🏡 Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.

Simple XLNet implementation with Pytorch Wrapper

This shows how to fine-tune Bert language model and use PyTorch-transformers for text classififcation

疫情期间网民情绪识别代码，包含lstm，bert，xlnet，robert，最高f1为0.725 部署在Google colab

Determine the polarity of amazon fine food reviews using ULMFiT, BERT, XLNet and RoBERTa

PyTorch implementation of Deep-Learning Architectures

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based method of learning language representations. It is a bidirectional transformer pre-trained model developed using a combination of two tasks namely: masked language modeling objective and next sentence prediction on a large corpus.

This GitHub repository presents our solution to Touché 2023 Task 4: Human Value Detection, a multilabel text classification task. We fine-tuned transformer architectures like BERT, RoBERTa, and XLNet to classify whether or not a given argument draws on a human value category.

This is a document's contextual similarity evaluation tool. It captures the contextual meaning of a document and compares it with other document's contextual meaning stored in its dataset.

R&D for datasets for book genres

Identifying complaints on social media using transformer-XL based XLNet model