sparknlp.annotator.embeddings.xlm_roberta_embeddings#

Classes#

class XlmRoBertaEmbeddings(classname='com.johnsnowlabs.nlp.embeddings.XlmRoBertaEmbeddings', java_model=None)[source]#

The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzman, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.

It is based on Facebook’s RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.

Pretrained models can be loaded with pretrained() of the companion object:

>>> embeddings = XlmRoBertaEmbeddings.pretrained() \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("embeddings")

The default model is "xlm_roberta_base", default language is "xx" (meaning multi-lingual), if no values are provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Examples. To see which models are compatible and how to import them see Import Transformers into Spark NLP 🚀.

Input Annotation types	Output Annotation type
DOCUMENT, TOKEN	WORD_EMBEDDINGS

Parameters:

batchSize

Size of every batch, by default 8

dimension

Number of embedding dimensions, by default 768

caseSensitive

Whether to ignore case in tokens for embeddings matching, by default True

maxSentenceLength

Max sentence length to process, by default 128

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

Notes

• XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does not require lang parameter to understand which language is used, and should be able to determine the correct language from the input ids.

• This implementation is the same as RoBERTa. Refer to RoBertaEmbeddings for usage examples as well as the information relative to the inputs and outputs.

References

Unsupervised Cross-lingual Representation Learning at Scale

Paper Abstract:

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> embeddings = XlmRoBertaEmbeddings.pretrained() \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("embeddings") \
...     .setCaseSensitive(True)
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True) \
...     .setCleanAnnotations(False)
>>> pipeline = Pipeline() \
...     .setStages([
...       documentAssembler,
...       tokenizer,
...       embeddings,
...       embeddingsFinisher
...     ])
>>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[-0.05969233065843582,-0.030789051204919815,0.04443822056055069,0.09564960747...|
|[-0.038839809596538544,0.011712731793522835,0.019954433664679527,0.0667808502...|
|[-0.03952755779027939,-0.03455188870429993,0.019103847444057465,0.04311436787...|
|[-0.09579929709434509,0.02494969218969345,-0.014753809198737144,0.10259044915...|
|[0.004710011184215546,-0.022148698568344116,0.011723337695002556,-0.013356896...|
+--------------------------------------------------------------------------------+

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]

ConfigProto from tensorflow, serialized into byte array

static loadSavedModel(folder, spark_session, use_openvino=False)[source]#

Loads a locally saved model.

Parameters:

folderstr

Folder of the saved model

spark_sessionpyspark.sql.SparkSession

The current SparkSession

use_openvino: bool

Use OpenVINO backend

Returns:

XlmRoBertaEmbeddings

The restored model

static pretrained(name='xlm_roberta_base', lang='xx', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “xlm_roberta_base”

langstr, optional

Language of the pretrained model, by default “xx”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

XlmRoBertaEmbeddings

The restored model