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”
This model is based on CMU/Google official XLNet: https://github.com/zihangdai/xlnet
How to use
embeddings = XlnetEmbeddings.pretrained("chinese_xlnet_base", "zh") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
val embeddings = XlnetEmbeddings.pretrained("chinese_xlnet_base", "zh")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
import nlu
text = ["I love NLP"]
embeddings_df = nlu.load('zh.embed.chinese_xlnet_base').predict(text, output_level='token')
embeddings_df
Model Information
Model Name: | chinese_xlnet_base |
Compatibility: | Spark NLP 3.1.2+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [token, sentence] |
Output Labels: | [embeddings] |
Language: | zh |
Case sensitive: | true |