Multilingual T5ForConditionalGeneration Small Cased model (from google)

Description

Pretrained T5ForConditionalGeneration model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. flan-t5-small is a Multilingual model originally trained by google.

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
    .setInputCols("text") \
    .setOutputCols("document")

t5 = T5Transformer.pretrained("t5_flan_small","xx") \
    .setInputCols("document") \
    .setOutputCol("answers")
    
pipeline = Pipeline(stages=[documentAssembler, t5])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler() 
      .setInputCols("text")
      .setOutputCols("document")
       
val t5 = T5Transformer.pretrained("t5_flan_small","xx") 
    .setInputCols("document")
    .setOutputCol("answers")
   
val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Model Information

References

• https://huggingface.co/google/flan-t5-small
• https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png
• https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints
• https://arxiv.org/pdf/2210.11416.pdf
• https://github.com/google-research/t5x
• https://arxiv.org/pdf/2210.11416.pdf
• https://arxiv.org/pdf/2210.11416.pdf
• https://arxiv.org/pdf/2210.11416.pdf
• https://s3.amazonaws.com/moonup/production/uploads/1666363265279-62441d1d9fdefb55a0b7d12c.png
• https://arxiv.org/pdf/2210.11416.pdf
• https://github.com/google-research/t5x
• https://github.com/google/jax
• https://s3.amazonaws.com/moonup/production/uploads/1668072995230-62441d1d9fdefb55a0b7d12c.png
• https://arxiv.org/pdf/2210.11416.pdf
• https://arxiv.org/pdf/2210.11416.pdf
• https://mlco2.github.io/impact#compute
• https://arxiv.org/abs/1910.09700