Description
Pretrained T5ForConditionalGeneration model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. it5-efficient-small-el32-news-summarization
is a Italian model originally trained by it5
.
How to use
documentAssembler = DocumentAssembler() \
.setInputCols("text") \
.setOutputCols("document")
t5 = T5Transformer.pretrained("t5_it5_efficient_small_el32_news_summarization","it") \
.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_it5_efficient_small_el32_news_summarization","it")
.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
Model Name: | t5_it5_efficient_small_el32_news_summarization |
Compatibility: | Spark NLP 4.3.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [documents] |
Output Labels: | [t5] |
Language: | it |
Size: | 594.0 MB |
References
- https://huggingface.co/it5/it5-efficient-small-el32-news-summarization
- https://github.com/stefan-it
- https://arxiv.org/abs/2203.03759
- https://gsarti.com
- https://malvinanissim.github.io
- https://arxiv.org/abs/2109.10686
- https://github.com/gsarti/it5
- https://paperswithcode.com/sota?task=News+Summarization&dataset=NewsSum-IT