Description
Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. bert-for-patents
is a English model orginally trained by anferico
.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_for_patents","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("bert_embeddings_bert_for_patents","en")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("I love Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.embed.bert_for_patents").predict("""I love Spark NLP""")
Model Information
Model Name: | bert_embeddings_bert_for_patents |
Compatibility: | Spark NLP 3.4.2+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [sentence, token] |
Output Labels: | [bert] |
Language: | en |
Size: | 1.3 GB |
Case sensitive: | true |
References
- https://huggingface.co/anferico/bert-for-patents
- https://cloud.google.com/blog/products/ai-machine-learning/how-ai-improves-patent-analysis
- https://services.google.com/fh/files/blogs/bert_for_patents_white_paper.pdf
- https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md
- https://github.com/ec-jrc/Patents4IPPC
- https://picampus-school.com/
- https://ec.europa.eu/jrc/en