Description
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”
How to use
embeddings = XlnetEmbeddings.pretrained("xlnet_large_cased", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
val embeddings = XlnetEmbeddings.pretrained("xlnet_large_cased", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
import nlu
text = ["I love NLP"]
embeddings_df = nlu.load('en.embed.xlnet_large_cased').predict(text, output_level='token')
embeddings_df
Model Information
| Model Name: | xlnet_large_cased |
| Compatibility: | Spark NLP 3.1.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, sentence] |
| Output Labels: | [embeddings] |
| Language: | en |
| Case sensitive: | true |