⭐️ NLP Algorithms with transformers lib. Supporting Text-Classification, Text-Generation, Information-Extraction, Text-Matching, RLHF, SFT etc.

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN

自然语言处理（nlp），小姜机器人（闲聊检索式chatbot），BERT句向量-相似度（Sentence Similarity），XLNET句向量-相似度（text xlnet embedding），文本分类（Text classification）， 实体提取（ner，bert+bilstm+crf），数据增强（text augment, data enhance），同义句同义词生成，句子主干提取（mainpart），中文汉语短文本相似度，文本特征工程，keras-http-service调用

🎯🗯 Dataset generation for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL!

Tools and recipes to train deep learning models and build services for NLP tasks such as text classification, semantic search ranking and recall fetching, cross-lingual information retrieval, and question answering etc.

Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

OpenTextClassification is all you need for text classification! Open text classification for everyone, enjoy your NLP journey! 这可能是目前为止最全面的开源文本分类项目，支持中英双语、多种模型、多种任务。

The project surveys 16+ Natural Language Processing (NLP) research papers that propose novel Deep Neural Network Models for Text Classification, based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). It also implements each of the models using Tensorflow and Keras.

🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097

This repo is for the Linkedin Learning course: Transformers: Text Classification for NLP using BERT

Oxford Deep NLP 2017 course - Practical 2: Text Classification

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Oxford Deep NLP 2017 course - Practical 3: Text Classification with RNNs

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, code generation, and more...

This is a simple web-based application that demonstrates an NLP (Natural Language Processing) Pipeline for text classification.

some basic deep learning models/method for nlp, text classification.

🤖💬 Tiny experimental NLP deep learning library for text classification and NER. Built with Tensorflow.js, Keras and Chatito. Implemented in JS and Python.

NLP related tasks, including text classification, sequence annotation, text relations, machine translation and other tasks.

天池-Datawhale 零基础入门NLP-新闻文本分类 最终榜Top10分享

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, code generation, and much more...

nlp text classification task with bert and pytorch on IMDB dataset

Online Inference API for NLP Transformer models - summarization, text classification, sentiment analysis and more

No description available

Basic NLP Toolkits include text classification, sequence labeling etc.

:mortar_board:RESEARCH [NLP] Analysis of N-gram Graphs and their applications in the domain of Text Classification and Extraction based Summarization

Focuses on detecting spam messages in SMS text using Natural Language Processing (NLP) and Machine Learning techniques. It leverages text preprocessing, feature extraction, and classification algorithms to accurately predict whether a message is Spam or Ham (Not Spam).

ESG-Risk-Analysis-with-NLP utilizes Natural Language Processing to assess Environmental, Social, and Governance risks from text data. It automates risk classification, generates synthetic datasets, computes ESG scores, and exports analysis to Excel.

Cyrillic Mongolian text classification with tensorflow 2, and also some fine-tuning on TugsTugi's Mongolian BERT model and other NLP experiments are included.

This repository offers NLP resources & tutorials using keras/tensorflow. Designed for University of Salford's NLP module students, but open for all to learn NLP topics like text classification, language modeling, and sentiment analysis.