Smaller BERT Sentence Embeddings (L-10_H-768_A-12)

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("sent_small_bert_L10_768", "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(pd.DataFrame({"text": ["I hate cancer, "Antibiotics aren't painkiller"]})))
...
val embeddings = BertEmbeddings.pretrained("sent_small_bert_L10_768", "en")
      .setInputCols("sentence")
      .setOutputCol("sentence_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, embeddings))
val result = pipeline.fit(Seq.empty["I hate cancer, "Antibiotics aren't painkiller"].toDS.toDF("text")).transform(data)
import nlu

text = ["I hate cancer", "Antibiotics aren't painkiller"]
embeddings_df = nlu.load('en.embed_sentence.small_bert_L10_768').predict(text, output_level='sentence')
embeddings_df

Results

	en_embed_sentence_small_bert_L10_768_embeddings	      sentence
		
 	[-0.6537564396858215, -0.2422734946012497, -0.... 	I hate cancer
 	[0.06436929106712341, -0.34515661001205444, 0.... 	Antibiotics aren't painkiller

Model Information

Model Name: sent_small_bert_L10_768
Type: embeddings
Compatibility: Spark NLP 2.6.0+
License: Open Source
Edition: Official
Input Labels: [sentence]
Output Labels: [sentence_embeddings]
Language: [en]
Dimension: 768
Case sensitive: false

Data Source

The model is imported from https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-10_H-768_A-12/1