Smaller BERT Sentence Embeddings (L-4_H-128_A-2)

Description

This is one of the smaller BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher.

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How to use

...
embeddings = BertSentenceEmbeddings.pretrained("sent_small_bert_L4_128", "en") \
.setInputCols("sentence") \
.setOutputCol("sentence_embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame([['I hate cancer', "Antibiotics aren't painkiller"]], ["text"]))

...
val embeddings = BertSentenceEmbeddings.pretrained("sent_small_bert_L4_128", "en")
.setInputCols("sentence")
.setOutputCol("sentence_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, embeddings))
val data = Seq("I hate cancer, "Antibiotics aren't painkiller").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["I hate cancer", "Antibiotics aren't painkiller"]
embeddings_df = nlu.load('en.embed_sentence.small_bert_L4_128').predict(text, output_level='sentence')
embeddings_df

Results

en_embed_sentence_small_bert_L4_128_embeddings 	      sentence
		
[-0.9171956777572632, 0.9261569976806641, 0.85... 	I hate cancer
[0.5746028423309326, 0.5926093459129333, -0.19... 	Antibiotics aren't painkiller

Model Information

Data Source

The model is imported from https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-128_A-2/1