Description
This pretrained pipeline is built on the top of flan_t5_base_jsl_qa model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
qa_pipeline = PretrainedPipeline("flan_t5_base_jsl_qa_pipeline", "en", "clinical/models")
context = """The visual indexing theory proposed by Zenon Pylyshyn (Cognition, 32, 65-97, 1989) predicts that visual attention mechanisms are employed when mental images are projected onto a visual scene."""
question = """What is the effect of directing attention on memory?"""
result = qa_pipeline.fullAnnotate([question], [context])
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val qa_pipeline = PretrainedPipeline("flan_t5_base_jsl_qa_pipeline", "en", "clinical/models")
val context = """The visual indexing theory proposed by Zenon Pylyshyn (Cognition, 32, 65-97, 1989) predicts that visual attention mechanisms are employed when mental images are projected onto a visual scene."""
val question = """What is the effect of directing attention on memory?"""
val result = qa_pipeline.fullAnnotate([question], [context])
Results
The effect of directing attention on memory is that it can help to improve memory retention and recall. It can help to reduce the amount of time spent on tasks, such as focusing on one task at a time, or focusing on
Model Information
| Model Name: | flan_t5_base_jsl_qa_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.2.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 928.7 MB |
Included Models
- MultiDocumentAssembler
- MedicalQuestionAnswering