Description
This model is a legal version of the E5 base model fine-tuned on Edgar and legal question-answering datasets. Reference: Wang, Liang, et al. “Text embeddings by weakly-supervised contrastive pre-training.” arXiv preprint arXiv:2212.03533 (2022).
Predicted Entities
How to use
document_assembler = (
nlp.DocumentAssembler().setInputCol("text").setOutputCol("document")
)
E5_embedding = (
nlp.E5Embeddings.pretrained(
"legembedding_e5_base", "en", "legal/models"
)
.setInputCols(["document"])
.setOutputCol("E5")
)
pipeline = nlp.Pipeline(stages=[document_assembler, E5_embedding])
data = spark.createDataFrame([[' What is the rate of shipment for crude oil from the Lincoln Parish Plant to the Mount Olive Plant and from the Mount Olive Plant to the DCP Black Lake in Ada, LA?']]).toDF("text")
result = pipeline.fit(data).transform(data)
result. Select("E5.result").show()
Results
+----------------------------------------------------------------------------------------------------+
| embeddings|
+----------------------------------------------------------------------------------------------------+
|[-1.0422493, 0.008562431, -0.31533027, -0.39874774, 0.27517456, 0.6205345, -0.34923095, 0.2872358...|
+----------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | legembedding_e5_base |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [E5] |
| Language: | en |
| Size: | 393.9 MB |
References
We used in-house annotated data.