Description
Pretrained T5ForConditionalGeneration model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. logisgenerator
is a English model originally trained by OnsElleuch
.
How to use
documentAssembler = DocumentAssembler() \
.setInputCols("text") \
.setOutputCols("document")
t5 = T5Transformer.pretrained("t5_logisgenerator","en") \
.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_logisgenerator","en")
.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_logisgenerator |
Compatibility: | Spark NLP 4.3.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [documents] |
Output Labels: | [t5] |
Language: | en |
Size: | 280.2 MB |
References
- https://huggingface.co/OnsElleuch/logisgenerator
- https://pypi.org/project/keytotext/
- https://pepy.tech/project/keytotext
- https://colab.research.google.com/github/gagan3012/keytotext/blob/master/notebooks/K2T.ipynb
- https://share.streamlit.io/gagan3012/keytotext/UI/app.py
- https://github.com/gagan3012/keytotext#api
- https://hub.docker.com/r/gagan30/keytotext
- https://keytotext.readthedocs.io/en/latest/?badge=latest
- https://github.com/psf/black
- https://socialify.git.ci/gagan3012/keytotext/image?description=1&forks=1&language=1&owner=1&stargazers=1&theme=Light