Description
Distilbert is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40% while retaining 97% of its language understanding. It’s smaller, faster than Bert and any other Bert-based model.
Predicted Entities
sadness, joy, love, anger, fear, surprise
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
sequenceClassifier = DistilBertForSequenceClassification \
.pretrained('distilbert_sequence_classifier_emotion', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setMaxSentenceLength(512)
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
example = spark.createDataFrame([['I like you.']]).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 = DistilBertForSequenceClassification.pretrained("distilbert_sequence_classifier_emotion", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setMaxSentenceLength(512)
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val example = Seq("I like you.").toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
import nlu
nlu.load("en.classify.distilbert_sequence.emotion").predict("""I like you.""")
Model Information
| Model Name: | distilbert_sequence_classifier_emotion |
| Compatibility: | Spark NLP 3.3.3+ |
| 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/bhadresh-savani/distilbert-base-uncased-emotion
Benchmarking
{
'test_accuracy': 0.938,
'test_f1': 0.937932884041714,
'test_loss': 0.1472451239824295,
'test_mem_cpu_alloc_delta': 0,
'test_mem_cpu_peaked_delta': 0,
'test_mem_gpu_alloc_delta': 0,
'test_mem_gpu_peaked_delta': 163454464,
'test_runtime': 5.0164,
'test_samples_per_second': 398.69
}