BERT Sequence Classification Base - AG News (bert_base_sequence_classifier_ag_news)

Description

BERT Model with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.

bert_base_sequence_classifier_dbpedia_14 is a fine-tuned BERT model that is ready to be used for Sequence Classification tasks such as sentiment analysis or multi-class text classification and it achieves state-of-the-art performance.

We used TFBertForSequenceClassification to train this model and used BertForSequenceClassification annotator in Spark NLP 🚀 for prediction at scale!

Predicted Entities

Business, Sci/Tech, Sports, World

Download Copy S3 URI

How to use

document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

sequenceClassifier = BertForSequenceClassification \
.pretrained('bert_base_sequence_classifier_ag_news', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setCaseSensitive(True) \
.setMaxSentenceLength(512)

pipeline = Pipeline(stages=[
document_assembler, 
tokenizer,
sequenceClassifier    
])

example = spark.createDataFrame([['Disney Comics was a comic book publishing company operated by The Walt Disney Company which ran from 1990 to 1993.']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler() 
.setInputCol("text") 
.setOutputCol("document")

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

val tokenClassifier = BertForSequenceClassification.pretrained("bert_base_sequence_classifier_ag_news", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setCaseSensitive(true)
.setMaxSentenceLength(512)

val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))

val example = Seq("Disney Comics was a comic book publishing company operated by The Walt Disney Company which ran from 1990 to 1993.").toDS.toDF("text")

import nlu
nlu.load("en.classify.bert_sequence.ag_news").predict("""Disney Comics was a comic book publishing company operated by The Walt Disney Company which ran from 1990 to 1993.""")

Model Information

Model Name: bert_base_sequence_classifier_ag_news
Compatibility: Spark NLP 3.3.2+
License: Open Source
Edition: Official
Input Labels: [token, document]
Output Labels: [class]
Language: en
Case sensitive: true
Max sentense length: 512

Data Source

https://huggingface.co/datasets/ag_news

Benchmarking

precision    recall  f1-score   support

Business       0.89      0.92      0.90      1852
Sci/Tech       0.92      0.90      0.91      1963
Sports       0.99      0.98      0.98      1922
World       0.94      0.95      0.95      1863

accuracy                           0.94      7600
macro avg       0.94      0.94      0.94      7600
weighted avg       0.94      0.94      0.94      7600