xlnet-base-cased

Maintainer: xlnet

Total Score

66

Last updated 5/23/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The xlnet-base-cased model is a transformer-based language model pre-trained on English text. It was introduced in the paper "XLNet: Generalized Autoregressive Pretraining for Language Understanding" and developed by the XLNet team. The model uses a novel generalized autoregressive pretraining objective, which allows it to achieve state-of-the-art results on various downstream language tasks.

Compared to similar models like xlm-roberta-base and gpt2-xl, the xlnet-base-cased model has unique capabilities and characteristics. While XLM-RoBERTa is a multilingual model pre-trained on 100 languages, XLNet is focused specifically on English. Additionally, XLNet uses a different pretraining objective compared to the masked language modeling objective used by BERT and XLM-RoBERTa.

Model inputs and outputs

Inputs

  • Text sequences: The model takes as input text sequences of up to 1024 tokens.

Outputs

  • Last hidden states: The model outputs the last hidden states of the input sequence, which can be used as features for downstream tasks.
  • Logits: The model can also output logits, which can be used for tasks like text classification.

Capabilities

The xlnet-base-cased model has shown strong performance on a variety of language understanding tasks, including question answering, natural language inference, sentiment analysis, and document ranking. Due to its generalized autoregressive pretraining objective, the model is able to capture long-range dependencies in text more effectively than some other transformer-based models.

What can I use it for?

The xlnet-base-cased model is primarily intended to be fine-tuned on downstream tasks. You can find fine-tuned versions of the model on the Hugging Face model hub for tasks like text classification, question answering, and more. The model can be a good choice for applications that require understanding long-range dependencies in text, such as document ranking or long-form question answering.

Things to try

One interesting thing to try with the xlnet-base-cased model is to compare its performance to other transformer-based models like BERT and XLM-RoBERTa on the same downstream tasks. This can give you a sense of the unique capabilities of the XLNet approach and how it compares to other popular language models.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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