all kinds of text classification models and more with deep learning
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers.
高质量中文预训练模型集合：最先进大模型、最快小模型、相似度专门模型
About Muti-Label Text Classification Based on Neural Network.
Opensource Korean chatbot framework
Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.
multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.
A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI 
Sentence Classifications with Neural Networks
The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction"
😉 🌀 🍓 TextRank implementation in Golang with extendable features (summarization, phrase extraction) and multithreading (goroutine).
PubMed 200k RCT dataset: a large dataset for sequential sentence classification.
Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.
top 1% solution to toxic comment classification challenge on Kaggle.
About Text Pairs (Sentence Level) Classification (Similarity Modeling) Based on Neural Network.
Implementation of Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014) on Pytorch.
Extracting scientific claims from biomedical abstracts (powered by AllenNLP)
CNN for intent classification task in a Chatbot
Semantic Segmentation of Legal texts that labels sentences with one of 7 rhetorical roles.
LSTM-based Models for Sentence Classification in PyTorch
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