Smaller BERT Embeddings (L-2_H-256_A-4)

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_256", "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 = BertEmbeddings.pretrained("small_bert_L2_256", "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.bert.small_L2_256').predict(text, output_level='token')
embeddings_df

Results

	token	en_embed_bert_small_L2_256_embeddings
		
	I	[-1.712011456489563, -1.076645851135254, 0.697...
      love 	[-1.1276499032974243, -0.9930340647697449, 1.5...
      NLP 	[-0.3206934928894043, 0.03202249854803085, 1.4...

Model Information

Model Name: small_bert_L2_256
Type: embeddings
Compatibility: Spark NLP 2.6.0+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [word_embeddings]
Language: [en]
Dimension: 256
Case sensitive: false

Data Source

The model is imported from https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-256_A-4/1