Description
Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. fralbert-base is a French model orginally trained by qwant.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = AlbertEmbeddings.pretrained("albert_embeddings_fralbert_base","fr") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["J'adore 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 = AlbertEmbeddings.pretrained("albert_embeddings_fralbert_base","fr")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("J'adore Spark Nlp").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("fr.embed.albert").predict("""J'adore Spark Nlp""")
Model Information
| Model Name: | albert_embeddings_fralbert_base |
| Compatibility: | Spark NLP 3.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [bert] |
| Language: | fr |
| Size: | 45.9 MB |
| Case sensitive: | false |
References
- https://huggingface.co/qwant/fralbert-base
- https://arxiv.org/abs/1909.11942
- https://github.com/google-research/albert
- https://fr.wikipedia.org/wiki/French_Wikipedia
- https://hal.archives-ouvertes.fr/hal-03336060