by xlnet
Open source · 410k downloads · 82 likes
XLNet is an advanced language model, pre-trained on English, that excels in text comprehension and generation by leveraging an innovative approach to generalized autoregressive modeling. With its Transformer-XL-based architecture, it effectively captures long-term dependencies, making it particularly strong for complex tasks such as question answering, sentiment analysis, or document ranking. Unlike other models, XLNet combines the advantages of autoregressive and autoencoding approaches, offering greater flexibility for adapting to diverse tasks. It is primarily designed to be fine-tuned for specific applications, such as sequence classification or entity extraction, rather than free-form text generation. Its efficiency and versatility make it a preferred tool for researchers and developers working on natural language processing projects.
XLNet model pre-trained on English language. It was introduced in the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Yang et al. and first released in this repository.
Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team.
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.
The model is mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2.
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import XLNetTokenizer, XLNetModel
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetModel.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
@article{DBLP:journals/corr/abs-1906-08237,
author = {Zhilin Yang and
Zihang Dai and
Yiming Yang and
Jaime G. Carbonell and
Ruslan Salakhutdinov and
Quoc V. Le},
title = {XLNet: Generalized Autoregressive Pretraining for Language Understanding},
journal = {CoRR},
volume = {abs/1906.08237},
year = {2019},
url = {http://arxiv.org/abs/1906.08237},
eprinttype = {arXiv},
eprint = {1906.08237},
timestamp = {Mon, 24 Jun 2019 17:28:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-08237.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}