XLNet is a pretrained transformer-based language model developed for Natural Language Processing (NLP). It improves upon traditional models by learning bidirectional context using a generalized autoregressive method. XLNet is designed to provide state-of-the-art performance on a variety of NLP tasks, making it suitable for both research and production environments.

Features of XLNet:

• Pretrained on large-scale datasets for strong language understanding.
• Supports text classification tasks such as sentiment analysis and spam detection.
• Can perform question answering by understanding context in passages.
• Enables text summarization and content generation with high accuracy.
• Handles named entity recognition (NER) to identify important entities in text.
• Can be fine-tuned for custom NLP tasks using Hugging Face Transformers library.
• Supports integration with Python APIs for easy deployment in applications.
• State-of-the-art performance on benchmarks like GLUE, SQuAD, and text classification datasets.

To Check the Version of Transformers (which includes XLNet) :
$ source /opt/xlnet-env/bin/activate
$ pip show transformers

Disclaimer: XLNet is an open-source software provided via the Hugging Face Transformers library. It is not affiliated with, endorsed by, or sponsored by any other company. XLNet is provided "as is," without any warranty, express or implied. Users utilize this software at their own risk. The developers and contributors are not responsible for any damages, losses, or consequences resulting from the use of this software. Users are encouraged to review and comply with licensing terms and any applicable regulations when using XLNet.