Description
Pretrained XLMRoBERTa Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. cino-large
is a Multilingual model orginally trained by hfl
.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = XlmRoBertaEmbeddings.pretrained("xlmroberta_embeddings_cino_large", "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_cino_large", "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.large.by_hfl").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | xlmroberta_embeddings_cino_large |
Compatibility: | Spark NLP 3.4.4+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [sentence, token] |
Output Labels: | [embeddings] |
Language: | xx |
Size: | 2.2 GB |
Case sensitive: | true |
References
- https://huggingface.co/hfl/cino-large
- https://github.com/ymcui/Chinese-Minority-PLM
- https://github.com/ymcui/MacBERT
- https://github.com/ymcui/Chinese-BERT-wwm
- https://github.com/ymcui/Chinese-ELECTRA
- https://github.com/ymcui/Chinese-XLNet
- https://github.com/airaria/TextBrewer
- https://github.com/ymcui/HFL-Anthology