Description
Pretrained XLMRoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. fairlex-fscs-minilm is a Multilingual model orginally trained by coastalcph.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_fscs_minilm","xx") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).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 = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_fairlex_fscs_minilm","xx")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("PUT YOUR STRING HERE").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("xx.embed.xlmr_roberta.mini_lm_mini").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | xlmroberta_embeddings_fairlex_fscs_minilm |
| Compatibility: | Spark NLP 5.0.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [embeddings] |
| Language: | xx |
| Size: | 402.9 MB |
| Case sensitive: | true |