ALBERT Embeddings (Base Uncase)


ALBERT is “A Lite” version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper “ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.


How to use

embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en") \
      .setInputCols("sentence", "token") \
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
pipeline_model =[[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame(pd.DataFrame({"text": ["I love NLP"]})))
val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased", "en")
      .setInputCols("sentence", "token")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val result =["I love NLP"].toDS.toDF("text")).transform(data)
import nlu

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


        token	en_embed_albert_base_uncased_embeddings
	I	[1.0153148174285889, 0.5481745600700378, -0.44...
	love	[0.3452114760875702, -1.191628336906433, 0.423...
	NLP	[-0.4268064796924591, -0.3819553852081299, 0.8...

Model Information

Model Name: albert_base_uncased
Type: embeddings
Compatibility: Spark NLP 2.5.0+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [word_embeddings]
Language: [en]
Dimension: 768
Case sensitive: false

Data Source

The model is imported from