minor update to readme

README.md

@@ -6,7 +6,7 @@ language:
 tags:
 - OntoLearner
 - ontology-learning
-- materials_science_and_engineering
+- materials-science-and-engineering
 pretty_name: Materials Science And Engineering
 ---
 <div align="center">
@@ -103,32 +103,47 @@ ontology.load()
 data = ontology.extract()
 ```
 
+
 **How use the loaded dataset for LLM4OL Paradigm task settings?**
 ``` python
+# Import core modules from the OntoLearner library
 from ontolearner import AMOntology, LearnerPipeline, train_test_split
 
+# Load the AMOntology ontology, which contains concepts related to wines, their properties, and categories
 ontology = AMOntology()
-ontology.load() 
+ontology.load()  # Load entities, types, and structured term annotations from the ontology
 data = ontology.extract()
 
 # Split into train and test sets
-train_data, test_data = train_test_split(data, test_size=0.2)
+train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
 
-# Create a learning pipeline (for RAG-based learning)
+# Initialize a multi-component learning pipeline (retriever + LLM)
+# This configuration enables a Retrieval-Augmented Generation (RAG) setup
 pipeline = LearnerPipeline(
-    task = "term-typing",  # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
-    retriever_id = "sentence-transformers/all-MiniLM-L6-v2", 
-    llm_id = "mistralai/Mistral-7B-Instruct-v0.1",
-    hf_token = "your_huggingface_token"  # Only needed for gated models
+    retriever_id='sentence-transformers/all-MiniLM-L6-v2',      # Dense retriever model for nearest neighbor search
+    llm_id='Qwen/Qwen2.5-0.5B-Instruct',                        # Lightweight instruction-tuned LLM for reasoning
+    hf_token='...',                                             # Hugging Face token for accessing gated models
+    batch_size=32,                                              # Batch size for training/prediction if supported
+    top_k=5                                                     # Number of top retrievals to include in RAG prompting
 )
 
-# Train and evaluate
-results, metrics = pipeline.fit_predict_evaluate(
+# Run the pipeline: training, prediction, and evaluation in one call
+outputs = pipeline(
     train_data=train_data,
     test_data=test_data,
-    top_k=3,
-    test_limit=10
+    evaluate=True,              # Compute metrics like precision, recall, and F1
+    task='term-typing'          # Specifies the task
+                                # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
 )
+
+# Print final evaluation metrics
+print("Metrics:", outputs['metrics'])
+
+# Print the total time taken for the full pipeline execution
+print("Elapsed time:", outputs['elapsed_time'])
+
+# Print all outputs (including predictions)
+print(outputs)
 ```
 
 For more detailed documentation, see the [![Documentation](https://img.shields.io/badge/Documentation-ontolearner.readthedocs.io-blue)](https://ontolearner.readthedocs.io)