Abstract: A battery pack, a vehicle, and an energy storage device are provided. The battery pack includes a cell array and a support member, where the cell array includes a plurality of cells, the cell has a first dimension, and the first dimension is a maximum spacing between two imaginary parallel planes sandwiching the cell; and at least one of the cells 600 mm?first dimension?2500 mm the at least one cell includes a casing and a core located inside the casing, a supporting region is formed on the casing, and the cell is connected to the support member through the supporting region and is supported by the support member. The support member is connected to the supporting region to support the cell.

Abstract: A power battery pack includes: a pack body; a plurality of cells, disposed in the pack body; the cell having a length L0, a width H0, and a thickness D0, where at least one cell meets: L0>H0?D0, a length direction of the cell is arranged along a width direction of a vehicle body of the electric vehicle, and in the width direction of the electric vehicle, the length L0 of the cell and a size W of the vehicle body of the electric vehicle in the width direction meet: 46%?L0/W?76%; or at least one cell meets: L0>H0?D0, a length direction of the cell is arranged along a length direction of a vehicle body of the electric vehicle, and in the length direction of the electric vehicle, the length L0 of the cell and a size X of the vehicle body of the electric vehicle in the length direction meet: 40%?L0/W?76%.

Abstract: A system to calculate a reconfigured confidence score is configured to receive a text, a plurality of labels, and a plurality of confidence scores from a plurality of models and assign a weightage to the inputs received from the plurality of models. The system is configured to select a first text with a first label and retrieve a second text, a third text, and a second label. The system is further configured to generate a first, second and third output confidence score for the first text, second text and third text, and corresponding labels. The system compares the plurality of output confidence scores and generates an output which comprises of the first text, the first label, and a final confidence score, wherein the final confidence score is one among the first, second and third output confidence scores.

Abstract: System for optimizing training dataset comprising sample documents. The system comprises one or more processors configured to create graph embedding vector for each of the sample documents of the training dataset and cluster the graph embedding vectors of the sample documents of the training dataset into clusters based on the similarity between the graph embedding vectors. Further, the processor is configured to select a first set of training data, using an optimization model, wherein the first set of training data comprises a finite number of graph embedding vectors of the sample documents from the clustered training dataset. Finally, the first set of training data is fed as for a machine learning model.

Abstract: System for graph-based clustering of documents. The system comprises one or more processors configured to receive a digital copy of a document to convert the document into a graph object. Further, the processor is configured to identify and label entities in the document, wherein each of the entities is represented as a node of the graph object. Further, the processor is configured to create the graph object for the received digital copy of the document and generate a graph embedding vector using a graph embedding neural network trained to receive the graph object as input and generate the graph embedding vector for the graph object as output. Finally, the processor is configured to cluster the graph embedding vector to a cluster comprising similar looking templates of the document.

Abstract: A system for template invariant information extraction. The system comprises of processor, a first neural network model and a second neural network model. The processor is configured to recognize and extract entities and location of the entities in the input document using the first neural network model. The processor is further configured to classify whether the input document belongs to at least a template of the documents of the first training dataset using the second neural network model. The second neural network model comprises a linear classifier configured to generate a plurality of confidence scores for the input document corresponding to a unique template of the documents of the first training dataset. A threshold value to classify the input document belonging to template of the documents of the first training dataset is determined, Classification is done by comparing the confidence score with the threshold value.