Description
Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. robbert-v2-dutch-base is a Dutch model orginally trained by pdelobelle.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_robbert_v2_dutch_base","nl") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["Ik hou van vonk 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 = RoBertaEmbeddings.pretrained("roberta_embeddings_robbert_v2_dutch_base","nl")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("Ik hou van vonk nlp").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("nl.embed.robbert_v2_dutch_base").predict("""Ik hou van vonk nlp""")
Model Information
| Model Name: | roberta_embeddings_robbert_v2_dutch_base |
| Compatibility: | Spark NLP 3.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | nl |
| Size: | 438.6 MB |
| Case sensitive: | true |
References
- https://huggingface.co/pdelobelle/robbert-v2-dutch-base
- https://github.com/iPieter/RobBERT
- https://scholar.google.com/scholar?oi=bibs&hl=en&cites=7180110604335112086
- https://www.aclweb.org/anthology/2021.wassa-1.27/
- https://arxiv.org/pdf/2001.06286.pdf
- https://biblio.ugent.be/publication/8704637/file/8704638.pdf
- https://arxiv.org/pdf/2001.06286.pdf
- https://arxiv.org/pdf/2001.06286.pdf
- https://arxiv.org/pdf/2004.02814.pdf
- https://github.com/proycon/deepfrog
- https://arxiv.org/pdf/2001.06286.pdf
- https://github.com/proycon/deepfrog
- https://arxiv.org/pdf/2001.06286.pdf
- https://arxiv.org/pdf/2010.13652.pdf
- https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/automatic-classification-of-participant-roles-in-cyberbullying-can-we-detect-victims-bullies-and-bystanders-in-social-media-text/A2079C2C738C29428E666810B8903342
- https://gitlab.com/spelfouten/dutch-simpletransformers/
- https://arxiv.org/pdf/2101.05716.pdf
- https://medium.com/broadhorizon-cmotions/nlp-with-r-part-5-state-of-the-art-in-nlp-transformers-bert-3449e3cd7494
- https://people.cs.kuleuven.be/~pieter.delobelle/robbert/
- https://arxiv.org/abs/2001.06286
- https://github.com/iPieter/RobBERT
- https://arxiv.org/abs/1907.11692
- https://github.com/pytorch/fairseq/tree/master/examples/roberta
- https://people.cs.kuleuven.be/~pieter.delobelle/robbert/
- https://arxiv.org/abs/2001.06286
- https://github.com/iPieter/RobBERT
- https://github.com/benjaminvdb/110kDBRD
- https://www.statmt.org/europarl/
- https://arxiv.org/abs/2001.02943
- https://universaldependencies.org/treebanks/nl_lassysmall/index.html
- https://www.clips.uantwerpen.be/conll2002/ner/
- https://oscar-corpus.com/
- https://github.com/pytorch/fairseq/tree/master/examples/roberta
- https://github.com/pytorch/fairseq/tree/master/examples/roberta
- https://arxiv.org/abs/2001.06286
- https://github.com/iPieter/RobBERT#how-to-replicate-our-paper-experiments
- https://arxiv.org/abs/1909.11942
- https://camembert-model.fr/
- https://en.wikipedia.org/wiki/Robbert
- https://muppet.fandom.com/wiki/Bert
- https://github.com/iPieter/RobBERT/blob/master/res/robbert_logo.png
- https://people.cs.kuleuven.be/~pieter.delobelle
- https://thomaswinters.be
- https://people.cs.kuleuven.be/~bettina.berendt/