English BertForSequenceClassification Cased model (from ismaelardo)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. BETO_4d is a English model originally trained by ismaelardo.

Predicted Entities

9411, 2433, 8322, 1323, 5414, 9412, 2413, 3343, 1212, 2522, 9621, 4321, 2242, 4225, 7212, 3331, 5249, 8344, 2351, 2431, 3411, 2411, 1330, 3322, 1345, 7127, 8332, 5223, 5242, 9333, 2221, 3511, 4416, 2141, 3251, 2161, 4226, 3344, 5230, 1324, 3111, 1219, 3311, 3257, 2423, 3512, 2519, 4323, 9112, 2143, 2310, 3321, 5244, 2635, 4110, 2421, 7412, 3118, 5222, 8343, 1221, 3122, 2521, 3115, 2330, 2529, 3313, 1211, 3112, 3611, 2341, 3113, 2243, 2513, 8321, 2342, 3323, 2145, 2151, 7233, 2512, 4214, 3221, 2424, 2166, 4222, 3432, 2642, 2144, 1412, 2511, 5120, 9334, 7231, 4211, 9321, 2142, 3142, 2634, 3312, 3114, 4311, 1420, 3334

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
        .setInputCol("text") \
        .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")

sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_beto_4d","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer,sequenceClassifier_loaded])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).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 sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_beto_4d","en") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer,sequenceClassifier_loaded))

val data = Seq("PUT YOUR STRING HERE").toDF("text")

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("en.classify.beto_bert").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/ismaelardo/BETO_4d