Description
“GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where the model is primed with an input and it generates a lengthy continuation.
Predicted Entities
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("documents")
gpt2 = GPT2Transformer.pretrained("gpt2") \
.setInputCols(["documents"]) \
.setMaxOutputLength(50) \
.setOutputCol("generation")
pipeline = Pipeline().setStages([documentAssembler, gpt2])
data = spark.createDataFrame([["My name is Leonardo."]]).toDF("text")
result = pipeline.fit(data).transform(data)
result.select("summaries.generation").show(truncate=False)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("documents")
val gpt2 = GPT2Transformer.pretrained("gpt2")
.setInputCols(Array("documents"))
.setMinOutputLength(10)
.setMaxOutputLength(50)
.setDoSample(false)
.setTopK(50)
.setNoRepeatNgramSize(3)
.setOutputCol("generation")
val pipeline = new Pipeline().setStages(Array(documentAssembler, gpt2))
val data = Seq("My name is Leonardo.").toDF("text")
val result = pipeline.fit(data).transform(data)
results.select("generation.result").show(truncate = false)
Model Information
| Model Name: | gpt2 |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [documents] |
| Output Labels: | [generation] |
| Language: | en |
| Size: | 467.4 MB |
References
https://huggingface.co/openai-community/gpt2