Smaller BERT Embeddings (L-2_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 = BertEmbeddings.pretrained("small_bert_L2_128", "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([['I love NLP']], ["text"]))

...
val embeddings = BertEmbeddings.pretrained("small_bert_L2_128", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val data = Seq("I love NLP").toDF("text")
val result = pipeline.fit(data).transform(data)

import nlu

text = ["I love NLP"]
embeddings_df = nlu.load('en.embed.bert.small_L2_128').predict(text, output_level='token')
embeddings_df

Results

	token	en_embed_bert_small_L2_128_embeddings
		
	I	[-1.2788691520690918,  -0.011364400386810303, 0....
	love	[-1.4087588787078857,  -0.348095178604126, -0....
	NLP	[-1.6277656555175781,  -0.28823617100715637, ...

Model Information

Data Source

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