Introduction
XLNet is an innovative method for learning language representations through unsupervised learning. It introduces a fresh approach by using a generalized permutation language modeling objective. This technique helps XLNet to better understand and predict language patterns, going beyond traditional models. By leveraging the strong foundations of Transformer-XL, a model known for its ability to handle long sequences of text, XLNet excels at tasks that involve understanding lengthy contexts.
Release Notes
XLNet was launched officially on June 19, 2019, with the release of XLNet-Large and its accompanying code. The XLNet-Base version followed shortly after on July 16, 2019.
Results
As of its initial release, XLNet has shown superior performance compared to BERT, another well-known model, on multiple language tasks. Specifically, it outperformed BERT on 20 different tasks and achieved state-of-the-art results in 18 of them.
Reading Comprehension
When tested on reading comprehension tasks like RACE and versions of SQuAD, XLNet-Large outperformed BERT-Large consistently. Notably, it achieved higher accuracy and exact match scores, reflecting its superior understanding of complex questions and contexts.
Text Classification
In tasks related to classifying and analyzing text sentiment and content (like IMDB reviews and Amazon product reviews), XLNet-Large displayed lower error rates compared to BERT-Large, indicating more accurate content classification.
GLUE Benchmark
On the GLUE benchmark, which evaluates general language understanding, XLNet once again surpassed BERT-Large in most tasks, demonstrating its versatility and capability across diverse language processing challenges.
Pre-trained Models
As of July 2019, two XLNet models were made available: XLNet-Large and XLNet-Base. These models come in a "cased" variant, meaning they consider uppercase and lowercase distinctions. Model-specific data files, like TensorFlow checkpoints and tokenization models, are included for practitioners to utilize.
Fine-tuning with XLNet
To achieve state-of-the-art results, fine-tuning involves adapting the pre-trained XLNet models to specific tasks using additional data. However, due to the large requirements for processing power and memory, fine-tuning XLNet-Large can be challenging on limited hardware like standard GPUs. Alternative methods and future updates may alleviate some of these constraints.
Text Classification/Regression
The pre-trained models can be fine-tuned for specific tasks such as sentence sentiment analysis or regression predictions. For example, scripts are provided to showcase how the model can be tailored to tasks like the Stanford Sentiment Treebank or IMDB movie reviews.
SQuAD2.0 and RACE
Scripts and guidelines are also provided for fine-tuning XLNet on reading comprehension datasets like SQuAD2.0 and RACE. Due to the complexity and length of these tasks, optimally configured setups require significant computational resources.
Custom Usage of XLNet
While existing scripts can be modified for specific tasks, an abstraction of the XLNet functionality is provided for users needing more control. This includes creating custom tactics for tokenization and text processing to best suit specific application needs.
Pretraining with XLNet
For those interested in further in-domain customization, the information on pretraining procedures is provided. This involves preparing text data and utilizing comprehensive pretraining scripts that set XLNet up for efficient learning in various text scenarios.
Through these capabilities, XLNet offers a robust framework for advancing language understanding technologies, enabling more intelligent and nuanced interactions with text data across a wide range of applications.