A text is segmented based on a theory of discourse analysis into at least a main discourse constituent containing spatio-temporal information about a single event in a possible world view. The discourse constituents are then inserted into a structural representation of discourse. Non-structural ...
Text analysis has undergone substantial evolution since its inception, moving from manual qualitative assessments to sophisticated quantitative and computational methods. Beginning in the late twentieth century, a surge in the utilization of computationa
比如GPT2、3中(GPT3更加清晰地认识到了这点),当我们输入带有指令的文本,以文本摘要为例“summarization:”接在我们要进行文本摘要的文本前面,GPT就会输出生成文本摘要,无独有偶T5将类似的方式用于微调步骤(finetune),虽然本意可能是为了将各种NLP任务(包括文本分类,情感分析,文本摘要等)统一成text2text任务,但是也得...
For example, consider the task of text summarization, where the input is a body of text, and the task of the model is to paraphrase the original text. Intuitively, the model should be able to “attend” to specific parts of the text and create smaller “summaries” that effectively ...
An 800GB Dataset of Diverse Text for Language Modeling Meta Search: Google Dataset Search OpenLegalData Belgium: Belgian Statutory Article Retrieval Dataset (BSARD), including code German NLP Resource: Awesome German NLP Legal Entity Recognition Legal Text Summarization Legal Text Translation Legal Docum...
Robust fine-tuning of zero-shot models 2022 CVPR Model Merging in Multi-Task/Multi-Objective/Multi-Domain/Auxiliary Learning Model Merging for Knowledge Transfer in Multi-Task Learning Paper TitleYearConference/JournalRemark Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalanc...
Here are some types of text analysis that a GPT-4 model could potentially handle: Sentiment analysis: Determine the sentiment of a given text, whether it’s positive, negative, or neutral, and potentially provide a sentiment score. Text summarization: Summarize long pieces of text into shorter,...
During the last two decades, automatic evaluation has been significant for NLP tasks because it is necessary to measure the scope of NLP systems in terms of robustness and efficacy. In the context of Automatic Text Summarization (ATS), proposed methods are evaluated depending on how well they ca...
Scripts for fine-tuning Llama2 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization & question answering. Supporting a number of candid inference solutions such as
United States Patent US8583374 Note: If you have problems viewing the PDF, please make sure you have the latest version ofAdobe Acrobat. Back to full text