Description
Classify IMDB reviews in negative and positive categories using Universal Sentence Encoder.
Predicted Entities
neg, pos
Live Demo Open in Colab Download Copy S3 URI
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained('tfhub_use', lang="en") \
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
classifier = SentimentDLModel().pretrained('sentimentdl_use_imdb')\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("sentiment")
nlp_pipeline = Pipeline(stages=[document_assembler,
use,
classifier
])
l_model = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
annotations = l_model.fullAnnotate('Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!')
import nlu
nlu.load("en.sentiment.imdb.use.dl").predict("""Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music was rad! Horror and sword fight freaks,buy this movie now!""")
Results
| | document | sentiment |
|---:|---------------------------------------------------------------------------------------------------------:|--------------:|
| | Demonicus is a movie turned into a video game! I just love the story and the things that goes on in the | |
| 0 | film.It is a B-film ofcourse but that doesn`t bother one bit because its made just right and the music | positive |
| | was rad! Horror and sword fight freaks,buy this movie now! | |
Model Information
| Model Name: | sentimentdl_use_imdb |
| Compatibility: | Spark NLP 2.7.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [sentiment] |
| Language: | en |
| Dependencies: | tfhub_use |
Data Source
This model is trained on data from https://ai.stanford.edu/~amaas/data/sentiment/
Benchmarking
precision recall f1-score support
neg 0.88 0.82 0.85 12500
pos 0.84 0.88 0.86 12500
accuracy 0.85 25000
macro avg 0.86 0.86 0.85 25000
weighted avg 0.86 0.85 0.85 25000