XLNet Embeddings (Base)

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”

Live Demo Open in Colab Download

How to use

...
embeddings = XlnetEmbeddings.pretrained("xlnet_base_cased", "en") \
      .setInputCols("sentence", "token") \
      .setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame(pd.DataFrame({"text": ["I love NLP"]})))

...
val embeddings = XlnetEmbeddings.pretrained("xlnet_base_cased", "en")
      .setInputCols("sentence", "token")
      .setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val result = pipeline.fit(Seq.empty["I love NLP"].toDS.toDF("text")).transform(data)

import nlu

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

Results

        token	en_embed_xlnet_base_cased_embeddings
	
	I	[0.0027268705889582634, -3.5811028480529785, 0...
	love	[-4.020033836364746, -2.2760159969329834, 0.88...
	NLP	[-0.2549888491630554, -2.2768502235412598, 1.1...

Model Information

Data Source

The model is imported from https://github.com/zihangdai/xlnet