Instructions to use Clementio/PLRS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Clementio/PLRS with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Clementio/PLRS", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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PLRS — Personalized Learning Recommendation System
====================================================
Constraint-aware personalized learning recommendations.
Plug in your curriculum DAG, get intelligent recommendations out.
Quick start:
from plrs import PLRSPipeline
from plrs.curriculum import load_dag
graph = load_dag("my_curriculum.json")
pipeline = PLRSPipeline(graph)
results = pipeline.recommend(student_history)
"""
from plrs.pipeline import PLRSPipeline
from plrs.model.sakt import SAKTModel
from plrs.constraints.dag import DAGConstraintLayer
from plrs.ranking.ranker import MultiObjectiveRanker
from plrs.curriculum.loader import load_dag, CurriculumGraph
__version__ = "0.1.0"
__all__ = [
"PLRSPipeline",
"SAKTModel",
"DAGConstraintLayer",
"MultiObjectiveRanker",
"load_dag",
"CurriculumGraph",
]
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