Description
Classify IMDB reviews in negative and positive categories.
Predicted Entities
neg, pos
How to use
...
embeddings = WordEmbeddingsModel().pretrained("glove_100d)\
.setInputCols(['document','tokens'])\
.setOutputCol('word_embeddings')
sentence_embeddings = SentenceEmbeddings() \
.setInputCols(["document", "word_embeddings"]) \
.setOutputCol("sentence_embeddings") \
.setPoolingStrategy("AVERAGE")
classifier = SentimentDLModel().pretrained('sentimentdl_glove_imdb')\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("sentiment")
nlp_pipeline = Pipeline(stages=[document_assembler, sentencer, tokenizer, embeddings, sentence_embeddings, 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!')
...
val embeddings = WordEmbeddingsModel().pretrained("glove_100d)
.setInputCols(Array('document','tokens'))
.setOutputCol('word_embeddings')
val sentence_embeddings = SentenceEmbeddings()
.setInputCols(Array("document", "word_embeddings"))
.setOutputCol("sentence_embeddings")
.setPoolingStrategy("AVERAGE")
val classifier = SentimentDLModel().pretrained('sentimentdl_glove_imdb')
.setInputCols(Array("sentence_embeddings"))
.setOutputCol("sentiment")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentencer, tokenizer, embeddings, sentence_embeddings, classifier))
val data = Seq("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!").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.sentiment.imdb.glove").predict(""")
nlp_pipeline = Pipeline(stages=[document_assembler, sentencer, tokenizer, embeddings, sentence_embeddings, classifier])
l_model = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF(""")
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_glove_imdb |
| Compatibility: | Spark NLP 2.7.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [sentiment] |
| Language: | en |
| Dependencies: | glove_840B_300 |
Data Source
https://ai.stanford.edu/~amaas/data/sentiment/
Benchmarking
precision recall f1-score support
neg 0.85 0.85 0.85 12500
pos 0.87 0.83 0.85 12500
accuracy 0.84 25000
macro avg 0.86 0.84 0.85 25000
weighted avg 0.86 0.84 0.85 25000