This work involves the usage of various NLP models to predict the winner of a
particular judgment by the means of text extraction and summarization from a
judgment document. These documents are useful when it comes to legal
proceedings. One such advantage is that these can be used for citations and
precedence reference in Lawsuits and cases which makes a strong argument for
their case by the ones using it. When it comes to precedence, it is necessary
to refer to an ample number of documents in order to collect legal points with
respect to the case. However, reviewing these documents takes a long time to
analyze due to the complex word structure and the size of the document. This
work involves the comparative study of 6 different self-attention-based
transformer models and how they perform when they are being tweaked in 4
different activation functions. These models which are trained with 200
judgement contexts and their results are being judged based on different
benchmark parameters. These models finally have a confidence level up to 99%
while predicting the judgment. This can be used to get a particular judgment
document without spending too much time searching relevant cases and reading
them completely.

本研究使用多种自然语言处理模型进行司法文书的预测和摘要，最终得出六种基于自我注意力的转换器模型在四种激活函数下的对比分析结果，准确率高达 99%。

基于 Transformer 的模型的判决文本分类比较研究

Comparative study on Judgment Text Classification for Transformer Based  Models

Deep CNN-based methods have so far achieved the state of the art results in
multi-view 3D object reconstruction. Despite the considerable progress, the two
core modules of these methods - multi-view feature extraction and fusion, are
usually investigated separately, and the object relations in different views
are rarely explored. In this paper, inspired by the recent great success in
self-attention-based Transformer models, we reformulate the multi-view 3D
reconstruction as a sequence-to-sequence prediction problem and propose a new
framework named 3D Volume Transformer (VolT) for such a task. Unlike previous
CNN-based methods using a separate design, we unify the feature extraction and
view fusion in a single Transformer network. A natural advantage of our design
lies in the exploration of view-to-view relationships using self-attention
among multiple unordered inputs. On ShapeNet - a large-scale 3D reconstruction
benchmark dataset, our method achieves a new state-of-the-art accuracy in
multi-view reconstruction with fewer parameters ($70\%$ less) than other
CNN-based methods. Experimental results also suggest the strong scaling
capability of our method. Our code will be made publicly available.

本文提出了一种名为 3D Volume Transformer（VolT）的新框架，将多视角 3D 重建转化成了序列到序列的预测问题，并使用 self-attention 来探索多个无序输入之间的视图关系。在大规模 3D 重建基准数据集 ShapeNet 上，我们的方法比其他基于 CNN 的方法使用更少的参数（减少了 70%），实现了新的多视角重建的最新精度。