com.johnsnowlabs.nlp.embeddings
CamemBertEmbeddings
Related Docs:
object CamemBertEmbeddings
| package embeddings
com.johnsnowlabs.nlp.embeddings
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
Annotations in batches that correspond to inputAnnotationCols generated by previous annotators if any
any number of annotations processed for every batch of input annotations. Not necessary one to one relationship
Size of every batch (Default depends on model).
Size of every batch (Default depends on model).
Whether to ignore case in index lookups (Default depends on model)
Whether to ignore case in index lookups (Default depends on model)
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with
config_proto.SerializeToString()
requirement for annotators copies
requirement for annotators copies
Number of embedding dimensions (Default depends on model)
Number of embedding dimensions (Default depends on model)
Override for additional custom schema checks
Override for additional custom schema checks
Size of every batch.
Size of every batch.
input annotations columns currently used
Gets annotation column name going to generate
Gets annotation column name going to generate
Annotator reference id.
Annotator reference id. Used to identify elements in metadata or to refer to this annotator type
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
Max sentence length to process (Default: 128)
Size of every batch.
Size of every batch.
Whether to lowercase tokens or not
Whether to lowercase tokens or not
Set Embeddings dimensions for the CamemBERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from CamemBERT config file
Set Embeddings dimensions for the CamemBERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from CamemBERT config file
Overrides required annotators column if different than default
Overrides required annotators column if different than default
Overrides annotation column name when transforming
Overrides annotation column name when transforming
It contains TF model signatures for the laded saved model
Unique identifier for storage (Default: this.uid)
Unique identifier for storage (Default: this.uid)
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Dataset[Row]
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
required uid for storing annotator to disk
required uid for storing annotator to disk
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
to be validated
True if all the required types are present, else false
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook’s RoBERTa model released in 2019. It is a model trained on 138GB of French text.
Pretrained models can be loaded with
pretrainedof the companion object:The default model is
"camembert_base", if no name is provided.For available pretrained models please see the Models Hub.
For extended examples of usage, see the Spark NLP Workshop and the CamemBertEmbeddingsTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.
Sources :
CamemBERT: a Tasty French Language Model
https://huggingface.co/camembert
Paper abstract
Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models --in all languages except English-- very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks.
Example
Annotators Main Page for a list of transformer based embeddings