keras-xlnet VS Transformers4Rec

Compare keras-xlnet vs Transformers4Rec and see what are their differences.

keras-xlnet

Implementation of XLNet that can load pretrained checkpoints (by CyberZHG)

Transformers4Rec

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch. (by NVIDIA-Merlin)
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keras-xlnet Transformers4Rec
1 4
172 1,041
- 2.5%
0.0 5.3
over 2 years ago 17 days ago
Python Python
MIT License Apache License 2.0
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keras-xlnet

Posts with mentions or reviews of keras-xlnet. We have used some of these posts to build our list of alternatives and similar projects.

Transformers4Rec

Posts with mentions or reviews of Transformers4Rec. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-02-22.

What are some alternatives?

When comparing keras-xlnet and Transformers4Rec you can also consider the following projects:

BERT-pytorch - Google AI 2018 BERT pytorch implementation

TabFormer - Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

NeuRec - Next RecSys Library

haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

kogpt - KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)

Multimodal-Toolkit - Multimodal model for text and tabular data with HuggingFace transformers as building block for text data

Neural-Scam-Artist - Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset.

transformer-mlm - Implementation of Transformer Encoders / Masked Language Modeling Objective