SparkNLP 996 - Introducing Phi-2 #14178
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* SPARKNLP-942: MPNetForSequenceClassification * SPARKNLP-942: MPNetForQuestionAnswering * SPARKNLP-942: MPNet Classifiers Documentation * Restore RobertaforQA bugfix
* introducing LLAMA2 * Added option to read model from model path to onnx wrapper * Added option to read model from model path to onnx wrapper * updated text description * LLAMA2 python API * added method to save onnx_data * added position ids * - updated Generate.scala to accept onnx tensors - added beam search support for LLAMA2 * updated max input length * updated python default params changed test to slow test * fixed serialization bug
* Added retrieval interface to the doc sim rank approach * Added Python interface as retriever in doc sim ranker --------- Co-authored-by: Stefano Lori <s.lori@izicap.com>
* adding code * adding notebook for import --------- Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
…4155) * introducing LLAMA2 * Added option to read model from model path to onnx wrapper * Added option to read model from model path to onnx wrapper * updated text description * LLAMA2 python API * added method to save onnx_data * added position ids * - updated Generate.scala to accept onnx tensors - added beam search support for LLAMA2 * updated max input length * updated python default params changed test to slow test * fixed serialization bug * Added Scala code for M2M100 * Documentation for scala code * Python API for M2M100 * added more tests for scala * added tests for python * added pretrained * rewording * fixed serialization bug * fixed serialization bug --------- Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
Some annotators might have different naming schemes for their files. Added a parameter to control this.
…bs/spark-nlp into release/530-release-candidate
#14167) * [SPARKNLP-940] Adding changes to correctly copy cluster index storage when defined * [SPARKNLP-940] Moving local mode control to its right place * [SPARKNLP-940] Refactoring sentToCLuster method
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Description
Introducing Phi-2
Phi-2 is a Transformer with 2.7 billion parameters. It was trained using the same data sources as Phi-1.5, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters.
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