English BertForSequenceClassification Base Cased model (from dipesh)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. Intent-Classification-Bert-Base-Cased is a English model originally trained by dipesh.

Predicted Entities

tell me joke, take screenshot, asking date, asking time, play games, check internet speed, send email, send whatsapp message, tell me about, asking weather, covid cases, download youtube video, goodbye, tell me news, open website, click photo, play on youtube, greet

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_intent_classification_base_cased","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_intent_classification_base_cased","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.bert.cased_base.by_dipesh").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: bert_classifier_intent_classification_base_cased
Compatibility: Spark NLP 4.2.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [class]
Language: en
Size: 406.5 MB
Case sensitive: true
Max sentence length: 256

References

  • https://huggingface.co/dipesh/Intent-Classification-Bert-Base-Cased