XLNet Embeddings (Base Cased)


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 details are described in the paper “​XLNet: Generalized Autoregressive Pretraining for Language Understanding

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How to use

embeddings = XlnetEmbeddings.pretrained("xlnet_base_cased", "en") \
.setInputCols("sentence", "token") \
val embeddings = XlnetEmbeddings.pretrained("xlnet_base_cased", "en")
.setInputCols("sentence", "token")
import nlu

text = ["I love NLP"]
embeddings_df = nlu.load('en.embed.xlnet_base_cased').predict(text, output_level='token')

Model Information

Model Name: xlnet_base_cased
Compatibility: Spark NLP 3.1.2+
License: Open Source
Edition: Official
Input Labels: [token, sentence]
Output Labels: [embeddings]
Language: en
Case sensitive: true

Data Source