Description
Pretrained BERT Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. PHS-BERT is an English model and trained to identify the tasks related to public health surveillance (PHS) on social media.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_phs_bert","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["No place in my city has shelter space for us, and I won't put my baby on the literal street. What cities have good shelter programs for homeless mothers and children?"]]).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_phs_bert","en")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("No place in my city has shelter space for us, and I won't put my baby on the literal street. What cities have good shelter programs for homeless mothers and children?").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.embed.bert.phs").predict("""No place in my city has shelter space for us, and I won't put my baby on the literal street. What cities have good shelter programs for homeless mothers and children?""")
Model Information
| Model Name: | bert_embeddings_phs_bert |
| Compatibility: | Spark NLP 4.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | en |
| Size: | 1.3 GB |
| Case sensitive: | false |
References
https://arxiv.org/abs/2204.04521