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Update llama_cpp_in_Spark_NLP_LLMEntityExtractor.ipynb #14756

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danilojsl merged 2 commits into from
May 8, 2026
+131 −98
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Update llama_cpp_in_Spark_NLP_LLMEntityExtractor.ipynb #14756

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b5ec66d
Update llama_cpp_in_Spark_NLP_LLMEntityExtractor.ipynb
AbdullahMubeenAnwar Apr 14, 2026
b6b181d
Update LLMEntityExtractor.md
AbdullahMubeenAnwar Apr 14, 2026
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10 changes: 6 additions & 4 deletions docs/en/annotator_entries/LLMEntityExtractor.md
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ according to your hardware to avoid out-of-memory errors.
{:.table-model-big}
| Parameter | Description | Default |
|---|---|---|
| `modelName` | Name of the AutoGGUF model to load for entity extraction | `"qwen3_4b_bf16_gguf"` |
| `pretrained` | Name of the AutoGGUF model to load for entity extraction | `"qwen3_4b_bf16_gguf"` |
| `entityTypes` | List of entity types to extract (used in prompt) | `["PERSON", "ORGANIZATION", "LOCATION", "DATE", "TIME"]` |
| `promptTemplate` | Custom prompt template for entity extraction. Use `{entityTypes}` and `{examples}` placeholders | Built-in default prompt |
| `fewShotExamples` | Few-shot examples as array of `(input_text, json_output)` tuples to guide the model | Empty array |
Expand Down Expand Up @@ -86,9 +86,9 @@ medical_examples = [
]

entityExtractor = LLMEntityExtractor() \
.pretrained("qwen3_4b_bf16_gguf") \
.setInputCols(["document"]) \
.setOutputCol("entities") \
.setModelName("qwen3_4b_bf16_gguf") \
.setEntityTypes(["MEDICATION", "DOSAGE", "ROUTE", "FREQUENCY", "PERSON", "ORGANIZATION"]) \
.setFewShotExamples(medical_examples) \
.setNPredict(500) \
Expand All @@ -114,6 +114,7 @@ result.select(
F.col("entity.metadata.entity").alias("entity_type"),
F.col("entity.metadata.chunk").alias("chunk_index")
).show(truncate=False)

+-------------------+-----+---+------------+-----------+
|text |begin|end|entity_type |chunk_index|
+-------------------+-----+---+------------+-----------+
Expand Down Expand Up @@ -152,9 +153,9 @@ val medicalExamples = Array(
)

val entityExtractor = new LLMEntityExtractor()
.pretrained("qwen3_4b_bf16_gguf")
.setInputCols("document")
.setOutputCol("entities")
.setModelName("qwen3_4b_bf16_gguf")
.setEntityTypes(Array("MEDICATION", "DOSAGE", "ROUTE", "FREQUENCY", "PERSON", "ORGANIZATION"))
.setFewShotExamples(medicalExamples)
.setNPredict(500)
Expand All @@ -180,6 +181,7 @@ result.select(
F.col("entity.metadata.entity").alias("entity_type"),
F.col("entity.metadata.chunk").alias("chunk_index")
).show(truncate=False)

+-------------------+-----+---+------------+-----------+
|text |begin|end|entity_type |chunk_index|
+-------------------+-----+---+------------+-----------+
Expand Down Expand Up @@ -220,4 +222,4 @@ scala_example=scala_example
api_link=api_link
python_api_link=python_api_link
source_link=source_link
%}
%}
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