ALBERT Sequence Classification Base - IMDB (albert_base_sequence_classifier_imdb)

Description

ALBERT 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.

albert_base_sequence_classifier_imdb is a fine-tuned ALBERT 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 TFAlbertForSequenceClassification to train this model and used AlbertForSequenceClassification annotator in Spark NLP 🚀 for prediction at scale!

Predicted Entities

neg, pos

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

sequenceClassifier = AlbertForSequenceClassification \
.pretrained('albert_base_sequence_classifier_imdb', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setCaseSensitive(False) \
.setMaxSentenceLength(512)

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

example = spark.createDataFrame([['I really liked that movie!']]).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 = AlbertForSequenceClassification.pretrained("albert_base_sequence_classifier_imdb", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setCaseSensitive(false)
.setMaxSentenceLength(512)

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

val example = Seq("I really liked that movie!").toDS.toDF("text")

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

import nlu
nlu.load("en.classify.albert.imdb").predict("""I really liked that movie!""")

Results

* +--------------------+
* |result              |
* +--------------------+
* |[neg, neg]          |
* |[pos, pos, pos, pos]|
* +--------------------+

Model Information

Data Source

https://huggingface.co/datasets/imdb

Benchmarking

{
	"eval_loss": 0.19857540726661682,
	"eval_accuracy": 0.94604,
	"eval_f1": 0.94604,
	"eval_precision": 0.94604,
	"eval_recall": 0.94604,
	"eval_runtime": 173.5385,
	"eval_samples_per_second": 144.06,
	"eval_steps_per_second": 4.506,
	"epoch": 3.0
}