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‎README.md‎

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@@ -39,16 +39,16 @@ Please refer to [Installation](https://ontolearner.readthedocs.io/installation.h
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## 🔗 Essential Resources
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| Resource | Info |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| **[📚 OntoLearner Documentation](https://ontolearner.readthedocs.io/)** | OntoLearner's extensive documentation website. |
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| **[🤗 Datasets on Hugging Face](https://huggingface.co/collections/SciKnowOrg/ontolearner-benchmarking-6823bcd051300c210b7ef68a)** | Access curated, machine-readable ontologies. |
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| **Quick Tour on OntoLearner** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DuElAyEFzd1vtqTjDEXWcc0zCbiV2Yee?usp=sharing) ``version=1.2.1`` | OntoLearner hands-on Colab tutorials. |
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| **[🚀 Quickstart](https://ontolearner.readthedocs.io/quickstart.html)** | Get started quickly with OntoLearner’s main features and workflow. |
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| **[🕸️ Learning Tasks](https://ontolearner.readthedocs.io/learning_tasks/learning_tasks.html)** | Explore supported ontology learning tasks like LLMs4OL Paradigm tasks and Text2Onto. | |
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| **[🧠 Learner Models](https://ontolearner.readthedocs.io/learners/llm.html)** | Browse and configure various learner models, including LLMs, Retrieval, or RAG approaches. |
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| **[📚 Ontologies Documentations](https://ontolearner.readthedocs.io/benchmarking/benchmark.html)** | Review benchmark ontologies and datasets used for evaluation and training. |
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| **[🧩 How to work with Ontologizer?](https://ontolearner.readthedocs.io/ontologizer/ontology_modularization.html)** | Learn how to modularize and preprocess ontologies using the Ontologizer module. |
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| Resource | Info |
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|:-----------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------|
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| **[📚 OntoLearner Documentation](https://ontolearner.readthedocs.io/)** | OntoLearner's extensive documentation website. |
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| **[🤗 Datasets on Hugging Face](https://huggingface.co/collections/SciKnowOrg/ontolearner-benchmarking-6823bcd051300c210b7ef68a)** | Access curated, machine-readable ontologies. |
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| **[🚀 Quickstart](https://ontolearner.readthedocs.io/quickstart.html)** | Get started quickly with OntoLearner’s main features and workflow. |
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| **[🕸️ Learning Tasks](https://ontolearner.readthedocs.io/learning_tasks/learning_tasks.html)** | Explore supported ontology learning tasks like LLMs4OL Paradigm tasks and Text2Onto. | |
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| **[🧠 Learner Models](https://ontolearner.readthedocs.io/learners/llm.html)** | Browse and configure various learner models, including LLMs, Retrieval, or RAG approaches. |
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| **[📚 Ontologies Documentations](https://ontolearner.readthedocs.io/benchmarking/benchmark.html)** | Review benchmark ontologies and datasets used for evaluation and training. |
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| **[🧩 How to work with Ontologizer?](https://ontolearner.readthedocs.io/ontologizer/ontology_modularization.html)** | Learn how to modularize and preprocess ontologies using the Ontologizer module. |
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| **[🤗 Ontology Metrics Dashboard](https://huggingface.co/spaces/SciKnowOrg/OntoLearner-Benchmark-Metrics)** | Benchmark ontologies with their metrics and complexity scores. |
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## 🚀 Quick Tour
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Get started with OntoLearner in just a few lines of code. This guide demonstrates how to initialize ontologies, load datasets, and train an LLM-assisted learner for ontology engineering tasks.
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ret_learner = AutoRetrieverLearner(top_k=5)
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ret_learner.load(model_id='sentence-transformers/all-MiniLM-L6-v2')
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# 5. Fit the model to training data and do the predict
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# 5. Fit the model to training data and then predict over the test data
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ret_learner.fit(train_data, task=task)
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predicts = ret_learner.predict(test_data, task=task)
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