xlnet-pytorch

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

Simple XLNet implementation with Pytorch Wrapper

An implementation of Google Brain's 2019 XLNet in PyTorch

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