English BertForSequenceClassification Cased model (from nbroad)

Description

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

Predicted Entities

Waste_And_Hazardous_Materials_Management, Management_Of_Legal_And_Regulatory_Framework, Air_Quality, GHG_Emissions, Business_Model_Resilience, Water_And_Wastewater_Management, Systemic_Risk_Management, Director_Removal, Data_Security, Employee_Engagement_Inclusion_And_Diversity, Access_And_Affordability, Competitive_Behavior, Ecological_Impacts, Employee_Health_And_Safety, Supply_Chain_Management, Critical_Incident_Risk_Management, Business_Ethics, Product_Design_And_Lifecycle_Management, Energy_Management, Labor_Practices, Physical_Impacts_Of_Climate_Change, Product_Quality_And_Safety, Human_Rights_And_Community_Relations, Customer_Welfare, Customer_Privacy

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

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

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

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

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

val documentAssembler = new DocumentAssembler()
      .setInputCols(Array("text"))
      .setOutputCols(Array("document"))

val tokenizer = new Tokenizer()
    .setInputCols("document")
    .setOutputCol("token")

val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_esg","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("class")

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

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

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

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

Model Information

References

References

• https://huggingface.co/nbroad/ESG-BERT
• https://github.com/mukut03/ESG-BERT