Description
Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. hateBERT
is a English model orginally trained by GroNLP
.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val embeddings = BertEmbeddings.pretrained("bert_embeddings_hateBERT","en")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("I love Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.embed.hateBERT").predict("""I love Spark NLP""")
Model Information
Model Name: | bert_embeddings_hateBERT |
Compatibility: | Spark NLP 3.4.2+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [sentence, token] |
Output Labels: | [bert] |
Language: | en |
Size: | 409.0 MB |
Case sensitive: | true |
References
- https://huggingface.co/GroNLP/hateBERT
- https://www.semanticscholar.org/author/Tommaso-Caselli/1864635
- https://www.semanticscholar.org/author/Valerio-Basile/3101511
- https://www.semanticscholar.org/author/Jelena-Mitrovic/145157863
- https://www.semanticscholar.org/author/M.-Granitzer/2389675
- https://aclanthology.org/2021.woah-1.3/
- https://osf.io/tbd58/?view_onlycb79b3228d4248ddb875eb1803525ad8