Description
This model contains a deep bidirectional transformer trained on Wikipedia and the BookCorpus. The details are described in the paper “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.
Predicted Entities
How to use
... embeddings = BertEmbeddings.pretrained("bert_base_cased_quantized", "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("bert_base_cased_quantized", "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.base_cased').predict(text, output_level='token') embeddings_df Results
Results token en_embed_bert_base_cased_embeddings I[0.43879568576812744, -0.40160006284713745, 0.... love[0.21737590432167053, -0.3865768313407898, -0.... NLP[-0.16226479411125183, -0.053727392107248306, ... {:.model-param} Model Information
| Model Name: | bert_base_cased_quantized |
| Compatibility: | Spark NLP 5.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | en |
| Size: | 139.5 MB |
| Case sensitive: | true |
References
The model is imported from https://tfhub.dev/google/bert_cased_L-12_H-768_A-12/1