2. Generation-driven Contrastive Self-training for Zero-shot TextClassification with Instruction-tuned GPT pdf:arxiv.org/abs/2304.1187code:github.com/RifleZhang/G 自训练的一个核心问题是伪数据的筛选构造。预测分数高的样本,准确度越高,噪声
假设llm的发展将做到人类级别的注释性能，从而彻底改变NLP中的few-shot和多任务领域。目录：数据增强 Few-shot Learning Very Large Language Models method Overall Framework Data Augmentation with ChatGPT ChatGPT的RLHF包含三个步骤 Few-shot Text Classification 评估指标 嵌入相似度 TransRate 实验结果 分类性能比较...
五、文本分类（Text Classification）文本分类是指将文本分为不同的类别，这种技术可以被用于各种领域，如情感分析、垃圾邮件过滤、文本归档等。我可以使用各种文本分类算法来对文本进行分类，如朴素贝叶斯、支持向量机、神经网络等。六、文本摘要（Text Summarization）文本摘要是指将长篇文章或文本自动转换成简短的摘要，这...
zip -d /home/aistudio/data # 解压清洗后的数据集和模型参数文文件 %pip install wordcloud # 词云 import numpy as np import pandas as pd from tqdm import tqdm import paddle from paddlenlp.transformers import AutoModelForSequenceClassification, AutoTokenizer from paddle.io import Dataset, DataLoader ...
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs Prompt Engineering for Healthcare: Methodologies and Applications Differentiate ChatGPT-generated and Human-written Medical Texts ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT ...
The study employed both chi-square tests and logistic regression analysis to examine the relationship between rater groups (human vs. machine) and the accuracy of text classification. Initial chi-square analyses suggested no significant differences in classification accuracy between human and AI raters....
前面说过,GPT-1 适配的下游任务有自然语言推断 NLI(natural language inference),问答QA(question answer),语义匹配(semantic similarity),文本分类(text classification)。 下游任务适配的过程分两步:1、根据任务定义不同输入,2、对不同任务增加不同的分类层。 具体定义可以参见下图: 随着训练次数的增加,GPT-1的性能...
Word embedding is a technique that is used to represent words in a numerical format that can be fed into a machine learning model. It allows the model to understand the relationships between words in a text, and it can be useful for tasks such as text classification and document similarity....
前面说过,GPT-1 适配的下游任务有自然语言推断 NLI(natural language inference),问答QA(question answer),语义匹配(semantic similarity),文本分类(text classification)。下游任务适配的过程分两步:1、根据任务定义不同输入,2、对不同任务增加不同的分类层。具体定义可以参见下图: ...
Prompt-learning最早来自于论文《Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference》https://arxiv.org/abs/2001.07676,当时把里面的范式简称为PET(Pattern-exploiting Training)。其核心思想为将不同类型的自然语言理解任务与BERT预训练中的掩码语言模型任务进行转化靠拢。例如对于...