English BertForSequenceClassification Cased model (from slowturtle)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. topic_v5 is a English model originally trained by slowturtle.

Predicted Entities

bottlenecks, responsability, srcs, justify, agree, procedures, world, experience, planning, jobrole, efficiency, ergonomics, suggestion, motivation_commitment, shame, impact, career, timesheet, transport, autonomy, collaboration, personal, burnout_stress, integration, employee, relationship, mental_health, learning, proud, delivery, communication, bureaucracy, support, ethics, turnover, changes, lifebalance, rwxp, fear, workload, environment, improvement, pandemics, allgood, diversity, questioning_criticism, clients, salary, safety, performance, health, clarity, growth, behaviour, product, recognition, challenges, skills, facilities, respect, routine, benefits, leadership, training, culture_values, feedback, disagree, attrition

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_topic_v5","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_topic_v5","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.by_slowturtle").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/slowturtle/topic_v5