Multilingual T5ForConditionalGeneration Cased model (from qiaoyi)

Description

Pretrained T5ForConditionalGeneration model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. Comment_Summarization4DesignTutor is a Multilingual model originally trained by qiaoyi.

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How to use

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

t5 = T5Transformer.pretrained("t5_comment_summarization4designtutor","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_comment_summarization4designtutor","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/qiaoyi/Comment_Summarization4DesignTutor
• https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html
• https://arxiv.org/abs/1805.12471
• https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf
• https://aclanthology.org/I05-5002
• https://arxiv.org/abs/1708.00055
• https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs
• https://arxiv.org/abs/1704.05426
• https://arxiv.org/abs/1606.05250
• https://link.springer.com/chapter/10.1007/11736790_9
• https://semanticsarchive.net/Archive/Tg3ZGI2M/Marneffe.pdf
• https://www.researchgate.net/publication/221251392_Choice_of_Plausible_Alternatives_An_Evaluation_of_Commonsense_Causal_Reasoning
• https://arxiv.org/abs/1808.09121
• https://aclanthology.org/N18-1023
• https://arxiv.org/abs/1810.12885
• https://arxiv.org/abs/1905.10044
• https://arxiv.org/pdf/1910.10683.pdf
• https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67