract mathematical computations were adapted to computation on
digital computers, the problem of efficient representation, manipulation, and
communication of the numerical values in those computations arose. Strongly
related to the problem of numerical representation is the problem of
quantization: in what manner should a set of continuous real-valued numbers be
distributed over a fixed discrete set of numbers to minimize the number of bits
required and also to maximize the accuracy of the attendant computations? This
perennial problem of quantization is particularly relevant whenever memory
and/or computational resources are severely restricted, and it has come to the
forefront in recent years due to the remarkable performance of Neural Network
models in computer vision, natural language processing, and related areas.
Moving from floating-point representations to low-precision fixed integer
values represented in four bits or less holds the potential to reduce the
memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x
to 8x are often realized in practice in these applications. Thus, it is not
surprising that quantization has emerged recently as an important and very
active sub-area of research in the efficient implementation of computations
associated with Neural Networks. In this article, we survey approaches to the
problem of quantizing the numerical values in deep Neural Network computations,
covering the advantages/disadvantages of current methods. With this survey and
its organization, we hope to have presented a useful snapshot of the current
research in quantization for Neural Networks and to have given an intelligent
organization to ease the evaluation of future research in this area.

本文旨在综述深度神经网络计算中数值量化的方法和优缺点，囊括了使用固定整数值的优点、及降低 4x 到 8x 等现实应用中的实际结果，以及占用更少的内存和降低延迟的潜力。